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Dergiler / Artificial Intelligence Theory and Applications / 2026 / Volume: 2 Issue: 1

Artificial Intelligence in Healthcare Industry: A Transformation From Model-Driven to Knowledge-Driven DSS

Abdulkadir Hızıroğlu(IZMIR BAKIRCAY UNIVERSITY)0000-0003-4582-3732
Ali Pişirgen(IZMIR BAKIRCAY UNIVERSITY)0000-0001-7257-2938
Mert Özcan(IZMIR BAKIRCAY UNIVERSITY)0000-0002-6083-1813
Halil Kemal İlter(IZMIR BAKIRCAY UNIVERSITY)0000-0002-6359-9976
Sayfa
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Yayın Tarihi
2022-04-30
DOI
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Overall, this study indicates a present course for DSS and offers useful knowledge for both scholars and professionals in the healthcare domain.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Healthcare professionals and inter (or multi) disciplinary academia have been paying more attention to decision support systems (DSS) for improved decision making during their health service processes or management, as well as clinical practices.
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Temel Bulgular & Veri:Although there have been numerous DSS applications in the healthcare industry, it has been intended to provide a categorical snapshot view of current implementations or academic work at specific DSS types for better understanding the application domains by addressing the gap in the literature.
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Sonuç & Katkı:Overall, this study indicates a present course for DSS and offers useful knowledge for both scholars and professionals in the healthcare domain.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:decision support systems · model-driven DSS · knowledge-driven DSS

Abstract

Healthcare professionals and inter (or multi) disciplinary academia have been paying more attention to decision support systems (DSS) for improved decision making during their health service processes or management, as well as clinical practices. Although there have been numerous DSS applications in the healthcare industry, it has been intended to provide a categorical snapshot view of current implementations or academic work at specific DSS types for better understanding the application domains by addressing the gap in the literature. To achieve this, it has been focused on DSS applications in healthcare specifically by concentrating on two main types: model-driven and knowledge-driven. In this context, relevant information systems and medical science literatures were reviewed. For health service problems like hospital placement decisions and homecare route planning, model-driven DSS applications are used for optimization and modelling. Both conventional operations research techniques like optimization, decision analysis, simulation, and multi-criteria decision making, as well as contemporary ones like heuristic search, benefit from these applications. In addition, artificial intelligence techniques help health decision makers via knowledge-driven DSS applications, specifically clinical decision support systems (CDSS). Artificial intelligence applications can also assist health professionals in enhancing their decision-making abilities by incorporating complex operational rules and developing such procedures as single-or multi-agent systems. This research focuses on what to emphasis on while designing a DSS in the healthcare setting, such as which programming or modelling languages to employ and how to transform a model-driven DSS into a knowledgedriven DSS, or how to create the DSS more intelligent. Overall, this study indicates a present course for DSS and offers useful knowledge for both scholars and professionals in the healthcare domain.

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Bu Makaleye Atıf Yap

Hızıroğlu, A., Pişirgen, A., Özcan, M., İlter, H. K. (2022). Artificial Intelligence in Healthcare Industry: A Transformation From Model-Driven to Knowledge-Driven DSS. Artificial Intelligence Theory and Applications, Volume: 2 Issue: 1. https://ajindex.com/articles/1609636

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Kaynakça (82)

