UPSI Digital Repository (UDRep)
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Abstract : Universiti Pendidikan Sultan Idris |
This research aimed to improve the fault tolerance of healthcare services provided for
Chronic Heart Disease (CHD) patients living in remote areas. A new fault-tolerant
mHealth framework was proposed to solve existing problems in healthcare services due
to frequent failures in the telemedicine architecture. This study used an experimental
research design that was carried out based on two stages. In the first stage, the researcher
proposed a new algorithm known as Three-level Localization Triage (3LLT) to exclude
the triage process from a medical center (Tier 3) and to overcome alarm failures related
to Tier 1. In the second stage, the proposed framework was used to assist the decision
maker to make the appropriate hospital selection based on a Multi-Criteria Decision
Making technique, namely the Analytic Hierarchy Process (AHP). Two datasets were
used comprising a dataset of 572 CHD patients and a dataset of hospitals healthcare
services, which were used in the triage stage and in the hospital selection stage,
respectively, based on two scenarios. The first scenario involved real high-level services
of 12 hospitals located in Baghdad, Iraq, and the second scenario was based on low-level
simulated services of 12 hospitals located in Kuala Lumpur, Malaysia. The results
showed that the AHP technique was highly effective in solving the failures of healthcare
services and the problems related to hospital selection. Moreover, the results showed
significant differences in the groups‘ scores, indicating that the ranking results were
identical for the three groups. Clearly, such empirical results suggest that the ranking of
hospitals cannot be determined in a specific situation with many combined factors that
may have a significant impact on the priority setting at the hospital level. For the
validation of the framework, the results showed that the ranking results were perfectly
identical. The implication of this study is that medical organizations can use the proposed
fault-tolerant framework to assign patients to appropriate hospitals that can provide them
with prompt, effective healthcare services. |
References |
A.A Zaidan, B.B Zaidan, Al-Haiqi. A, Kiah M.L.M, Hussain.M, A. . (2014). Evaluation and selection of opensource EMR software packages. Elsevier, 53, N/A.
Acampora, G., Cook, D. J., Rashidi, P., & Vasilakos, A. V. (2013). A survey on ambient intelligence in healthcare. Proceedings of the IEEE, 101(12), 2470–2494. https://doi.org/10.1109/JPROC.2013.2262913
Adibi, S. (2015). A mobile health network disaster management system. In 2015 Seventh International Conference on Ubiquitous and Future Networks (pp. 424–428). IEEE. https://doi.org/10.1109/ICUFN.2015.7182579
Adunlin, G., Diaby, V., & Xiao, H. (2015). Application of multicriteria decision analysis in health care: a systematic review and bibliometric analysis. Health Expectations : An International Journal of Public Participation in Health Care and Health Policy, 18(6), 1894–905. https://doi.org/10.1111/hex.12287
Ahmadi-Javid, A., Seyedi, P., & Syam, S. S. (2017). A survey of healthcare facility location. Computers and Operations Research, 79, 223–263. https://doi.org/10.1016/j.cor.2016.05.018
Ahmadi, H., Nilashi, M., & Ibrahim, O. (2014). Organizational decision to adopt hospital information system: An empirical investigation in the case of Malaysian public hospitals. International Journal of Medical Informatics, 84(3), 166–188. https://doi.org/10.1016/j.ijmedinf.2014.12.004
Ahmed, A., Rebeiro-Hargrave, A., Nohara, Y., Kai, E., Hossein Ripon, Z., & Nakashima, N. (2014). Targeting morbidity in unreached communities using portable health clinic system. In IEICE Transactions on Communications (Vol. E97–B, pp. 540– 545). https://doi.org/10.1587/transcom.E97.B.540
Ahn, J., Heo, J., Lim, S., & Kim, W. (2008). A study on Ubiquitous Healthcare system based on LBS. In World Congress on Engineering 2008, Vols I-Ii (Vol. I, pp. 270– 273). IEEE.
Akdag, H., Kalayci, T., Karagoz, S., Zulfikar, H., Giz, D. (2014). The evaluation of hospital service quality by fuzzy MCDM, Applied Soft Computing. Applied Soft Computing, 23, 239–248.
Akdag, H., Kalayc?, T., Karagöz, S., Zülfikar, H., Giz, D., & Akdag, H., Kalayci, T., Karagoz, S., Zulfikar, H., Giz, D. (2014). The evaluation of hospital service quality by fuzzy MCDM, Applied Soft Computing. Applied Soft Computing, 23, 239–248.
Albahri, O. S., Zaidan, A. A., Zaidan, B. B., Hashim, M., Albahri, A. S., & Alsalem, M. A. (2018). Real-Time Remote Health-Monitoring Systems in a Medical Centre: A Review of the Provision of Healthcare Services-Based Body Sensor Information, Open Challenges and Methodological Aspects. Journal of Medical Systems, 42(9), 164. https://doi.org/10.1007/s10916-018-1006-6
Aldlaigan, A. H., & Buttle, F. A. (2002). SYSTRA?SQ: a new measure of bank service quality. International Journal of Service Industry Management, 13(4), 362–381. https://doi.org/10.1108/09564230210445041
Ali, R., Siddiqi, M. H., Idris, M., Ali, T., Hussain, S., Huh, E. N., … Lee, S. (2015).GUDM: Automatic generation of unified datasets for learning and reasoning in healthcare. Sensors (Switzerland), 15(7), 15772–15798. https://doi.org/10.3390/s150715772
Almadani, B., Saeed, B., & Alroubaiy, A. (2016). Healthcare systems integration using Real Time Publish Subscribe (RTPS) middleware. Computers and Electrical Engineering, 50(May), 67–78. https://doi.org/10.1016/j.compeleceng.2015.12.009
Alnanih, R., Ormandjieva, O., & Radhakrishnan, T. (2013). Context-based and rule- based adaptation of mobile user interfaces in mHealth. Procedia Computer Science, 21, 390–397. https://doi.org/10.1016/j.procs.2013.09.051
Ar, I. M., & Kurtaran, A. (2013). Evaluating the Relative Efficiency of Commercial Banks in Turkey: An Integrated AHP/DEA Approach. International Business Research, 6(4), 129. https://doi.org/10.5539/ibr.v6n4p129
Armbrust, M., Stoica, I., Zaharia, M., Fox, A., Griffith, R., Joseph, A. D., … Rabkin, A. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50. https://doi.org/10.1145/1721654.1721672
Aruldoss, M. (2013). A Survey on Multi Criteria Decision Making Methods and Its Applications. American Journal of Information Systems, 1(1), 31–43. https://doi.org/10.12691/ajis-1-1-5
Ashour, O. M., & Okudan, G. E. (2010). Fuzzy AHP and utility theory based patient sorting in emergency departments. International Journal of Collaborative Enterprise, 1(3/4), 332. https://doi.org/10.1504/IJCENT.2010.038357
Auerbach, P. S. (1991). Health and medical aspects of disaster preparedness. Jama (Vol. 265). Springer Science & Business Media. https://doi.org/10.1001/jama.1991.03460180121047
Auffray, C., Balling, R., Barroso, I., Bencze, L., Benson, M., Bergeron, J., … Zanetti, G. (2016). Making sense of big data in health research: Towards an EU action plan. Genome Medicine, 8(1), 1–13. https://doi.org/10.1186/s13073-016-0323-y
Australia, M. T. A. o. (2012). (n.d.). A telehealth strategy for Australia: supporting patients in the community. Retrieved from San Diego, USA: Australia, M. T. A. o. (2012). A Telehealth Strategy for Australia: Supporting Patients in the Community. Retrieved from Http://Mtaa.Org.Au/Docs/Position-Papers/Supporting-a-Telehealth- Strategyfor-Australia-Release-Version-M.
Azeez, D., Ali, M. A. M., Gan, K. B., & Saiboon, I. (2013). Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department. SpringerPlus, 2(1), 1–10. https://doi.org/10.1186/2193-1801-2-416
Azeredo, T. R. M., Guedes, H. M., Rebelo de Almeida, R. A., Chianca, T. C. M., & Martins, J. C. A. (2015). Efficacy of the manchester triage system: A systematic review. International Emergency Nursing, 23(2), 47–52. https://doi.org/10.1016/j.ienj.2014.06.001
Baehr, D., McKinney, S., Quirk, A., & Harfoush, K. (2014). On the practicality of elliptic curve cryptography for medical sensor networks. In 2014 11th Annual High Capacity Optical Networks and Emerging/Enabling Technologies (Photonics for Energy), HONET-PfE 2014 (pp. 41–45). IEEE. https://doi.org/10.1109/HONET.2014.7029358
Baig, M. M., & Gholamhosseini, H. (2013). Smart health monitoring systems: An overview of design and modeling. Journal of Medical Systems, 37(2). https://doi.org/10.1007/s10916-012-9898-z
Baltussen, R., & Niessen, L. (2006). Priority setting of health interventions: The need for multi-criteria decision analysis. Cost Effectiveness and Resource Allocation, 4(1), 14. https://doi.org/10.1186/1478-7547-4-14
Barbera, Joseph A., A. G. M. (2004). Medical Surge Capacity and Capability : A Management System for Integrating Medical and Health Resources During Large- Scale Emergencies. Department of Health and Human Services.The CNA Corporation. Washington, DC: US Department of Health and Human Services.
Barrios, M. A. O., De Felice, F., Negrete, K. P., Romero, B. A., Arenas, A. Y., & Petrillo, A. (2016). An AHP-Topsis Integrated Model for Selecting the Most Appropriate Tomography Equipment. International Journal of Information Technology & Decision Making, 15(04), 861–885. https://doi.org/10.1142/S021962201640006X
Barsan, W. G., Brott, T. G., Broderick, J. P., Haley, E. C., Levy, D. E., & Marler, J. R. (1993). Time of Hospital Presentation in Patients With Acute Stroke. Archives of Internal Medicine, 153(22), 2558–2561. https://doi.org/10.1001/archinte.1993.00410220058006
Bashshur, R. L., Shannon, G. W., Smith, B. R., Alverson, D. C., Antoniotti, N., Barsan, W. G., … Yellowlees, P. (2014). The Empirical Foundations of Telemedicine Interventions for Chronic Disease Management. Telemedicine and E-Health, 20(9), 769–800. https://doi.org/10.1089/tmj.2014.9981
Beck, C., & Georgiou, J. (2016). A wearable, multimodal, vitals acquisition unit for intelligent field triage. Proceedings - IEEE International Symposium on Circuits and Systems, 2016–July(3), 1530–1533. https://doi.org/10.1109/ISCAS.2016.7538853
Beikkhakhian, Y., Javanmardi, M., Karbasian, M., & Khayambashi, B. (2015). The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods. Expert Systems with Applications, 42(15–16), 6224–6236. https://doi.org/10.1016/j.eswa.2015.02.035
Bellod Cisneros, J. L., & Lund, O. (2017). KmerFinderJS: A Client-Server Method For Fast Species Typing Of Bacteria Over Slow Internet Connections. Doi.Org, 145284. https://doi.org/10.1101/145284
Ben Elhadj, H., Elias, J., Chaari, L., & Kamoun, L. (2016). Multi-Attribute Decision Making Handover Algorithm for Wireless Body Area Networks. Computer Communications, 81, 97–108. https://doi.org/10.1016/j.comcom.2016.01.007
Ben Othman, S., Zgaya, H., Hammadi, S., Quilliot, A., Martinot, A., & Renard, J. M. (2016). Agents endowed with uncertainty management behaviors to solve a multiskill healthcare task scheduling. Journal of Biomedical Informatics, 64, 25–43. https://doi.org/10.1016/j.jbi.2016.08.011
Benmansour, T., Ahmed, T., & Moussaoui, S. (2016). Performance Evaluation of IEEE 802.15.6 MAC in Monitoring of a Cardiac Patient. In 2016 IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops) (pp. 241–247). IEEE. https://doi.org/10.1109/LCN.2016.054
Beratarrechea, A., Lee, A. G., Willner, J. M., Jahangir, E., Ciapponi, A., & Rubinstein, A. (2014). The Impact of Mobile Health Interventions on Chronic Disease Outcomes in Developing Countries: A Systematic Review. Telemedicine and E- Health, 20(1), 75–82. https://doi.org/10.1089/tmj.2012.0328
Berglas, N. F., Battistelli, M. F., Nicholson, W. K., Sobota, M., Urman, R. D., & Roberts, S. C. M. (2018). The effect of facility characteristics on patient safety, patient experience, and service availability for procedures in non-hospital-affiliated outpatient settings: A systematic review. PloS One, 13(1), e0190975.
