Smart Healthcare System Design. Группа авторов
Читать онлайн книгу.
to evaluate the health-care and medical problems: A review of three decades of research with recent developments. Expert Syst. Appl., 137, 202–231, 2019.
14. Moya, A., Navarro, E., Jaén, J., González, P., Fuzzy‐description logic for supporting the rehabilitation of the elderly. Expert Systems. 37, 2, e12464, 2020 Apr.
15. Tucan, P., Gherman, B., Major, K., Vaida, C., Major, Z., Plitea, N., Carbone, G., Pisla, D., Fuzzy logic-based risk assessment of a parallel robot for elbow and wrist rehabilitation. Int. J. Environ. Res. Public Health, 17, 654, 2020.
16. Tüzün, S. and Topcu, Y.I., A taxonomy of operations research studies in healthcare management. Oper. Res. Appl. HealthCare Manage., 3–21, 2017.
17. Narayanamurthy, G., Gurumurthy, A., Is the hospital lean? A mathematical model for assessing the implementation of lean thinking in healthcare institutions. Oper. Res. HealthCare, 1, 18, 84–98, 2018 Sep.
18. Suresh, M., Vaishnavi, V., Pai, R.D., Leanness evaluation in healthcare organizations using fuzzy logic approach. Int. J. Org. Anal., 2020.
19. Akram, M., Dudek, W.A., Ilyas, F., Group decision-making based on pythagorean fuzzy TOPSIS method. Int. J. Intell. Syst., 1–21, 2019.
20. Rajabi, F., Jahangiri, M., Bagherifard, F., Banaee, S., Farhadi, P., Strategies for controlling violence against healthcare workers: Application of fuzzy analytical hierarchy process and fuzzy additive ratio assessment. J. Nurs. Manage., 28, 4, 777–786, 2020 May.
21. Garai, A., Roy, TK., Multi-objective optimization of cost-effective and customercentric closed-loop supply chain management model in T-environment. Soft Comput., 24, 1, 155–178, 2020 Jan.
22. Maghsoodi, A.I., Mosavat, M., Hafezalkotob, A., Hafezalkotob, A., Hybrid hierarchical fuzzy group decision-making based on information axioms and BWM: Prototype design selection. Comput. Ind. Eng., 1, 127, 788–804, 2019 Jan.
23. Ozsahin, I., Sharif, T., Ozsahin, D.U., Uzun, B., Evaluation of solid-state detectors in medical imaging with fuzzy PROMETHEE. J. Instrum., 14, 01, C01019, 2019 Jan.
24. Moreno-Cabezali, B.M., Fernandez-Crehuet, J.M., Application of a fuzzy-logic based model for risk assessment in additive manufacturing R&D projects. Comput. Ind. Eng., 1, 145, 106529, 2020 Jul.
25. AlZu’bi, S., Shehab, M., Al-Ayyoub, M., Jararweh, Y., Gupta, B., Parallel implementation for 3d medical volume fuzzy segmentation. Pattern Recognit. Lett. 1;130:312-8.
26. La Fata, C.M., Lupo, T., Piazza, T., Service quality benchmarking via a novel approach based on fuzzy ELECTRE III and IPA: an empirical case involving the Italian public healthcare context. HealthCare Manag Sci., 22, 1, 106–120, 2019 Mar.
27. Samiei, S., Pourbabaki, R., Risk factors of low back pain using adaptive neuro-fuzzy. Archieves Occup. Health, 3, 2, 339–345, 2019 Apr.
28. Akinnuwesi BA, Adegbite BA, Adelowo F, Ima-Edomwonyi U, Fashoto G, Amumeji OT. Decision support system for diagnosing Rheumatic-Musculoskeletal disease using fuzzy cognitive MAP technique. Informatics in Medicine Unlocked. 1, 18, 100279, 2020 Jan.
29. Vidhya, K. and Shanmugalakshmi, R., Modified adaptive neuro-fuzzy inference system (M-ANFIS) based multi-disease analysis of healthcare big data. J. Supercomput., 2020.
30. Vaishnavi, V. and Suresh, M., Assessing the readiness level of healthcare for implementing agility using fuzzy logic approach. Global J. Flexible Syst. Manage., 2020.
31. Sumrit, D., Supplier selection for vendor-managed inventory in healthcare using fuzzy multi-criteria decision-making approach. Decis. Sci. Lett., 9, 2, 233–256, 2020.
32. Salazar, E.M. and Sanz-Calcedo, J.G., Study on the influence of maintenance operations on energy consumption and emissions in healthcare centres by fuzzy cognitive maps. J. Build. Perform. Simul., 2018.