  1. Rong, G., Mendez, A., Bou Assi, E., Zhao, B., & Sawan, M. (2020). Artificial Intelligence in Healthcare: Review and Prediction Case Studies. In Engineering (Vol. 6, Issue 3, pp. 291–301). Elsevier Ltd. https://doi.org/10.1016/j.eng.2019.08.015
  2. Eldabi, T., Jun, G. T., Clarkson, J., Connell, C., & Klein, J. H. (2010). Model driven healthcare: Disconnected practices. Proceedings of the 2010 Winter Simulation Conference, 2271–2282. https://doi.org/10.1109/WSC.2010.5678925
  3. Shahmoradi, L., Safdari, R., Ahmadi, H., & Zahmatkeshan, M. (2021). Clinical decision support systemsbased interventions to improve medication outcomes: A systematic literature review on features and effects. Medical Journal of The Islamic Republic of Iran, 35(27). https://doi.org/10.47176/mjiri.35.27Ansen, David (1986). “Tall Tale: Lord of the Mosquitoes.” (Mosquito Coast) Newsweek 106 (Dec 1).
  4. Power, D. J., & Sharda, R. (2007). Model-driven decision support systems: Concepts and research directions. Decision Support Systems, 43(3), 1044–1061. /https://doi.org/10.1016/j.dss.2005.05.030
  5. Kavitha, P. T., & Sasipraba, T. (2012). Knowledge Driven HealthCare Decision Support System using Distributed Data Mining. Asian Journal of Computer Science and Technology (AJCST), 1(2), 24–27.
  6. Moreira, M. W. L., Rodrigues, J. J. P. C., Korotaev, V., Al-Muhtadi, J., & Kumar, N. (2019). A Comprehensive Review on Smart Decision Support Systems for Health Care. IEEE Systems Journal, 13(3), 3536–3545. https://doi.org/10.1109/JSYST.2018.2890121
  7. Gupta, S., Modgil, S., Bhattacharyya, S., & Bose, I. (2022). Artificial intelligence for decision support systems in the field of operations research: review and future scope of research. Annals of Operations Research, 308(1–2), 215–274.https://doi.org/10.1007/s10479-020-03856-6
  8. Demirer Ö., Alkan R.M., “The Usage of Operational Research for Engineering Applications as an Interdisciplinary Approach” Electronic Journal of Map Technologies, 2015, 7(1), 37-46, doi: 10.15659/hartek.15.04.88
  9. Hall, D. J. (2008). Decision Makers and Their Need for Support Dianne. In P. Bernus, J. Blazewicz, G. Schmidt, & M. Shaw (Eds.), International Handbook on Information Systems (pp. 83–103). SpringerVerlag Berlin Heidelberg.
  10. Camacho, J., Zanoletti-Mannello, M., Landis-Lewis, Z., Kane-Gill, S. L., & Boyce, R. D. (2020). A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping. J Med Internet Res, 22(8), e18388. https://doi.org/10.2196/18388
  11. El Morr, C., & Ali-Hassan, H. (2019). Analytics in Healthcare: A Practical Introduction. Springer.
  12. Power, D. J. (2008). Decision Support Systems: A Historical Overview. In P. Bernus, J. Blazewicz, G. Schmidt, & M. Shaw (Eds.), International Handbook on Information Systems (pp. 121–140). SpringerVerlag Berlin Heidelberg. https://doi.org/10.1007/978-3-540-48713-5
  13. Arnott, D. (2004). Decision support systems evolution: framework, case study and research agenda. European Journal of Information Systems, 13(4), 247–259.
  14. Power, D. J. (2004). Specifying An Expanded Framework for Classifying and DescribingDecision Support Systems. Communications of the Association for Information Systems, 13(1), 13.
  15. Alter, S. L. (1980). Decision Support Systems: Current Practice and Continuing Challenges. AddisonWesley Publishing Co.
  16. Bonczek, R. H., Holsapple, C. W., & Whinston, A. B. (1981). A Generalized Decision Support System Using Predicate Calculus and Network Data Base Management. Operations Research, 29(2), 263–281. https://doi.org/10.1287/opre.29.2.263
  17. Klein, M. R., & Methlie, L. B. (1995). Knowledge-Based Decision Support Systems With Applications in Business. Wiley.
  18. Holsapple, C. W., & Andrew B., W. (1996). Decision Support Systems: A Knowledge Based Approach. West Group.
  19. Power, D. J. (2002). A Brief History of Decision Support Systems. http://dssresources.com/history/dsshistoryv28.html
  20. Dios, M., Molina-Pariente, J. M., Fernandez-Viagas, V., Andrade-Pineda, J. L., & Framinan, J. M. (2015). A Decision Support System for Operating Room scheduling. Computers & Industrial Engineering, 88, 430–443. https://doi.org/https://doi.org/10.1016/j.cie.2015.08.001