Bernocchi, P., Scalvini, S., Tridico, C., Borghi, G., Zanaboni, P., Masella, C., … Marzegalli, M. (2012). Healthcare continuity from hospital to territory in Lombardy: TELEMACO project. American Journal of Managed Care, 18(3), 101–108.
Besaleva, L. I., & Weaver, A. C. (2013). Mobile Electronic Triaging for Emergency Response Information. 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 1092–1093. https://doi.org/10.1197/aemj.9.3.255.
Besaleva, L. I., & Weaver, A. C. (2014). CrowdHelp: M-Health application for emergency response improvement through crowdsourced and sensor-detected information. In 2014 Wireless Telecommunications Symposium (pp. 1–5). New York, New York, USA: IEEE. https://doi.org/10.1109/WTS.2014.6835005
Bharatula, S., & Meenakshi, M. (2016). Design of Cognitive Radio Network for Hospital Management System. Wireless Personal Communications, 90(2), 1021–1038. https://doi.org/10.1007/s11277-016-3280-2
Bicen, A. O., & Akan, O. B. (2011). Reliability and congestion control in cognitive radio sensor networks. Ad Hoc Networks, 9(7), 1154–1164. https://doi.org/10.1016/j.adhoc.2011.01.004
Boatin, A. A., Wylie, B. J., Goldfarb, I., Azevedo, R., Pittel, E., Ng, C., & Haberer, J. E. (2016). Wireless Vital Sign Monitoring in Pregnant Women: A Functionality and Acceptability Study. Telemedicine and E-Health, 22(7), 564–571. https://doi.org/10.1089/tmj.2015.0173
Bouakaz, S., Vacher, M., Bobillier Chaumon, M. E., Aman, F., Bekkadja, S., Portet, F., … Chevalier, T. (2014). CIRDO: Smart companion for helping elderly to live at home for longer. Irbm, 35(2), 100–108. https://doi.org/10.1016/j.irbm.2014.02.011
Boursalie, O., Samavi, R., & Doyle, T. E. (2015). M4CVD: Mobile machine learning model for monitoring cardiovascular disease. In Procedia Computer Science (Vol. 63, pp. 384–391). Elsevier Ireland Ltd. https://doi.org/10.1016/j.procs.2015.08.357
Bradai, N., Chaari Fourati, L., & Kamoun, L. (2015). WBAN data scheduling and aggregation under WBAN/WLAN healthcare network. Ad Hoc Networks, 25(PA), 251–262. https://doi.org/10.1016/j.adhoc.2014.10.017
Bradai, N., Charfi, E., Fourati, L. C., & Kamoun, L. (2016). Priority consideration in inter-WBAN data scheduling and aggregation for monitoring systems. Transactions on Emerging Telecommunications Technologies, 27(4), 589–600. https://doi.org/10.1002/ett.2995
Bresó, A., Martínez-miranda, J., Fuster-, E., & García-gómez, J. M. (2015). Author ‘ s Accepted Manuscript A Novel Approach to Improve the Planning of Adaptive and Interactive Sessions for the treatment of Major Depression Reference : Journal of Human Computer Studies, 87, 80–91. https://doi.org/10.1016/j.ijhcs.2015.11.003
Brunetti, N. D., De Gennaro, L., Dellegrottaglie, G., Di Giuseppe, G., Antonelli, G., & Di Biase, M. (2014). All for one, one for all: Remote telemedicine hub pre-hospital triage for public Emergency Medical Service 1-1-8 in a regional network for primary PCI in Apulia, Italy. European Research in Telemedicine, 3(1), 9–15. https://doi.org/10.1016/j.eurtel.2013.11.001
Brunetti, N. D., Scalvini, S., Acquistapace, F., Parati, G., Volterrani, M., Fedele, F., & Molinari, G. (2016). Corrigendum to ?Telemedicine for cardiovascular disease continuum: A position paper from the Italian Society of Cardiology Working Group on Telecardiology and Informatics? [Int J Cardiol 184 (2015) 452-458]. International Journal of Cardiology, 215, 546. https://doi.org/10.1016/j.ijcard.2016.04.160
Buchmueller, T. C., Jacobson, M., & Wold, C. (2006). How far to the hospital?. The effect of hospital closures on access to care. Journal of Health Economics, 25(4), 740–761. https://doi.org/10.1016/j.jhealeco.2005.10.006
Busse, R., Schreyögg, J., & Smith, P. C. (2008). Variability in healthcare treatment costs amongst nine eu countries - Results from the healthbasket project. Health Economics, 17(SUPPL. 1), S1–S8. https://doi.org/10.1002/hec.1330
Çali?kan, H. (2013). Selection of boron based tribological hard coatings using multi- criteria decision making methods. Materials and Design, 50, 742–749. https://doi.org/10.1016/j.matdes.2013.03.059
Calyam, P., Mishra, A., Antequera, R. B., Chemodanov, D., Berryman, A., Zhu, K., … Skubic, M. (2016). Synchronous Big Data analytics for personalized and remote physical therapy. Pervasive and Mobile Computing, 28, 3–20. https://doi.org/10.1016/j.pmcj.2015.09.004
Cardellini, V., Colajanni, M., & Yu, P. S. (1999). Dynamic load balancing on web-server systems. IEEE Internet Computing, 3(3), 28–39. https://doi.org/10.1109/4236.769420
Cassar, K., Godden, D. J., & Duncan, J. L. (2001). Community mortality after ruptured abdominal aortic aneurysm is unrelated to the distance from the surgical centre. British Journal of Surgery, 88(10), 1341–1343. https://doi.org/10.1046/j.0007- 1323.2001.01877.x Chakraborty, S., Ghosh, S. K., Jamthe, A., & Agrawala, D. P. (2013). Detecting mobility for monitoring patients with Parkinson‘s disease at home using RSSI in a wireless sensor network. Procedia Computer Science, 19, 956–961. https://doi.org/10.1016/j.procs.2013.06.132
Chan, M., Estève, D., Fourniols, J. Y., Escriba, C., & Campo, E. (2012). Smart wearable systems: Current status and future challenges. Artificial Intelligence in Medicine, 56(3), 137–156. https://doi.org/10.1016/j.artmed.2012.09.003
Chang, M.-Y., Pang, C., Michael Tarn, J., Liu, T.-S., & Yen, D. C. (2015). Exploring user acceptance of an e-hospital service: An empirical study in Taiwan. Computer Standards & Interfaces, 38, 35–43. https://doi.org/10.1016/j.csi.2014.08.004
Chen, S.-J., & Hwang, C.-L. (1992). Fuzzy Multiple Attribute Decision Making Methods. In Fuzzy multiple attribute decision making (pp. 289–486). Springer. https://doi.org/10.1007/978-3-642-46768-4_5
Chiang, H. P., Lai, C. F., & Huang, Y. M. (2014). A green cloud-assisted health monitoring service on wireless body area networks. Information Sciences, 284, 118– 129. https://doi.org/10.1016/j.ins.2014.07.013
Chowdhury, M., Mciver, W., & Light, J. (2012). Data association in remote health monitoring systems. IEEE Communications Magazine, 50(6), 144–149. https://doi.org/10.1109/MCOM.2012.6211499
Christensen, D., Jensen, N. M., Maaløe, R., Rudolph, S. S., Belhage, B., & Perrild, H. (2011a). Low compliance with a validated system for emergency department triage. Danish Medical Bulletin, 58(6), A4294. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21651880
Christensen, D., Jensen, N. M., Maaløe, R., Rudolph, S. S., Belhage, B., & Perrild, H. (2011b). Nurse-administered early warning score system can be used for emergency department triage. Danish Medical Bulletin, 58(6), A4221. https://doi.org/DMBA4221 [pii]
Clark, R. A., Inglis, S. C., McAlister, F. A., Cleland, J. G. F., & Stewart, S. (2007). Telemonitoring or structured telephone support programmes for patients with chronic heart failure: Systematic review and meta-analysis. British Medical Journal, 334(7600), 942–945. https://doi.org/10.1136/bmj.39156.536968.55
Clohessy, S., & Ehlers, A. (1999). PTSD symptoms and coping in ambulance service workers. British Journal of Clinical Psychology, 38(3), 32.
Cox, I., Roberts, L., & Stevens, S. (2002). How can we improve patient care? Community Eye Health / International Centre for Eye Health, 15(41), 1–3.
De Backere, F., Bonte, P., Verstichel, S., Ongenae, F., & De Turck, F. (2017). The OCarePlatform: A context-aware system to support independent living. Computer Methods and Programs in Biomedicine, 140, 111–120. https://doi.org/10.1016/j.cmpb.2016.11.008
De La Piedra, A., Braeken, A., Touhafi, A., & Wouters, K. (2013). Secure event logging in sensor networks. Computers and Mathematics with Applications, 65(5), 762–773. https://doi.org/10.1016/j.camwa.2012.06.019
De Silva, A. H. T. E. H. T. E., Sampath, W. H. P. H. P., Sameera, N. H. L. H. L., Amarasinghe, Y. W. R. W. R., & Mitani, A. (2018). Development of a novel telecare system, integrated with plantar pressure measurement system. Informatics in Medicine Unlocked, 12, 98–105. https://doi.org/10.1016/j.imu.2018.07.001
De Souza, V. C., & Strachan, D. P. (2005). Relationship between travel time to the nearest hospital and survival from ruptured abdominal aortic aneurysms: Record linkage study. Journal of Public Health, 27(2), 165–170. https://doi.org/10.1093/pubmed/fdi001
Derlet, R. W., Kinser, D., Ray, L., Hamilton, B., & McKenzie, J. (1995). Prospective Identification and Triage of Nonemergency Patients Out of an Emergency Department: A 5-Year Study. Annals of Emergency Medicine, 25(2), 215–223. https://doi.org/10.1016/S0196-0644(95)70327-6
Diaby, V., Campbell, K., & Goeree, R. (2013). Multi-criteria decision analysis (MCDA) in health care: A bibliometric analysis. Operations Research for Health Care, 2(1– 2), 20–24. https://doi.org/10.1016/j.orhc.2013.03.001
Diallo, O., Rodrigues, J. J. P. C., & Sene, M. (2012). Real-time data management on wireless sensor networks: A survey. Journal of Network and Computer Applications, 35(3), 1013–1021. https://doi.org/10.1016/j.jnca.2011.12.006
Dolan, J. G., Boohaker, E., Allison, J., & Imperiale, T. F. (2013). Patients‘ preferences and priorities regarding colorectal cancer screening. Medical Decision Making, 33(1), 59–70. https://doi.org/10.1177/0272989X12453502
Dong, J., & Yang, G.-H. (2015). Reliable State Feedback Control of T–S Fuzzy Systems With Sensor Faults. IEEE Transactions on Fuzzy Systems, 23(2), 421–433. https://doi.org/10.1109/TFUZZ.2014.2315298
Doumbouya, M. B., Kamsu-Foguem, B., Kenfack, H., & Foguem, C. (2014). Telemedicine using mobile telecommunication: Towards syntactic interoperability in teleexpertise. Telematics and Informatics, 31(4), 648–659. https://doi.org/10.1016/j.tele.2014.01.003
Doumbouya, M. B., Kamsu-Foguem, B., Kenfack, H., & Foguem, C. (2015). A framework for decision making on teleexpertise with traceability of the reasoning. Irbm, 36(1), 40–51. https://doi.org/10.1016/j.irbm.2014.09.002
Duke, J. M., & Aull-hyde, R. (2002). Identifying public preferences for land preservation using the analytic hierarchy process. Ecological Economics, 42(1), 131–145.