33. Rajput, N., Pandey, R.K., Singh, A.P., Chauhan, A., An optimization of fuzzy EOQ model in healthcare industries with three different demand pattern using signed distance technique. Math. Eng. Sci. Aerosp., 10, 2, 2019.
34. Priyan, S. and Uthayakumar, R., Economic design of an inventory system involving probabilistic deterioration and variable setup cost through mathematical approach. Int. J. Math. Oper. Res., 8, 3, 2016.
35. Pascual-Figal, D., Wachter, R., Senni, M., Belohlavek, J., Noè, A., Carr, D., Butylin, D., Rationale and design of TRANSITION: A randomized trial of pre-discharge vs. post-discharge initiation of sacubitril/valsartan. ESC Heart Failure, 5, 2, 327–336, 2018.
36. Wachter, R., Senni, M., Belohlavek, J., Straburzynska-Migaj, E., Witte, K.K., Kobalava, Z., Chaaban, S., Initiation of sacubitril/valsartan in haemodynamically stabilised heart failure patients in hospital or early after discharge: Primary results of the randomised TRANSITION study. Eur. J. Heart Failure, 21, 8, 998–1007, 2019.
37. Barker, L.R., Curriculum for ambulatory care training in medical residency.
J. Gener. Internal Med., 5, 1, S3–S14, 1990.
38. Shah, R., Melvin, L., Cavalcanti, R.B., EPAs for the Ambulatory Internist in Translation: Findings from a Canadian Multi-Center Survey. Can. J. Gen. Internal Med., 14, 3, 9–15, 2019.
39. Armstrong, K.A., Coyte, P.C., Brown, M., Beber, B., Semple, J.L., Effect of home monitoring via mobile app on the number of in-person visits following ambulatory surgery: A randomized clinical trial. JAMA Surgery, 152, 7, 622–627, 2017.
40. Warrington, L., Absolom, K., Conner, M., Kellar, I., Clayton, B., Ayres, M., Velikova, G., Electronic Systems for Patients to report and manage side effects of Cancer treatment: Systematic review. J. Med. Internet Res., 21, 1, e10875, 2019.
41. Pramanik, M.I., Lau, R.Y., Demirkan, H., Azad, M.A.K., Smart health: Big data enabled health paradigm within smart cities. Expert Syst. Appl., 87, 370–383, 2017.
42. López-Torres, S., López-Torres, H., Rocha-Rocha, J., Butt, S.A., Tariq, M.I., Collazos-Morales, C., Piñeres-Espitia, G., IoT Monitoring of Water Consumption for Irrigation Systems Using SEMMA Methodology. International Conference on Intelligent Human Computer Interaction, Springer, Cham, pp. 222–234, 2019, December.
43. Wang, X., White, L., Chen, X., Gao, Y., Li, H., Luo, Y., An empirical study of wearable technology acceptance in healthcare. Ind. Manage. Data Syst., 2015.
44. Tariq, M.I., Tayyaba, S., Ali Mian, N., Sarfraz, M.S., De-la-Hoz-Franco, E., Butt, S.A., Rad, D.V., Combination of AHP and TOPSIS methods for the ranking of information security controls to overcome its obstructions under fuzzy environment. J. Intell. Fuzzy Syst., (Preprint). 1–14, 2020.
45. Martinez, D., Talbert, T., Romero-Steiner, S., Kosmos, C., Redd, S., Evolution of the public health preparedness and response capability standards to support public health emergency management practices and processes. Health Secur., 17, 6, 430–438, 2019.
46. Shapiro, J.S., Crowley, D., Hoxhaj, S., Langabeer II, J., Panik, B., Taylor, T.B., Nielson, J.A., Health information exchange in emergency medicine. Ann. Emergency Med., 67, 2, 216–226, 2016.
47. Jeong, P.S. and Cho, Y.H., Emergency Support System using Smart Device. J. Korea Inst. Inf. Commun. Eng., 20, 9, 1791–1798, 2016.
48. Jiang, J. and Cameron, A.F., IT-Enabled Self-Monitoring for Chronic Disease Self-Management: An Interdisciplinary Review. MIS Quarterly, 44, 1, 2020.
49. Dudakiya, S., Galani, H., Shaikh, A., Thanki, D., Late, R.A., Pawar, S.E., Monitoring mobile patients using predictive analysis by data from wearable sensors. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), IEEE, pp. 332–335, 2016, March.
50. Rezaeibagha,