  21. Djitog, I., Aliyu, H. O., & Traoré, M. K. (2018). A model-driven framework for multi-paradigm modeling and holistic simulation of healthcare systems. SIMULATION, 94(3), 235–257. https://doi.org/10.1177/0037549717744888
  22. Kalton, A., Falconer, E., Docherty, J., Alevras, D., Brann, D., & Johnson, K. (2015). Multi-Agent-Based Simulation of a Complex Ecosystem of Mental Health Care. Journal of Medical Systems, 40(2), 39. https://doi.org/10.1007/s10916-015-0374-4
  23. Kim, D.-G., & Kim, Y.-D. (2013). A Lagrangian heuristic algorithm for a public healthcare facility location problem. Annals of Operations Research, 206(1), 221–240. https://doi.org/10.1007/s10479-013-1378-4
  24. Mardani, A., Hooker, R. E., Ozkul, S., Yifan, S., Nilashi, M., Sabzi, H. Z., & Fei, G. C. (2019). Application of decision making and fuzzy sets theory to evaluate the healthcare and medical problems: A review of three decades of research with recent developments. Expert Systems with Applications, 137, 202–231. https://doi.org/https://doi.org/10.1016/j.eswa.2019.07.002
  25. Bashiri, A., Ghazisaeedi, M., Safdari, R., Shahmoradi, L., & Ehtesham, H. (2017). Improving the prediction of survival in cancer patients by using machine learning techniques: experience of gene expression data: a narrative review. Iranian Journal of Public Health, 46(2), 165.
  26. Hamedan, F., Orooji, A., Sanadgol, H., & Sheikhtaheri, A. (2020). Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach. International Journal of Medical Informatics, 138, 104134. https://doi.org/https://doi.org/10.1016/j.ijmedinf.2020.104134
  27. Nadarzynski, T., Miles, O., Cowie, A., & Ridge, D. (2019). Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. DIGITAL HEALTH, 5, 2055207619871808. https://doi.org/10.1177/2055207619871808
  28. Shillan, D., Sterne, J. A. C., Champneys, A., & Gibbison, B. (2019). Use of machine learning to analyse routinely collected intensive care unit data: A systematic review. Critical Care, 23(1), 1–11 https://doi.org/10.1186/s13054-019-2564-9
  29. Keefer, D. L., Kirkwood, C. W., & Corner, J. L. (2004). Perspective on Decision Analysis Applications, 1990–2001. Decision Analysis, 1(1), 4–22. https://doi.org/10.1287/deca.1030.0004
  30. Zarkogianni, K., Litsa, E., Mitsis, K., Wu, P. Y., Kaddi, C. D., Cheng, C. W., Wang, M. D., & Nikita, K. S. (2015). A Review of Emerging Technologies for the Management of Diabetes Mellitus. IEEE Transactions on Biomedical Engineering, 62(12), 2735–2749. https://doi.org/10.1109/TBME.2015.2470521
  31. Andriopoulou, F., Orphanoudakis, T., & Dagiuklas, T. (2017). IoTA: IoT automated SIP-based emergency call triggering system for general eHealth purposes. International Conference on Wireless and Mobile Computing, Networking and Communications, 2017-Octob, 362–369. https://doi.org/10.1109/WiMOB.2017.8115830
  32. Nair, B. G., Newman, S. F., Peterson, G. N., & Schwid, H. A. (2013). Smart anesthesia manager TM(SAM)-a real-time decision support system for anesthesia care during surgery. IEEE Transactions on Biomedical Engineering, 60(1), 207–210. https://doi.org/10.1109/TBME.2012.2205384
  33. Ağaç, G., & Baki, B. (2016). Application of Multi-Criteria Decision Making in Health Care Field: A Literature Review. Hacettepe Journal of Health Administration, 19(3), 343–363.
  34. Broekhuizen, H., Groothuis-Oudshoorn, C. G. M., van Til, J. A., Hummel, J. M., & IJzerman, M. J. (2015). A review and classification of approaches for dealing with uncertainty in multi-criteria decision analysis for healthcare decisions. Pharmacoeconomics, 33(5), 445–455.
  35. Nazari, S., Fallah, M., Kazemipoor, H., & Salehipour, A. (2018). A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Systems with Applications, 95, 261–271. https://doi.org/https://doi.org/10.1016/j.eswa.2017.11.001
  36. Thomas, L. L., Goni, I., & Emeje, G. D. (2019). Fuzzy Models Applied to Medical Diagnosis: A Systematic Review. Advances in Networks, 7(2), 45.
  37. Chang, N.-B., & Wang, S. F. (1996). The development of an environmental Decision Support System or municipal solid waste management. Computers, Environment and Urban Systems, 20(3), 201–212. https://doi.org/https://doi.org/10.1016/S0198-9715(96)00015-4
  38. Batur, G. D., & Erol, S. (2018). Operations research in healthcare systems: literature review of years 2007-2017. Pamukkale University Journal of Engineering Sciences, 24(1), 153–166. https://doi.org/10.5505/pajes.2017.44389