Duong-Ba, T., Nguyen, T., Bose, B., & Tran, D. A. (2014). Distributed client-server assignment for online social network applications. IEEE Transactions on Emerging Topics in Computing, 2(4), 422–435. https://doi.org/10.1109/TETC.2014.2358801
Durisko, C., McCue, M., Doyle, P. J., Dickey, M. W., & Fiez, J. A. (2016). A Flexible and Integrated System for the Remote Acquisition of Neuropsychological Data in Stroke Research. Telemedicine and E-Health, 22(12), 1032–1040. https://doi.org/10.1089/tmj.2015.0235
Elhadj, H. Ben, Elias, J., Chaari, L., & Kamoun, L. (2015). A Priority based Cross Layer Routing Protocol for healthcare applications. Ad Hoc Networks, 42. https://doi.org/http://dx.doi.org/10.1016/j.adhoc.2015.10.007
Faiola, A., & Holden, R. J. (2017). Consumer Health Informatics: Empowering Healthy- Living-Seekers Through mHealth. Progress in Cardiovascular Diseases, 59(5), 479–486. https://doi.org/10.1016/j.pcad.2016.12.006
Fan, X., Du, F., Guo, J., & Zhang, J. (2014). Energy independent clustering routing algorithm for wireless sensor networks. 11th International Conference on Fuzzy Systems and Knowledge Discovery. https://doi.org/10.1109/FSKD.2014.6980948
Farrohknia, N., Castrén, M., Ehrenberg, A., Lind, L., Oredsson, S., Jonsson, H., … Göransson, K. E. (2011). Emergency Department Triage Scales and Their Components: A Systematic Review of the Scientific Evidence. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 19(1), 42. https://doi.org/10.1186/1757-7241-19-42
Faulin, J., Juan, A. A., Grasman, S. E., & Fry, M. J. (2012). Decision Making in Service Industries: A Practical Approach. CRC Press.
Fezari, M., Rasras, R., & Emary, I. M. M. E. (2015). Ambulatory Health Monitoring System Using Wireless Sensors Node. Procedia Computer Science, 65(Iccmit), 86– 94. https://doi.org/10.1016/j.procs.2015.09.082
Figueredo, M. V. M., & Dias, J. S. (2004). Mobile Telemedicine System for Home Care and Patient Monitoring. Engineering in Medicine and Biology Society, 2004. IEMBS ?04. 26th Annual International Conference of the IEEE, 2, 3387–3390. https://doi.org/10.1109/iembs.2004.1403951
Fitzgerald, J. D., Soohoo, N. F., Losina, E., & Katz, J. N. (2012). Potential impact on patient residence to hospital travel distance and access to care under a policy of preferential referral to high-volume knee replacement hospitals. Arthritis Care and Research, 64(6), 890–897. https://doi.org/10.1002/acr.21611
Fourati, H., Idoudi, H., Val, T., Van Den Bossche, A., & Saidane, L. A. (2016). Performance evaluation of IEEE 802.15.6 CSMA/CA-based CANet WBAN. Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA. https://doi.org/10.1109/AICCSA.2015.7507181
Fraile, J. A., Bajo, J., Corchado, J. M., & Abraham, A. (2010). Applying wearable solutions in dependent environments. IEEE Transactions on Information Technology in Biomedicine, 14(6), 1459–1467. https://doi.org/10.1109/TITB.2010.2053849
Fratini, A., & Caleffi, M. (2014). Medical emergency alarm dissemination in urban environments. Telematics and Informatics, 31(3), 511–517. https://doi.org/10.1016/j.tele.2013.11.007
Frcpc, R. B., John, S., Brunswick, N., Rn, L. J., Bn, N. S., Msa, R. N., … Ontario, L. (1998). Implementation Guidelines for The Canadian Emergency Department Triage & Acuity Scale ( CTAS ). Canadian ED Triage & Acuity Scale. Canadian ED Triage & Acuity Scale, 32.
Gambhir, S. (2016). DWBAN : Dynamic Priority based WBAN Architecture for Healthcare System. 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), 0–6.
Ganapathy, K., Priya, B., Priya, B., Dhivya, A., Prashanth, V., & Vaidehi, V. (2013). SOA framework for geriatric remote health care using wireless sensor network. Procedia Computer Science, 19(Fams), 1012–1019. https://doi.org/10.1016/j.procs.2013.06.141
Ganapathy, K., Vaidehi, V., Kannan, B., & Murugan, H. (2014). Hierarchical Particle Swarm Optimization with Ortho-Cyclic Circles. Expert Systems with Applications, 41(7), 3460–3476. https://doi.org/10.1016/j.eswa.2013.10.050
Ganz, A., Schafer, J. M., Tang, J., Yang, Z., Yi, J., & Ciottone, G. (2015). Urban Search and Rescue Situational Awareness using DIORAMA Disaster Management System. Procedia Engineering, 107, 349–356. https://doi.org/10.1016/j.proeng.2015.06.091
Gao, T., Massey, T., Selavo, L., Crawford, D., Chen, B. R., Lorincz, K., … Welsh, M. (2007). The advanced health and disaster aid network: A light-weight wireless medical system for tiage. IEEE Transactions on Biomedical Circuits and Systems, 1(3), 203–216. https://doi.org/10.1109/TBCAS.2007.910901
Gaynor, M., & Waterman, J. (2016). Design framework for sensors and RFID tags with healthcare applications. Health Policy and Technology, 5(4), 357–369. https://doi.org/10.1016/j.hlpt.2016.07.007
Gbanie, S. P., Tengbe, P. B., Momoh, J. S., Medo, J., & Kabba, V. T. S. (2013). Modelling landfill location using Geographic Information Systems (GIS) and Multi- Criteria Decision Analysis (MCDA): Case study Bo, Southern Sierra Leone. Applied Geography, 36(January), 3–12. https://doi.org/10.1016/j.apgeog.2012.06.013
Gehr, C. R., Von Behren, P. D., Williams, M. P., & Wood, R. B. (1998, October). Dynamic server switching for maximum server availability and load balancing. Google Patents.
Georgopoulos, V. C., & Stylios, C. D. (2013). Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care. In R. Seising & M. E. Tabacchi (Eds.), Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care (Vol. 302, pp. 415–436). Berlin: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-36527-0
Ghanavati, S., Abawaji, J., & Izadi, D. (2015). A Congestion Control Scheme Based on Fuzzy Logic in Wireless Body Area Networks. In 2015 IEEE 14th International Symposium on Network Computing and Applications (pp. 235–242). IEEE. https://doi.org/10.1109/NCA.2015.38
Ghanavati, S., Abawajy, J., & Izadi, D. (2016). ECG rate control scheme in pervasive health care monitoring system. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 2265–2270). IEEE. https://doi.org/10.1109/FUZZ- IEEE.2016.7737975
Godfrey, B., et al. (2000). Emergency Medical Guidelines. Sunshine Act of Florida (Third Edit). Sunshine Act of Florida.
Gogan, J. L., Davidson, E. J., & Proudfoot, J. (2016). The HealthCare.gov project. Journal of Information Technology Teaching Cases, 6(2), 99–110. https://doi.org/10.1057/jittc.2016.2
Gómez, J., Oviedo, B., & Zhuma, E. (2016). Patient Monitoring System Based on Internet of Things. Procedia Computer Science, 83(Ant), 90–97. https://doi.org/10.1016/j.procs.2016.04.103
Grossmann, F. F., Delport, K., & Keller, D. I. (2009). Emergency Severity Index. Notfall + Rettungsmedizin, 12(4), 290–292. https://doi.org/10.1007/s10049-009-1156-7 Guindo, L. A., Wagner, M., Baltussen, R., Rindress, D., van Til, J., Kind, P., &
Goetghebeur, M. M. (2012). From efficacy to equity: Literature review of decision criteria for resource allocation and healthcare decisionmaking. Cost Effectiveness and Resource Allocation, 10(1), 9. https://doi.org/10.1186/1478-7547-10-9
Gunasekaran, S., & Suresh, M. (2014). A novel control of disaster protection (NCDP) for pilgrims by pan technology. In 2014 IEEE 8th International Conference on Intelligent Systems and Control: Green Challenges and Smart Solutions, ISCO 2014 - Proceedings (pp. 103–107). IEEE. https://doi.org/10.1109/ISCO.2014.7103927 Gündo?du, K., & Çalhan, A. (2016). An Implementation of Wireless Body Area Networks for Improving Priority Data Transmission Delay. Journal of Medical Systems, 40(3), 1–7. https://doi.org/10.1007/s10916-016-0443-3
Haque, S. A., & Aziz, S. M. (2013). False Alarm Detection in Cyber-physical Systems for Healthcare Applications. AASRI Procedia, 5, 54–61. https://doi.org/10.1016/j.aasri.2013.10.058
Haralambopoulos, D. A., & Polatidis, H. (2003). Renewable energy projects: structuring a multi-criteria group decision-making framework. Renewable Energy, 28(6), 961– 973.
Harper, R. E., Salapura, V., & Viswanathan, M. (2017, March). Automatic management of server failures. Google Patents.
Hedin, D. S., Kollmann, D. T., Gibson, P. L., Riehle, T. H., & Seifert, G. J. (2014). Distance bounded energy detecting ultra-wideband impulse radio secure protocol. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (Vol. 2014, pp. 6619–6622). IEEE. https://doi.org/10.1109/EMBC.2014.6945145
Hermens, H., op den Akker, H., Tabak, M., Wijsman, J., & Vollenbroek, M. (2014). Personalized Coaching Systems to support healthy behavior in people with chronic conditions. Journal of Electromyography and Kinesiology, 24(6), 815–826. https://doi.org/10.1016/j.jelekin.2014.10.003
Hilgerink, M. P., Hummel, M. J. M., Manohar, S., Vaartjes, S. R., & Ijzerman, M. J. (2011). Assessment of the added value of the Twente Photoacoustic Mammoscope in breast cancer diagnosis. Medical Devices: Evidence and Research, 4(1), 107–115. https://doi.org/10.2147/MDER.S20169
Hindia, M. N., Rahman, T. A., Ojukwu, H., Hanafi, E. B., & Fattouh, A. (2016). Enabling remote health-caring utilizing IoT concept over LTE-femtocell networks. PLoS ONE, 11(5), e0155077. https://doi.org/10.1371/journal.pone.0155077
Ho, W. (2008). Integrated analytic hierarchy process and its applications - A literature review. European Journal of Operational Research, 186(1), 211–228. https://doi.org/10.1016/j.ejor.2007.01.004 Https://bestdoctors.com/. (n.d.). Retreved From.
Hu, L., Zhang, Y., Feng, D., Hassan, M. M., Alelaiwi, A., & Alamri, A. (2015). Design of QoS-Aware Multi-Level MAC-Layer for Wireless Body Area Network. Journal of Medical Systems, 39(12), 192. https://doi.org/10.1007/s10916-015-0336-x
Hu, P. F., Yang, S., Li, H. C., Stansbury, L. G., Yang, F., Hagegeorge, G., … Mackenzie, C. F. (2017). Reliable Collection of Real-Time Patient Physiologic Data from less Reliable Networks: a ?Monitor of Monitors? System (MoMs). Journal of Medical Systems, 41(1), 3. https://doi.org/10.1007/s10916-016-0648-5
Hummel, M. J. M., Volz, F., van Manen, J. G., Danner, M., Dintsios, C.-M., IJzerman, M. J., & Gerber, A. (2012). Using the Analytic Hierarchy Process to Elicit Patient Preferences. The Patient: Patient-Centered Outcomes Research, 5(4), 225–237. https://doi.org/10.1007/BF03262495
Hung, C., Chang, P., & Huang, Y. (2005). Comparison of Fuzzy-based MCDM and Non- fuzzy MCDM in Setting a New Fee Schedule for Orthopedic Procedures in Taiwan ‘ s National Health Insurance Program. WSEAS Transactions on Mathematics, 2005(1), 281–285.
Hussain, A., Wenbi, R., Da Silva, A. L., Nadher, M., & Mudhish, M. (2015). Health and emergency-care platform for the elderly and disabled people in the Smart City. Journal of Systems and Software, 110, 253–263. https://doi.org/10.1016/j.jss.2015.08.041
Hussain, M., Zaidan, A. A., Zidan, B. B., Iqbal, S., Ahmed, M. M., Albahri, O. S., & Albahri, A. S. (2018, March). Conceptual framework for the security of mobile health applications on Android platform. Telematics and Informatics. https://doi.org/10.1016/j.tele.2018.03.005
Hwang, C.-L., & Yoon, K. (1981). Multiple Attribute Decision Methods and Applications. 2011.
Hwang, T. H., Kim, D. S., & Kim, J. G. (2013). An on-time power-aware scheduling scheme for medical sensor SoC-based WBAN systems. Sensors (Switzerland), 13(1), 375–392. https://doi.org/10.3390/s130100375
Iftikhar, M., & Ahmad, I. (2014). A novel analytical model for provisioning QoS in body area sensor networks. Procedia Computer Science, 32, 900–907. https://doi.org/10.1016/j.procs.2014.05.509
Iftikhar, M., Elaiwi, N. A. I., & Aksoy, M. S. (2014). Performance analysis of priority queuing model for low power Wireless Body Area Networks (WBANs). Procedia Computer Science, 34, 518–525. https://doi.org/10.1016/j.procs.2014.07.060
Jadhav, A., & Sonar, R. (2009). Analytic Hierarchy Process (AHP), Weighted Scoring Method (WSM), and Hybrid Knowledge Based System (HKBS) for Software Selection: A Comparative Study. In 2009 Second International Conference on Emerging Trends in Engineering & Technology (pp. 991–997). IEEE. https://doi.org/10.1109/ICETET.2009.33
J?drkiewicz, R., Tsakovski, S., Lavenu, A., Namie?nik, J., & Tobiszewski, M. (2018). Simultaneous grouping and ranking with combination of SOM and TOPSIS for selection of preferable analytical procedure for furan determination in food. Talanta, 178, 928–933. https://doi.org/10.1016/j.talanta.2017.10.044
Jeong, S., Youn, C. H., Shim, E. B., Kim, M., Cho, Y. M., & Peng, L. (2012). An integrated healthcare system for personalized chronic disease care in home-hospital environments. IEEE Transactions on Information Technology in Biomedicine, 16(4), 572–585. https://doi.org/10.1109/TITB.2012.2190989
Jin, X., Liu, H. H., Gandhi, R., Kandula, S., Mahajan, R., Zhang, M., … Wattenhofer, R. (2014). Dynamic scheduling of network updates. In ACM SIGCOMM Computer Communication Review (Vol. 44, pp. 539–550). ACM. https://doi.org/10.1145/2740070.2626307
Johnson Colin, D., & Taylor, I. (2010). Recent Advances in Surgery 33 (Vol. 27). CRC Press. https://doi.org/10.5005/jp/books/11221
Kaiser Foundation. (2007). Trends in Health Care Costs and Spending. kasir family foundation.
Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., Albahri, O. S., & Albahri, A. S. (2018). Based on Real Time Remote Health Monitoring Systems: A New Approach for Prioritization ?Large Scales Data? Patients with Chronic Heart Diseases Using Body Sensors and Communication Technology. Journal of Medical Systems, 42(4), 69. https://doi.org/10.1007/s10916-018-0916-7
Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., & Muzammil, H. (2018a). Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related ?Big Data? Using Body Sensors information and Communication Technology. Journal of Medical Systems, 42(2). https://doi.org/10.1007/s10916-017-0883-4
Kalid, N., Zaidan, A. A., Zaidan, B. B., Salman, O. H., Hashim, M., & Muzammil, H. (2018b). Based Real Time Remote Health Monitoring Systems: A Review on Patients Prioritization and Related ?Big Data? Using Body Sensors information and Communication Technology. Journal of Medical Systems, 42(2), 30. https://doi.org/10.1007/s10916-017-0883-4
Kamsu-Foguem, B., Tchuenté-Foguem, G., & Foguem, C. (2014). Conceptual graph operations for formal visual reasoning in the medical domain. IRBM, 35(5), 262– 270. https://doi.org/10.1016/j.irbm.2014.04.001
Kandakoglu, A., Celik, M., & Akgun, I. (2009). A multi-methodological approach for shipping registry selection in maritime transportation industry. Mathematical and Computer Modelling, 49(3–4), 586–597. https://doi.org/10.1016/j.mcm.2008.09.001
Kao, D. P., Lindenfeld, J., Macaulay, D., Birnbaum, H. G., Jarvis, J. L., Desai, U. S., & Page, R. L. (2016). Impact of a Telehealth and Care Management Program on All- Cause Mortality and Healthcare Utilization in Patients with Heart Failure. Telemedicine and E-Health, 22(1), 2–11. https://doi.org/10.1089/tmj.2015.0007
Kateretse, C., Lee, G.-W., & Huh, E.-N. (2013). A Practical Traffic Scheduling Scheme for Differentiated Services of Healthcare Systems on Wireless Sensor Networks. Wireless Personal Communications, 71(2), 909–927. https://doi.org/10.1007/s11277-012-0851-8
Katib, A., Rao, D., Rao, P., Williams, K., & Grant, J. (2015). A prototype of a novel cell phone application for tracking the vaccination coverage of children in rural communities. Computer Methods and Programs in Biomedicine, 122(2), 215–228. https://doi.org/10.1016/j.cmpb.2015.08.008
Kaur, J., Saini, K. S., & Grewal, R. (2015). Priority based congestion avoidance hybrid scheme for Wireless Sensor Network. In 2015 1st International Conference on Next Generation Computing Technologies (NGCT) (pp. 158–165). IEEE. https://doi.org/10.1109/NGCT.2015.7375104
Keeney, R. L., & Raiffa, H. (1976). Decisions With Multiple Objectives: Preference and Value Tradeoffs. Cambridge university press.
Khan, A. M. R., Prasad, P. N., & Rajamanoharane, S. (2010). A decision-making framework for service quality measurements in hospitals. International Journal of Enterprise Network Management, 4(1), 80. https://doi.org/10.1504/IJENM.2010.034478
Khelil, A. (2011). Pa2Pa: Patient to patient communication for emergency response support. 2011 IEEE 13th International Conference on E-Health Networking, Applications and Services, HEALTHCOM 2011. https://doi.org/10.1109/HEALTH.2011.6026755
Kim, H. K. (2014). Convergence agent model for developing u-healthcare systems. Future Generation Computer Systems, 35, 39–48. https://doi.org/10.1016/j.future.2013.10.025
Kim, K., Kyung, T., Kim, W., Shin, C., Song, Y., Lee, M. Y., … Cho, Y. (2009). Efficient Management Design for Swimming Exercise Treatment. The Korean Journal of Physiology and Pharmacology, 13(6), 497. https://doi.org/10.4196/kjpp.2009.13.6.497
Kim, R. H., & Kim, P. S. (2015). An Effect of Delay Reduced MAC Protocol for WBAN based Medical Signal Monitoring, 434–437.
Kim, W., Han, S. K., Oh, K. J., Kim, T. Y., Ahn, H., & Song, C. (2010). The dual analytic hierarchy process to prioritize emerging technologies. Technological Forecasting and Social Change, 77(4), 566–577. https://doi.org/10.1016/j.techfore.2009.12.008
Kitamura, Y. (2010). Decision-making process of patients with gynecological cancer regarding their cancer treatment choices using the analytic hierarchy process. Japan Journal of Nursing Science, 7(2), 148–157. https://doi.org/10.1111/j.1742- 7924.2010.00147.x
Klersy, C., De Silvestri, A., Gabutti, G., Regoli, F., & Auricchio, A. (2009). A Meta- Analysis of Remote Monitoring of Heart Failure Patients. Journal of the American College of Cardiology, 54(18), 1683–1694. https://doi.org/10.1016/j.jacc.2009.08.017
Kormanyos, B., & Pataki, B. (2013). Multilevel simulation of daily activities: Why and how? In 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) (pp. 1–6). IEEE. https://doi.org/10.1109/CIVEMSA.2013.6617386
Kovalchuk, S. V., Krotov, E., Smirnov, P. A., Nasonov, D. A., & Yakovlev, A. N. (2018). Distributed data-driven platform for urgent decision making in cardiological ambulance control. Future Generation Computer Systems, 79, 144–154. https://doi.org/10.1016/j.future.2016.09.017
Kumar, N., Kaur, K., Jindal, A., & Rodrigues, J. J. P. C. (2015). Providing healthcare services on-the-fly using multi-player cooperation game theory in Internet of Vehicles (IoV) environment. Digital Communications and Networks, 1(3), 191–203. https://doi.org/10.1016/j.dcan.2015.05.001
Labovitz, C., Malan, G. R., & Jahanian, F. (1998). Internet Routing Instability - Networking, IEEE/ACM Transactions on. IEEE/ACM Transactions on Networking, 6(5), 515–527.
Lahby, M., Cherkaoui, L., & Adib, A. (2013). A novel ranking algorithm based network selection for heterogeneous wireless access. Journal of Networks, 8(2), 263–272. https://doi.org/10.4304/jnw.8.2.263-272
Lam, K., & Zhao, X. (1998). An application of quality function deployment to improve the quality of teaching. International Journal of Quality & Reliability Management, 15(4), 389–413. https://doi.org/10.1108/02656719810196351
Lamprinakos, G. C., Asanin, S., Broden, T., Prestileo, A., Fursse, J., Papadopoulos, K. A., … Venieris, I. S. (2015). An integrated remote monitoring platform towards Telehealth and Telecare services interoperability. Information Sciences, 308(March), 23–37. https://doi.org/10.1016/j.ins.2015.02.032
Leister, J., & Stausberg, J. (2007). Why Do Patients Select a Hospital? Journal of Hospital Marketing & Public Relations, 17(2), 13–31. https://doi.org/10.1300/J375v17n02_03
Leite, C. R. M., Sizilio, G. R. A., Neto, A. D. D., Valentim, R. A. M., & Guerreiro, A. M. G. (2011). A fuzzy model for processing and monitoring vital signs in ICU patients. BioMedical Engineering Online, 10(1), 68. https://doi.org/10.1186/1475-925X-10- 68
Lerner, E. B., Cone, D. C., Weinstein, E. S., Schwartz, R. B., Coule, P. L., Cronin, M., … Hunt, R. C. (2011). Mass casualty triage: An evaluation of the science and refinement of a national guideline. Disaster Medicine and Public Health Preparedness, 5(2), 129–137. https://doi.org/10.1001/dmp.2011.39
Lesmes, D., Castillo, M., & Zarama, R. (2009). Application of the Analytic Network Process (ANP) to establish weights in order to re-accredit a program of a university. In Proc. of The 10th International Symposium on The Analytic Hierarchy Process (Vol. 29).
Li, C., Yuan, X., Yang, L., & Song, Y. (2015). A hybrid lifetime extended directional approach for WBANs. Sensors (Switzerland), 15(11), 28005–28030. https://doi.org/10.3390/s151128005
Li, H., & Tan, J. (2006). Body Sensor Network Based Context Aware QRS Detection. Pervasive Health Conference and Workshops, 2006, 1(2), 1–8. https://doi.org/10.1109/PCTHEALTH.2006.361683
Li, N., Lin, K., Yong, S., Chen, X., Wang, X., & Zhang, X. (2015). Design and implementation of a MAC protocol for a wearable monitoring system on human body. In 2015 IEEE 11th International Conference on ASIC (ASICON) (pp. 1–4). IEEE. https://doi.org/10.1109/ASICON.2015.7517194
Liaqat, T., Javaid, N., Ali, S. M., Imran, M., & Alnuem, M. (2015). Depth-based energy- balanced hybrid routing protocol for underwater WSNs. Proceedings - 2015 18th International Conference on Network-Based Information Systems, NBiS 2015. https://doi.org/10.1109/NBiS.2015.7
Liberatore, M. J., & Nydick, R. L. (2008). The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research, 189(1), 194–207. https://doi.org/10.1016/j.ejor.2007.05.001
Liddy, C., Dusseault, J. J., Dahrouge, S., Hogg, W., Lemelin, J., & Humbert, J. (2008). Telehomecare for patients with multiple chronic illnesses: Pilot study. Canadian Family Physician, 54(1), 58–65. https://doi.org/54/1/58 [pii]
Ligmann-Zielinska, A., & Jankowski, P. (2012). Impact of proximity-adjusted preferences on rank-order stability in geographical multicriteria decision analysis. Journal of Geographical Systems, 14(2), 167–187. https://doi.org/10.1007/s10109- 010-0140-6
Lima Junior, F. R., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing Journal, 21, 194–209. https://doi.org/10.1016/j.asoc.2014.03.014
Lin, D., Labeau, F., Yao, Y., Vasilakos, A. V., & Tang, Y. (2016). Admission Control over Internet of Vehicles Attached with Medical Sensors for Ubiquitous Healthcare Applications. IEEE Journal of Biomedical and Health Informatics, 20(4), 1195– 1204. https://doi.org/10.1109/JBHI.2015.2431744
Lingsma, H. F., Steyerberg, E. W., Eijkemans, M. J. C., Dippel, D. W. J., Scholte Op Reimer, W. J. M., & van Houwelingen, H. C. (2009). Comparing and ranking hospitals based on outcome: Results from The Netherlands Stroke Survey. Qjm, 103(2), 99–108. https://doi.org/10.1093/qjmed/hcp169
Liu, C. T., Long, A. G., Li, Y. C., Tsai, K. C., & Kuo, H. S. (2001). Sharing patient care records over the World Wide Web. International Journal of Medical Informatics, 61(2–3), 189–205. https://doi.org/10.1016/S1386-5056(01)00141-1
Liu, X., Zheng, Y., Phyu, M. W., Zhao, B., Je, M., & Yuan, X. (2011). Multiple functional ECG signal is processing for wearable applications of long-term cardiac monitoring. IEEE Transactions on Biomedical Engineering, 58(2), 380–389. https://doi.org/10.1109/TBME.2010.2061230
Lounis, A., Hadjidj, A., Bouabdallah, A., & Challal, Y. (2016). Healing on the cloud: Secure cloud architecture for medical wireless sensor networks. Future Generation Comp. Syst. 55: 266-277 (2016). Future Generation Computer Systems, 55, 266– 277. https://doi.org/http://dx.doi.org/10.1016/j.future.2015.01.009
Lwin, M. O., Vijaykumar, S., Fernando, O. N. N., Cheong, S. A., Rathnayake, V. S., Lim, G., … Foo, S. (2014). A 21st century approach to tackling dengue: Crowdsourced surveillance, predictive mapping and tailored communication. Acta Tropica, 130(1), 100–107. https://doi.org/10.1016/j.actatropica.2013.09.021
Malczewski, J. (1999). GIS and Multicriteria Decision Analysis. GIS, Remote Sensing, & Cartography. John Wiley & Sons. https://doi.org/10.1353/geo.2002.0003
Mamoon, I. Al, Muzahidul-Islam, A. K. M., Baharun, S., Komaki, S., & Ahmed, A. (2015). Architecture and communication protocols for cognitive radio network enabled hospital. In International Symposium on Medical Information and Communication Technology, ISMICT (Vol. 2015–May, pp. 170–174). IEEE. https://doi.org/10.1109/ISMICT.2015.7107522
Manfredi, S. (2014). Congestion control for differentiated healthcare service delivery in emerging heterogeneous wireless body area networks. IEEE Wireless Communications, 21(2), 80–90. https://doi.org/10.1109/MWC.2014.6812295
Mani, D., & Mahendran, A. (2017). Availability modelling of fault tolerant cloud computing system. International Journal of Intelligent Engineering and Systems, 10(1), 154–165. https://doi.org/10.22266/ijies2017.0228.17
MANSOOREH, M., & PET-EDWARDS, J. (1997). Technical Briefing: Making Multiple-Objective Decisions. IEEE Computer Society Press.
Mansor, H., Meskam, S. S., Zamery, N. S., Rusli, N. Q. A. M., & Akmeliawati, R. (2015). Portable heart rate measurement for remote health monitoring system. 2015 10th Asian Control Conference (ASCC), (June 2013), 1–5. https://doi.org/10.1109/ASCC.2015.7244405
Marsh, K., Dolan, P., Kempster, J., & Lugon, M. (2013). Prioritizing investments in public health: A multi-criteria decision analysis. Journal of Public Health (United Kingdom), 35(3), 460–466. https://doi.org/10.1093/pubmed/fds099
Marsh, K., Lanitis, T., Neasham, D., Orfanos, P., & Caro, J. (2014). Assessing the value of healthcare interventions using multi-criteria decision analysis: A review of the literature. PharmacoEconomics, 32(4), 345–365. https://doi.org/10.1007/s40273- 014-0135-0
Martí, R., Robles, S., Martín-Campillo, A., & Cucurull, J. (2009). Providing early resource allocation during emergencies: The mobile triage tag. Journal of Network and Computer Applications, 32(6), 1167–1182. https://doi.org/10.1016/j.jnca.2009.05.006
Matin, M. (Ed.). (2012). Wireless Sensor Networks - Technology and Protocols. InTech. https://doi.org/10.5772/2604
Mazomenos, E. B., Biswas, D., Acharyya, A., Chen, T., Maharatna, K., Rosengarten, J., … Curzen, N. (2013). A low-complexity ECG feature extraction algorithm for mobile healthcare applications. IEEE Journal of Biomedical and Health Informatics, 17(2), 459–469. https://doi.org/10.1109/TITB.2012.2231312
Meizoso, J. P., Allen, C. J., Ray, J. J., Van Haren, R. M., Teisch, L. F., Baez, X. R., … Proctor, K. G. (2016). Evaluation of Miniature Wireless Vital Signs Monitor in a Trauma Intensive Care Unit. Military Medicine, 181(5S), https://doi.org/10.7205/MILMED-D-15-00162
Mendes, J., Simões, H., Rosa, P., Costa, N., Rabadão, C., & Pereira, A. (2013). Secure low-cost solution for elder‘s eCardio surveillance. Procedia Computer Science, 27(Dsai 2013), 46–56. https://doi.org/10.1016/j.procs.2014.02.007
Mendoza, G. A., & Martins, H. (2006). Multi-criteria decision analysis in natural resource management. Forest Ecology and Management (Vol. 230). Ashgate Publishing, Ltd.
Merzougui, R. (2015). Adaptation of an Intelligent Mobile Assistant Medical (IMAM) of the Heterogeneous Data for the Telemedicine Services: Design and Implementation. Wireless Personal Communications, 84(4), 3091–3107. https://doi.org/10.1007/s11277-015-2785-4
Miah, S. J., Hasan, J., & Gammack, J. G. (2017). On-Cloud Healthcare Clinic: An e- health consultancy approach for remote communities in a developing country. Telematics and Informatics, 34(1), 311–322. https://doi.org/10.1016/j.tele.2016.05.008
Minutolo, A., Esposito, M., & De Pietro, G. (2015). Design and validation of a light- weight reasoning system to support remote health monitoring applications. Engineering Applications of Artificial Intelligence, 41, 232–248. https://doi.org/10.1016/j.engappai.2015.01.019
Mirkovic, J., Bryhni, H., & Ruland, C. (2012). A framework for the development of ubiquitous patient support systems. Proceedings of the 6th International Conference on Pervasive Computing Technologies for Healthcare. https://doi.org/10.4108/icst.pervasivehealth.2012.248594
Misra, S., & Chatterjee, S. (2014). Social choice considerations in cloud-assisted WBAN architecture for post-disaster healthcare: Data aggregation and channelization. Information Sciences, 284, 95–117. https://doi.org/10.1016/j.ins.2014.05.010
Misra, S., & Sarkar, S. (2015). Priority-based time-slot allocation in wireless body area networks during medical emergency situations: An evolutionary game-theoretic perspective. IEEE Journal of Biomedical and Health Informatics, 19(2), 541–548. https://doi.org/10.1109/JBHI.2014.2313374
Moore, P., Thomas, A., Qassem, T., Bessis, N., & Hu, B. Monitoring Patients with Mental Disorders, 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing § (2015). https://doi.org/10.1109/IMIS.2015.15
Moreno, S., Quintero, A., Ochoa, C., Bonfante, M., Villareal, R., & Pestana, J. Remote monitoring system of vital signs for triage and detection of anomalous patient states in the emergency room, 2016 21st Symposium on Signal Processing, Images and Artificial Vision, STSIVA 2016 § (2016). https://doi.org/10.1109/STSIVA.2016.7743353
Moretti, S., Cicalò, S., Mazzotti, M., Tralli, V., & Chiani, M. (2014). Content/context- aware multiple camera selection and video adaptation for the support of m-health services. Procedia Computer Science, 40(C), 206–213. https://doi.org/10.1016/j.procs.2014.12.028
Moser, D. K., Kimble, L. P., Alberts, M. J., Alonzo, A., Croft, J. B., Dracup, K., … Zerwic, J. J. (2006). Reducing delay in seeking treatment by patients with acute coronary syndrome and stroke: A scientific statement from the American Heart Association Council on Cardiovascular Nursing and Stroke Council. Circulation, 114(2), 168–182. https://doi.org/10.1161/CIRCULATIONAHA.106.176040
Moutacalli, M. T., Marmen, V., Bouzouane, A., & Bouchard, B. (2013). Activity pattern mining using temporal relationships in a smart home. In 2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE) (pp. 83–87). IEEE. https://doi.org/10.1109/CICARE.2013.6583073
Mühlbacher, A., & Kaczynski, A. (2016). Making good decisions in healthcare with multi-criteria decision analysis: the use, current research and future development of MCDA. Applied Health Economics and Health Policy, 14(1), 29–40. https://doi.org/10.1007/s40258-015-0203-4
Murtaza, S., Al, R., & Email, W. S. (2013). QoS Taxonomy towards Wireless Body Area. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 2(4), 221–234.
Nageba, E., Rubel, P., & Fayn, J. (2013). Towards an intelligent exploitation of heterogeneous and distributed resources in cooperative environments of eHealth. Irbm, 34(1), 79–85. https://doi.org/10.1016/j.irbm.2012.12.002
Negra, R., Jemili, I., & Belghith, A. (2016). Wireless Body Area Networks: Applications and Technologies. Procedia Computer Science, 83, 1274–1281. https://doi.org/10.1016/j.procs.2016.04.266
Nguyen, T., Khosravi, A., Creighton, D., & Nahavandi, S. (2015). Medical data classification using interval type-2 fuzzy logic system and wavelets. Applied Soft Computing Journal, 30(4), 812–822. https://doi.org/10.1016/j.asoc.2015.02.016
Nicholl, J., West, J., Goodacre, S., & Turner, J. (2007). The relationship between distance to hospital and patient mortality in emergencies: An observational study. Emergency Medicine Journal, 24(9), 665–668. https://doi.org/10.1136/emj.2007.047654
Nilsson, H., Nordström, E.-M., & Öhman, K. (2016). Decision Support for Participatory Forest Planning Using AHP and TOPSIS. Forests, 7(5), 100. https://doi.org/10.3390/f7050100
Niswar, M., Ilham, A. A., Palantei, E., Sadjad, R. S., Ahmad, A., Suyuti, A., … Adi. (2013). Performance evaluation of ZigBee-based wireless sensor network for monitoring patients‘ pulse status. In 2013 International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 291–294). IEEE. https://doi.org/10.1109/ICITEED.2013.6676255
Niswar, M., Wijaya, A. S., Ridwan, M., Adnan, A., Ilham, A. A., Sadjad, R. S., & Vogel, A. (2015). The design of wearable medical device for triaging disaster casualties in developing countries. In 2015 5th International Conference on Digital Information Processing and Communications, ICDIPC 2015 (pp. 207–212). IEEE. https://doi.org/10.1109/ICDIPC.2015.7323030
Okura, T., Enomoto, D., Miyoshi, K., Nagao, T., Kukida, M., Tanino, A., … Uemura, H. (2016). The Importance of Walking for Control of Blood Pressure: Proof Using a Telemedicine System. Telemedicine and E-Health, 22(12), 1019–1023. https://doi.org/10.1089/tmj.2016.0008
Oliveira, M., Fontes, D. B. M. M., & Pereira, T. (2014). Multicriteria decision making: a case study in the automobile industry. Annals of Management Science, 3(1), 109.
Ortíz, M. A., Cómbita, J. P., La De Hoz, Á. A., De Felice, F., & Petrillo, A. (2016). An integrated approach of AHP-DEMATEL methods applied for the selection of allied hospitals in outpatient service. International Journal of Medical Engineering and Informatics, 8(2), 87–107. https://doi.org/10.1504/IJMEI.2016.075760
Palozzi, G., Binci, D., & Appolloni, A. (2017). e-Health and Co-production: Critical Drivers for Chronic Diseases Management. Service Business Model Innovation in Healthcare and Hospital Management. https://doi.org/10.1007/978-3-319-46412- 1_15
Paulus, A., Meisen, P., Meisen, T., Jeschke, S., Czaplik, M., & Hirsch, F. (2016). AUDIME: Augmented disaster medicine. In 2015 17th International Conference on E-Health Networking, Application and Services, HealthCom 2015 (pp. 342–345). Cham: IEEE. https://doi.org/10.1109/HealthCom.2015.7454522
Pawar, P., Jones, V., van Beijnum, B. J. F., & Hermens, H. (2012). A framework for the comparison of mobile patient monitoring systems. Journal of Biomedical Informatics, 45(3), 544–556. https://doi.org/10.1016/j.jbi.2012.02.007
Pecchia, L., Bath, P. A., Pendleton, N., & Bracale, M. (2011). Analytic Hierarchy Process (AHP) for examining healthcare professionals‘ assessments of risk factors: The relative importance of risk factors for falls in community-dwelling older people. Methods of Information in Medicine, 50(5), 435–444. https://doi.org/10.3414/ME10- 01-0028
Peleg, M., Shahar, Y., Quaglini, S., Broens, T., Budasu, R., Fung, N., … van Schooten, B. (2017). Assessment of a personalized and distributed patient guidance system. International Journal of Medical Informatics, 101, 108–130. https://doi.org/10.1016/j.ijmedinf.2017.02.010
Pertet, S., Pertet, S., Narasimhan, P., & Narasimhan, P. (2005). Causes of failure in web applications. Parallel Data Laboratory, (December), 1–19.
Petrovic-Lazarevic, S., & Abraham, A. (2004). Hybrid Fuzzy-Linear Programming Approach for Multi Criteria Decision Making Problems. Neural, Parallel Sci. Comput., 11(1 & 2), 53–68.
Phillips, L., & Bana e Costa, C. (2007). Transparent priorisation, budgeting and resource allocation with multicriteria decision analysis and decision conferencing. Annals of Operations Research, 154(1), 51–68.
Piotin, S., Benassarou, A., Blanchard, F., Nocent, O., & Bertin, E. (2013). Abdominal morphometric data acquisition using depth sensors. In 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013 (pp. 653–657). IEEE. https://doi.org/10.1109/HealthCom.2013.6720757
Pombo, N., Garcia, N., Felizardo, V., & Bousson, K. (2015). Big data reduction using RBFNN: A predictive model for ECG waveform for eHealth platform integration. In 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services, Healthcom 2014 (pp. 66–70). IEEE. https://doi.org/10.1109/HealthCom.2014.7001815
Puri, T., Challa, R. K., & Sehgal, N. K. (2015). Energy efficient QoS aware MAC layer time slot allocation scheme for WBASN. In 2015 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015 (pp. 966– 972). IEEE. https://doi.org/10.1109/ICACCI.2015.7275736
Qader, M. A., Zaidan, B. B., Zaidan, A. A., Ali, S. K., Kamaluddin, M. A., & Radzi, W. B. (2017). A methodology for football players selection problem based on multi- measurements criteria analysis. Measurement: Journal of the International Measurement Confederation, 111, 38–50. https://doi.org/10.1016/j.measurement.2017.07.024
Qin, Y., Li, L., Zhong, X., Yang, Y., & Gwee, C. L. (2015). A Cross-Layer QoS Design with Energy and Traffic Balance Aware for Different Types of Traffic in MANETs. Wireless Personal Communications, 85(3), 1429–1449. https://doi.org/10.1007/s11277-015-2849-5
Qu, L., & Chen, Y. (2008). A hybrid MCDM method for route selection of multimodal transportation network. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5263 LNCS(PART 1), 374–383. https://doi.org/10.1007/978-3-540-87732-5-42
Radhakrishnan, S., Duvvuru, A., & Kamarthi, S. V. (2014). Investigating Discrete Event Simulation Method to Assess the Effectiveness of Wearable Health Monitoring Devices. Procedia Economics and Finance, 11(14), 838–856. https://doi.org/10.1016/S2212-5671(14)00248-2
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., & Liljeberg, P. (2018). Exploiting smart e-Health gateways at the edge of healthcare Internet-of-Things: A fog computing approach. Future Generation Computer Systems, 78(2), 641–658. https://doi.org/10.1016/j.future.2017.02.014
Raikhelkar, J., & Raikhelkar, J. K. (2015). The Impact of Telemedicine in Cardiac Critical Care. Critical Care Clinics, 31(2), 305–317. https://doi.org/10.1016/j.ccc.2014.12.008
Rajan, S. P. (2015). Review and investigations on future research directions of mobile based telecare system for cardiac surveillance. Journal of Applied Research and Technology, 13(4), 454–460. https://doi.org/10.1016/j.jart.2015.09.002
Rajkumar, R., & Sriman Narayana Iyengar, N. C. (2013). Dynamic Integration of Mobile JXTA with Cloud Computing for Emergency Rural Public Health Care. Osong Public Health and Research Perspectives, 4(5), 255–264. https://doi.org/10.1016/j.phrp.2013.09.004
Ramesh, A., & Kumar, S. (2010). Triage, monitoring, and treatment of mass casualty events involving chemical, biological, radiological, or nuclear agents. Journal of Pharmacy and Bioallied Sciences, 2(3), 239. https://doi.org/10.4103/0975- 7406.68506
Randell, B., Lee, P., & Treleaven, P. C. (1978). Reliability Issues in Computing System Design. ACM Comput. Surv., 10(2), 123–165. https://doi.org/10.1145/356725.356729
Rekha, R., Mathambigai, T. G., & Vidhyapriya, R. (2012). Secure Medical Data Transmission in Body Area Sensor Networks Using Dynamic Biometrics and Steganography. Bonfring International Journal of Software Engineering and Soft Computing, 2(1), 5–11.
Ren, J., Wu, G., Li, X., Pirozmand, P., & Obaidat, M. S. (2015). Probabilistic response- time analysis for real-time systems in body area sensor networks. International Journal of Communication Systems, 28(16), 6. https://doi.org/10.1002/dac.2990
Renner, A., Williams, R., Afb, W. P., Ganapathy, S., West, J., Weinle, N., … Boswell, L. (2014). RIPPLE : Scalable Medical Telemetry System for Supporting Combat Rescue, 228–232.
Rezaee, A. A., Yaghmaee, M. H., & Rahmani, A. M. (2014). Optimized congestion management protocol for healthcare wireless sensor networks. Wireless Personal Communications, 75(1), 11–34. https://doi.org/10.1007/s11277-013-1337-z
Rezaee, A. A., Yaghmaee, M. H., Rahmani, A. M., & Mohajerzadeh, A. H. (2014). HOCA: Healthcare aware optimized congestion avoidance and control protocol for wireless sensor networks. Journal of Network and Computer Applications, 37(1), 216–228. https://doi.org/10.1016/j.jnca.2013.02.014
Rezvani, S., & Ghorashi, S. A. (2013). Context aware and channel-based resource allocation for wireless body area networks. IET Wireless Sensor Systems, 3(1), 16– 25. https://doi.org/10.1049/iet-wss.2012.0100
RG, R., KL, K., LB, S., K, R., & TR, P. (1984). Mood disorders in stroke patients: Importance of lesion location. Brain, 107(Pt 1), 81–93.
Rocha, A., Martins, A., Freire Junior, J. C., Kamel Boulos, M. N., Vicente, M. E., Feld, R., … Rodriguez-Molinero, A. (2013). Innovations in health care services: the CAALYX system. Int J Med Inform, 82(11), e307-20. https://doi.org/10.1016/j.ijmedinf.2011.03.003
Rodrigues, E. M. G., Godina, R., Cabrita, C. M. P., & Catalão, J. P. S. (2017). Experimental low cost reflective type oximeter for wearable health systems. Biomedical Signal Processing and Control, 31, 419–433. https://doi.org/10.1016/j.bspc.2016.09.013
Rodriguez, D., Heuer, S., Guerra, A., Stork, W., Weber, B., & Eichler, M. (2014). Towards automatic sensor-based triage for individual remote monitoring during mass casualty incidents. In Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 (pp. 544–551). https://doi.org/10.1109/BIBM.2014.6999217
Rojahn, K., Laplante, S., Sloand, J., Main, C., Ibrahim, A., Wild, J., … Johnson, K. I. (2016). Remote monitoring of chronic diseases: A landscape assessment of policies in four European countries. PLoS ONE, 11(5), 1–15. https://doi.org/10.1371/journal.pone.0155738
Ru Kong, Chen, C., Yu, W., Yang, B., & Guan, X. (2013). Data priority based slot allocation for Wireless Body Area Networks. 2013 International Conference on Wireless Communications and Signal Processing, 1–6. https://doi.org/10.1109/WCSP.2013.6677273
Rubio, Ó. J., Trigo, J. D., Alesanco, Á., Serrano, L., & García, J. (2016). Analysis of ISO/IEEE 11073 built-in security and its potential IHE-based extensibility. Journal of Biomedical Informatics, 60, 270–285. https://doi.org/10.1016/j.jbi.2016.02.006
Ryu, S. (2012). Book Review: mHealth: New Horizons for Health through Mobile Technologies: Based on the Findings of the Second Global Survey on eHealth (Global Observatory for eHealth Series, Volume 3). Healthcare Informatics Research, 18(3), 231. https://doi.org/10.4258/hir.2012.18.3.231
Saaty, T. L. (1977). A scaling model for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 213–281. Saaty, T. L., & Ozdemir, M. S. (2003). Why the magic number seven plus or minus two. Mathematical and Computer Modelling, 38(3–4), 233–244.
Safavi, S., & Shukur, Z. (2014). Conceptual privacy framework for health information on wearable device. PLoS ONE, 9(12), e114306. https://doi.org/10.1371/journal.pone.0114306
Sakanushi, K., Hieda, T., Shiraishi, T., Ode, Y., Takeuchi, Y., Imai, M., … Tanaka, H. (2013). Electronic triage system for continuously monitoring casualties at disaster scenes. Journal of Ambient Intelligence and Humanized Computing, 4(5), 547–558. https://doi.org/10.1007/s12652-012-0130-2
Sakr, S., & Elgammal, A. (2016). Towards a Comprehensive Data Analytics Framework for Smart Healthcare Services. Big Data Research, 4(May), 44–58. https://doi.org/10.1016/j.bdr.2016.05.002
Saksrisathaporn, K., Bouras, A., Reeveerakul, N., & Charles, A. (2016). Application of a Decision Model by Using an Integration of AHP and TOPSIS Approaches within Humanitarian Operation Life Cycle. International Journal of Information Technology & Decision Making, 15(04), 887–918. https://doi.org/10.1142/S0219622015500261
Salatge, N., & Fabre, J. C. (2007). Fault tolerance connectors for unreliable Web Services. Proceedings of the International Conference on Dependable Systems and Networks, 51–60. https://doi.org/10.1109/DSN.2007.48
Saleem, K., Derhab, A., Al-Muhtadi, J., & Shahzad, B. (2015). Human-oriented design of secure Machine-to- Machine communication system for e- Healthcare society. Computers in Human Behavior, 51(NOVEMBER), 977–985. https://doi.org/10.1016/j.chb.2014.10.010
SALMAN, O. H. (2014). MULTI SOURCES DATA FUSION FRAMEWORK FOR REMOTE TRIAGE AND PRIOTIRIZATION IN TELEMEDICINE. Universiti Putra Malaysia.
Salman, O. H., Rasid, M. F. A., Saripan, M. I., & Subramaniam, S. K. (2014). Multi- sources data fusion framework for remote triage prioritization in telehealth. Journal of Medical Systems, 38(9), 103. https://doi.org/10.1007/s10916-014-0103-4
Salman, O. H., Zaidan, A. A., Zaidan, B. B., Naserkalid, & Hashim, M. (2017). Novel Methodology for Triage and Prioritizing Using ?Big Data? Patients with Chronic Heart Diseases Through Telemedicine Environmental. International Journal of Information Technology & Decision Making, 16(05), 1211–1245. https://doi.org/10.1142/S0219622017500225
Sanders, T. H., Devergnas, A., Wichmann, T., & Clements, M. a. (2013). Remote smartphone monitoring for management of Parkinson‘s Disease. In Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments - PETRA ?13 (pp. 1–5). ACM. https://doi.org/10.1145/2504335.2504380
Santos, J. R. B. Dos, Blard, G., Oliveira, A. S. R., & Carvalho, N. B. De. Wireless sensor tag and network for improved clinical triage, 31 Proceedings - 18th Euromicro Conference on Digital System Design, DSD 2015 § (2015). https://doi.org/10.1109/DSD.2015.66
Sarkar, P., & Sinha, D. (2014). An Approach to Continuous Pervasive Care of Remote Patients Based on Priority Based Assignment of Nurse. In Lncs (Vol. 8838, pp. 327– 338). Springer. https://doi.org/10.1007/978-3-662-45237-0_31
Sebillo, M., Tortora, G., Tucci, M., Vitiello, G., Ginige, A., & Di Giovanni, P. (2015). Combining personal diaries with territorial intelligence to empower diabetic patients. Journal of Visual Languages and Computing, 29, 1–14. https://doi.org/10.1016/j.jvlc.2015.03.002
Sene, A., Kamsu-Foguem, B., & Rumeau, P. (2015). Telemedicine framework using case-based reasoning with evidences. Computer Methods and Programs in Biomedicine, 121(1), 21–35. https://doi.org/10.1016/j.cmpb.2015.04.012
Sevin, A., Bayilmis, C., & Kirbas, I. (2016). Design and implementation of a new quality of service-aware cross-layer medium access protocol for wireless body area networks. Computers and Electrical Engineering, 56, 145–156. https://doi.org/10.1016/j.compeleceng.2016.02.003
Shah, M. A., Kim, J., Khadra, M. H., & Feng, D. (2014). Home area network for optimizing telehealth services-empirical simulation analysis. In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 (Vol. 2014, pp. 1370–1373). IEEE. https://doi.org/10.1109/EMBC.2014.6943854
Sherekar, V., & Tatikonda, M. (2016). Impact of Factor Affecting on Labour Productivity in Construction Projects by AHP Method. International Journal of Engineering Science and Computing, 6(6), 6771–6775. https://doi.org/10.4010/2016.1619
Shih, D. H., Chiang, H. Sen, Lin, B., & Lin, S. Bin. (2010). An embedded mobile ECG reasoning system for elderly patients. IEEE Transactions on Information Technology in Biomedicine, 14(3), 854–865. https://doi.org/10.1109/TITB.2009.2021065
Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7–8), 801–813. https://doi.org/10.1016/j.mcm.2006.03.023
Shnayder, V., Chen, B., Lorincz, K., Jones, T. R. F. F., & Welsh, M. (2005). Sensor networks for medical care. Proceedings of the 3rd International Conference on Embedded Networked Sensor Systems - SenSys ?05, 314. https://doi.org/10.1145/1098918.1098979
Slotwiner, D., Varma, N., Akar, J. G., Annas, G., Beardsall, M., Fogel, R. I., … Yu, C. M. (2015). HRS expert consensus statement on remote interrogation and monitoring for cardiovascular implantable electronic devices. Heart Rhythm, 12(7), e69–e100. https://doi.org/10.1016/j.hrthm.2015.05.008
Smith, E., & Macdonald, R. (2006). Managing health information during disasters. The HIM Journal, 35(2), 8–13.
Smith, J., Cook, A., & Packer, C. (2010). Evaluation criteria to assess the value of identification sources for horizon scanning. International Journal of Technology Assessment in Health Care, 26(3), 348–353. https://doi.org/10.1017/S026646231000036X
Sneha, S., & Varshney, U. (2013). A framework for enabling patient monitoring via mobile ad hoc network. Decision Support Systems, 55(1), https://doi.org/10.1016/j.dss.2013.01.024
Sockolow, P. S., Bowles, K. H., Adelsberger, M. C., Chittams, J. L., & Liao, C. (2014). Impact of Homecare Electronic Health Record on Timeliness of Clinical Documentation, Reimbursement, and Patient Outcomes. Applied Clinical Informatics, 5(2), 445–462. https://doi.org/10.4338/ACI-2013-12-RA-0106
Soto, J., Queiroz, S., & Nogueira, M. (2012). Managing sensing and cooperation to analyze PUE attacks in Cognitive Radio Ad Hoc Networks. 2012 8Th International Conference on Network and Service Management (Cnsm) and 2012 Workshop on Systems Virtualiztion Management (Svm).
Soufiene, B. O., Bahattab, A. A., Trad, A., & Youssef, H. (2016). Lightweight and confidential data aggregation in healthcare wireless sensor networks. Transactions on Emerging Telecommunications Technologies, 27(4), 576–588. https://doi.org/10.1002/ett.2993
Steele, R., Lo, A., Secombe, C., & Wong, Y. K. (2009). Elderly persons‘ perception and acceptance of using wireless sensor networks to assist healthcare. International Journal of Medical Informatics, 78(12), 788–801. https://doi.org/10.1016/j.ijmedinf.2009.08.001
Sudha, G. F., Karthik, S., & Kumar, N. S. (2014). Activity aware energy efficient priority based multi patient monitoring adaptive system for body sensor networks. Technology and Health Care, 22(2), 167–177. https://doi.org/10.3233/THC-140782
Sugeno, M., fuzzy, T. Y.-I. T. on, & 1993, undefined. (n.d.). A fuzzy-logic-based approach to qualitative modeling. Pdfs.Semanticscholar.Org.
Sung, W. T., & Chang, K. Y. (2014). Health parameter monitoring via a novel wireless system. Applied Soft Computing Journal, 22, 667–680. https://doi.org/10.1016/j.asoc.2014.04.036
T. Takagi and M. Sugeno. (1985). Fuzzy identification of systems and its applications to modeling and control. In EEE Trans. Syst., Man, Cybernatics (Vol. 15, p. 116132). Elsevier.
Takakuwa, K. M., Shofer, F. S., & Abbuhl, S. B. (2007). Strategies for Dealing With Emergency Department Overcrowding: A One-Year Study on How Bedside Registration Affects Patient Throughput Times. Journal of Emergency Medicine, 32(4), 337–342. https://doi.org/10.1016/j.jemermed.2006.07.031
Tamura, T., Maeno, S., Hattori, T., Kimura, Y., Kimura, Y., Yoshida, M., & Minato, K. (2014). Assessment of participant compliance with a Web-based home healthcare system for promoting specific health checkups. Biocybernetics and Biomedical Engineering, 34(1), 63–69. https://doi.org/10.1016/j.bbe.2013.12.001
Tang, D., Yu, J., Chen, X., & Makis, V. (2015). An optimal condition-based maintenance policy for a degrading system subject to the competing risks of soft and hard failure. Computers and Industrial Engineering, 83, 100–110. https://doi.org/10.1016/j.cie.2015.02.003
Tawfik, H., & Anya, O. (2015). Evaluating practice-centered awareness in cross- boundary telehealth decision support systems. Telematics and Informatics, 32(3), 486–503. https://doi.org/10.1016/j.tele.2014.11.002
Taylan, O., Kaya, D., & Demirbas, A. (2016). An integrated multi attribute decision model for energy efficiency processes in petrochemical industry applying fuzzy set theory. Energy Conversion and Management, 117, 501–512. https://doi.org/10.1016/j.enconman.2016.03.048
Tegegne, T., & Van Der Weide, T. P. (2014). Enriching queries with user preferences in healthcare. Information Processing and Management, 50(4), 599–620. https://doi.org/10.1016/j.ipm.2014.03.004
Teijeiro, T., Félix, P., Presedo, J., & Zamarrón, C. (2013). An open platform for the protocolization of home medical supervision. Expert Systems with Applications, 40(7), 2607–2614. https://doi.org/10.1016/j.eswa.2012.11.001
Thokala, P., Devlin, N., Marsh, K., Baltussen, R., Boysen, M., Kalo, Z., … Ijzerman, M. (2016). Multiple criteria decision analysis for health care decision making - An introduction: Report 1 of the ISPOR MCDA Emerging Good Practices Task Force. Value in Health, 19(1), 1–13. https://doi.org/10.1016/j.jval.2015.12.003
Tindale, R. (2006). Paediatric triage tape. Emergency Nurse : The Journal of the RCN Accident and Emergency Nursing Association, 13(9), 6. Retrieved from http://ovidsp.ovid.com/ovidweb.cgi?T=JS&PAGE=reference&D=prem&NEWS=N &AN=27704883
Touati, F., & Tabish, R. (2013). U-healthcare system: State-of-the-art review and challenges. Journal of Medical Systems, 37(3), 9949. https://doi.org/10.1007/s10916-013-9949-0
Traverso, G., Ciccarelli, G., Schwartz, S., Hughes, T., Boettcher, T., Barman, R., … Swiston, A. (2015). Physiologic status monitoring via the gastrointestinal tract. PLoS ONE, 10(11). https://doi.org/10.1371/journal.pone.0141666
Triantaphyllou, E. (2000). Multi-criteria Decision Making Methods: A Comparative Study. In In Multi-criteria decision making methods: A comparative study (Vol. 44, pp. 5–12). Springer. https://doi.org/10.1007/978-1-4757-3157-6
Triantaphyllou, E., Shu, B., Sanchez, S. N., & Ray, T. (1998). Multi-criteria decision making: an operations research approach. Encyclopedia of Electrical and Electronics Engineering, 15(1998), 175–186.
Ullah, F., Khelil, A., Sheikh, A. A., Felemban, E., & Bojan, H. M. A. (2013). Towards automated self-tagging in emergency health cases. In 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013 (pp. 658–663). IEEE. https://doi.org/10.1109/HealthCom.2013.6720758
Urovi, V., del Toro, O. J., Dubosson, F., Torres, A. R., & Schumacher, M. I. (2017). COMPOSE: Using temporal patterns for interpreting wearable sensor data with computer interpretable guidelines. Computers in Biology and Medicine, 81, 24–31. https://doi.org/10.1016/j.compbiomed.2016.11.015
Vaidehi, V., Vardhini, M., Yogeshwaran, H., Inbasagar, G., Bhargavi, R., & Sweetlin Hemalatha, C. (2013). Agent based health monitoring of elderly people in indoor environments using wireless sensor networks. In Procedia Computer Science (Vol. 19, pp. 64–71). https://doi.org/10.1016/j.procs.2013.06.014
Valerie, B., & Stewart, T. J. (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Springer Science & Business Media.
van Dyk, L. (2014). A review of telehealth service implementation frameworks. International Journal of Environmental Research and Public Health, 11(2), 1279– 1298. https://doi.org/10.3390/ijerph110201279
van Til, J. A., Renzenbrink, G. J., Dolan, J. G., & IJzerman, M. J. (2008). The Use of the Analytic Hierarchy Process to Aid Decision Making in Acquired Equinovarus Deformity. Archives of Physical Medicine and Rehabilitation, 89(3), 457–462. https://doi.org/10.1016/j.apmr.2007.09.030
Varshney, U. (2014). A model for improving quality of decisions in mobile health. Decision Support Systems, 62, 66–77. https://doi.org/10.1016/j.dss.2014.03.005
Villalonga, C., Pomares, H., Rojas, I., & Banos, O. (2017). MIMU-Wear: Ontology- based sensor selection for real-world wearable activity recognition. Neurocomputing, 250(2017), 76–100. https://doi.org/10.1016/j.neucom.2016.09.125
Villarreal, V., Fontecha, J., Hervas, R., & Bravo, J. (2014). Mobile and ubiquitous architecture for the medical control of chronic diseases through the use of intelligent devices: Using the architecture for patients with diabetes. Future Generation Computer Systems, 34, 161–175. https://doi.org/10.1016/j.future.2013.12.013
Wang, J. J., Jing, Y. Y., Zhang, C. F., & Zhao, J. H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13(9), 2263–2278. https://doi.org/10.1016/j.rser.2009.06.021
Wang, J., Qiu, M., & Guo, B. (2017). Enabling real-time information service on telehealth system over cloud-based big data platform. Journal of Systems Architecture, 72, 69–79. https://doi.org/10.1016/j.sysarc.2016.05.003
Wang, S., Hu, H., & Kingdom, U. (2012). Wireless Sensor Networks for Underwater Localization : A Survey. Computer Networks, 54(15), 2688–2710.
Wei, L., Lang, C. C., Sullivan, F. M., Boyle, P., Wang, J., Pringle, S. D., & MacDonald, T. M. (2008). Impact on mortality following first acute myocardial infarction of distance between home and hospital: Cohort study. Heart, 94(9), 1141–1146. https://doi.org/10.1136/hrt.2007.123612
Westergren, H., Ferm, M., & Häggström, P. (2014). First evaluation of the paediatric version of the Swedish rapid emergency triage and treatment system shows good reliability. Acta Paediatrica, International Journal of Paediatrics, 103(3), 305–308. https://doi.org/10.1111/apa.12491
Whaiduzzaman, M., Gani, A., Anuar, N. B., Shiraz, M., Haque, M. N., & Haque, I. T. (2014). Cloud service selection using multicriteria decision analysis. TheScientificWorldJournal, 2014, 459375. https://doi.org/10.1155/2014/459375
WHO. (2013). Surgical Care at the District Hospital. World Health Organization.
WHO. (2014). [ HeRAMS ] Health Resources Availability Mapping System Greater Darfur, (June), 52.
Who Organization, W. H. (2011). Disaster risk management for health fact sheets: Disaster risk management for health: Children health. Global Platform-May 2011.
Widgren, B. R., & Jourak, M. (2011). Medical Emergency Triage and Treatment System (METTS): A new protocol in primary triage and secondary priority decision in emergency medicine. Journal of Emergency Medicine, 40(6), 623–628. https://doi.org/10.1016/j.jemermed.2008.04.003
Wind, Y., & Saaty, T. L. (1980). Marketing Applications of the Analytic Hierarchy Process. Management Science, 26(7), 641–658. https://doi.org/10.1287/mnsc.26.7.641
Winkler, S., Schieber, M., Lücke, S., Heinze, P., Schweizer, T., Wegertseder, D., … Koehler, F. (2011). A new telemonitoring system intended for chronic heart failure patients using mobile telephone technology - Feasibility study. International Journal of Cardiology, 153(1), 55–58. https://doi.org/10.1016/j.ijcard.2010.08.038
Wiseman, D. B., Ellenbogen, R., & Shaffrey, C. I. (2002). Triage for the neurosurgeon. Neurosurgical Focus, 12(3), E5. https://doi.org/120305 [pii]
Wizig, L. H. (2004, May). Method and system for providing a user-selected healthcare services package and healthcare services panel customized based on a user‘s selections. Google Patents.
Woo, M. W., Lee, J. W., & Park, K. H. (2018). A reliable IoT system for Personal Healthcare Devices. Future Generation Computer Systems, 78, 626–640. https://doi.org/10.1016/j.future.2017.04.004
Wood, A. (1995). Predicting client/server availability. Computer, 28(4), 41–48.
World Health Organization. (1996). WHO Investing in Health Research and Development. Report of the Ad Hoc committee on health research relating to future intervention options.
Xiang, Y., Liu, Z., Liu, R., Sun, W., & Wang, W. (2013). GeoSVR: A map-based stateless VANET routing. Ad Hoc Networks, 11(7), 2125–2135. https://doi.org/10.1016/j.adhoc.2012.02.015
Xiang, Y., & Zhuang, J. (2016). A medical resource allocation model for serving emergency victims with deteriorating health conditions. Annals of Operations Research, 236(1), 177–196. https://doi.org/10.1007/s10479-014-1716-1
Xiao, Y., & Chen, H. (2008). Mobile telemedicine: a computing and networking perspective. Auerbach Publications.
Yaacoub, E., & Abu-dayya, A. (2012). Multihop Routing for Energy Efficiency in Wireless Sensor Networks. Wireless Sensor Networks - Technology and Protocols. INTECH Open Access Publisher.
Yas, Q. M., Zaidan, A. A., Zaidan, B. B., Rahmatullah, B., & Karim, H. A. (2017). Comprehensive Insights into Evaluation and Benchmarking of Real-time Skin Detectors: Review, Open Issues & Challenges, and Recommended Solutions. Measurement.
Yassen, M. F., & Tarabia, A. M. K. (2017). Transient analysis of Markovian queueing system with balking and reneging subject to catastrophes and server failures. Applied Mathematics and Information Sciences, 11(4), 1041–1047. https://doi.org/10.18576/amis/110410
Yi, C., Alfa, A. S., & Cai, J. (2016). An Incentive-Compatible Mechanism for Transmission Scheduling of Delay-Sensitive Medical Packets in E-Health Networks. IEEE Transactions on Mobile Computing, 15(10), 2424–2436. https://doi.org/10.1109/TMC.2015.2500241
Yi, C., Zhao, Z., Cai, J., Lobato De Faria, R., & Zhang, G. (2016). Priority-aware pricing- based capacity sharing scheme for beyond-wireless body area networks. Computer Networks, 98, 29–43. https://doi.org/10.1016/j.comnet.2016.01.010
Yoon, K., & Hwang, C.-L. (1995). Multiple attribute decision making: an introduction. Sage Publications Thousand Oaks CA (Vol. 104). Sage publications.
Yuan, X., Li, C., Song, Y., Yang, L., & Ullah, S. (2015). On energy-saving in e-healthcare: A directional MAC protocol for WBAN. In 2015 IEEE Globecom Workshops, GC Wkshps 2015 - Proceedings (pp. 1–6). IEEE. https://doi.org/10.1109/GLOCOMW.2015.7414214
Zachariasse, J. M., Seiger, N., Rood, P. P. M., Alves, C. F., Freitas, P., Smit, F. J., … Moll, H. A. (2017). Validity of the Manchester triage system in emergency care: A prospective observational study. PLoS ONE, 12(2), e0170811. https://doi.org/10.1371/journal.pone.0170811
Zaidan, A. A., Karim, H. A., Ahmad, N. N., Zaidan, B. B., & Kiah, M. L. M. (2015). Robust Pornography Classification Solving the Image Size Variation Problem Based on Multi-Agent Learning. Journal of Circuits Systems and Computers, 24(2), 37. https://doi.org/10.1142/s0218126615500231
Zaidan, A. A., Zaidan, B. B., Al-Haiqi, A., Kiah, M. L. M., Hussain, M., & Abdulnabi, M. (2015). Evaluation and selection of open-source EMR software packages based on integrated AHP and TOPSIS. Journal of Biomedical Informatics, 53, 390–404. https://doi.org/10.1016/j.jbi.2014.11.012
Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., Yas, Q. M., & Hashim, M. (2018). A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution. Health and Technology. https://doi.org/10.1007/s12553-018- 0223-9
Zaidan, A. A., Zaidan, B. B., Qahtan, M. Y., Albahri, O. S., Albahri, A. S., Alaa, M., … Lim, C. K. (2018). A survey on communication components for IoT-based technologies in smart homes. Telecommunication Systems, 69(1), 1–25. https://doi.org/10.1007/s11235-018-0430-8
Zane, R. D., & Biddinger, P. (2011). Home Health Patient Assessment Tools: Preparing for Emergency Triage. Abt Associates.
Zanjal, S. V., & Talmale, G. R. (2016). Medicine Reminder and Monitoring System for Secure Health Using IOT. Physics Procedia, 78(December 2015), 471–476. https://doi.org/10.1016/j.procs.2016.02.090
Zarabzadeh, A., O‘Donoghue, J., O‘Connor, Y., O‘Kane, T., Woodworth, S., Gallagher, J., & O‘Connor, S. (2013). Variation in health care providers‘ perceptions: Decision making based on patient vital signs. Journal of Decision Systems, 22(3), 168–189. https://doi.org/10.1080/12460125.2013.817063
Zardari, N. H., Ahmed, K., Shirazi, S. M., & Yusop, Z. Bin. (2015). Weighting Methods and their Effects on Multi-Criteria Decision Making Model Outcomes in Water Resources Management. International Journal of Operarations Research, 10(2), 56–66. https://doi.org/10.1007/978-3-319-12586-2
Zhang, J., Goode, K. M., Cuddihy, P. E., & Cleland, J. G. F. (2009). Predicting hospitalization due to worsening heart failure using daily weight measurement: Analysis of the Trans-European Network-Home-Care Management System (TEN- HMS) study. European Journal of Heart Failure, 11(4), 420–427. https://doi.org/10.1093/eurjhf/hfp033
Zhang, K., Liang, X., Baura, M., Lu, R., & Shen, X. (2014). PHDA: A priority based health data aggregation with privacy preservation for cloud assisted WBANs. Information Sciences, 284, 130–141. https://doi.org/10.1016/j.ins.2014.06.011
Zhang, Z., Buckler, E. S., Casstevens, T. M., & Bradbury, P. J. (2009). Software engineering the mixed model for genome-wide association studies on large samples. Briefings in Bioinformatics (Vol. 10). IEEE Computer Society. https://doi.org/10.1093/bib/bbp050
Zhao, Y., & Kerkhoff, H. G. (2014). Design of an embedded health monitoring infrastructure for accessing multi-processor SoC degradation. In Proceedings - 2014 17th Euromicro Conference on Digital System Design, DSD 2014 (pp. 154–160). IEEE. https://doi.org/10.1109/DSD.2014.80
Zheng, G., Ning, Y., & Wang, M. (2010). Energy efficient geography-based data forwarding algorithm for multi-hop wireless sensor network. Proceedings - International Conference on Electrical and Control Engineering, ICECE 2010. https://doi.org/10.1109/iCECE.2010.1263
Zheng, Z., Zhang, Y., & Lyu, M. R. (2010). Distributed QoS evaluation for real-world Web services. ICWS 2010 - 2010 IEEE 8th International Conference on Web Services, 83–90. https://doi.org/10.1109/ICWS.2010.10
Zhu-juan, W. (2015). Emergency Treatment in Smart Terminal-based E-healthcare Networks, (Iccsnt), 1178–1181.
Zionts, S. (1979). MCDM—If Not a Roman Numeral, Then What? Interfaces, 9(4), 94– 101. https://doi.org/10.1287/inte.9.4.94
Spada, E. J., & Kim, Y. (2018, May 15). Fault tolerant clock network. Google Patents.
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