  39. Geçici, E., & Güler, M. G. (2020). A decision support system for nurse scheduling problem. Pamukkale University Journal of Engineering Sciences, 26(4), 749–757. https://doi.org/10.5505/pajes.2019.86402
  40. Lin, S., & Kernighan, B. W. (1973). An Effective Heuristic Algorithm for the Traveling-Salesman Problem. Operations Research, 21(2), 498–516. https://doi.org/10.1287/opre.21.2.498
  41. Kuo, Y. H., Leung, J. M. Y., Meng, H. M., & Tsoi, K. K. F. (2015). A Real-Time Decision Support Tool for Disaster Response: A Mathematical Programming Approach. Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015, 639–642. https://doi.org/10.1109/BigDataCongress.2015.98
  42. Laesanklang, W., & Landa-Silva, D. (2017). Decomposition techniques with mixed integer programming and heuristics for home healthcare planning. Annals of Operations Research, 256(1), 93–127. https://doi.org/10.1007/s10479-016-2352-8
  43. Fathollahi-Fard, A. M., Hajiaghaei-Keshteli, M., & Mirjalili, S. (2020). A set of efficient heuristics for a home healthcare problem. Neural Computing and Applications, 32(10), 6185–6205.
  44. Katsaliaki, K., & Mustafee, N. (2011). Applications of simulation within the healthcare context. Journal of the Operational Research Society, 62(8), 1431–1451. https://doi.org/10.1057/jors.2010.20
  45. Huang, E. S., Zhang, Q., Brown, S. E. S., Drum, M. L., Meltzer, D. O., & Chin, M. H. (2007). The CostEffectiveness of Improving Diabetes Care in U.S. Federally Qualified Community Health Centers. Health Services Research, 42(6p1), 2174–2193. https://doi.org/https://doi.org/10.1111/j.1475-6773.2007.00734.x
  46. Roze, S., Liens, D., Palmer, A., Berger, W., Tucker, D., & Renaudin, C. (2006). A health economic model to determine the long-term costs and clinical outcomes of raising low HDL-cholesterol in the prevention of coronary heart disease. Current Medical Research and Opinion, 22(12), 2549–2556. https://doi.org/10.1185/030079906X148490
  47. Bagni, R., Berchi, R., & Cariello, P. (2002). A comparison of simulation models applied to epidemics. Journal of Artificial Societies and Social Simulation, 5(3).
  48. Abbott, R. G., Forrest, S., & Pienta, K. J. (2006). Simulating the Hallmarks of Cancer. Artificial Life, 12(4), 617–634. https://doi.org/10.1162/artl.2006.12.4.617
  49. Liu, F., Enanoria, W. T. A., Zipprich, J., Blumberg, S., Harriman, K., Ackley, S. F., Wheaton, W. D., Allpress, J. L., & Porco, T. C. (2015). The role of vaccination coverage, individual behaviors, and the public health response in the control of measles epidemics: an agent-based simulation for California. BMC Public Health, 15(1), 447. https://doi.org/10.1186/s12889-015-1766-6
  50. Cabrera, E., Taboada, M., Iglesias, M. L., Epelde, F., & Luque, E. (2011). Optimization of Healthcare Emergency Departments by Agent-Based Simulation. Procedia Computer Science, 4, 1880–1889. https://doi.org/https://doi.org/10.1016/j.procs.2011.04.204
  51. Peng, Q., Niu, Q., Xie, Y., & Elmekkawy, T. (2009). Operating Room Simulation and Agent-Based Optimization Improvement. In Multi-Agent Systems for Healthcare Simulation and Modeling: Applications for System Improvement (pp. 69–89). IGI Global.
  52. Lee, S. M., & Hong, S. (2002). An enterprise‐wide knowledge management system infrastructure. Industrial Management & Data Systems, 102(1), 17–25. https://doi.org/10.1108/02635570210414622
  53. Hall, D. J., & Paradice, D. (2005). Philosophical foundations for a learning-oriented knowledge management system for decision support. Decision Support Systems, 39(3), 445–461. https://doi.org/https://doi.org/10.1016/j.dss.2004.01.005
  54. Alalwan, J. A., Thomas, M. A., & Weistroffer, H. R. (2014). Decision support capabilities of enterprise content management systems: An empirical investigation. Decision Support Systems, 68, 39–48. https://doi.org/https://doi.org/10.1016/j.dss.2014.09.002
  55. Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–98. https://doi.org/10.7861/futurehosp.6-2-94
  56. Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., & Coiera, E. (2018). Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association, 25(9), 1248–1258. https://doi.org/10.1093/jamia/ocy072
  57. Bharti, U., Bajaj, D., Batra, H., Lalit, S., Lalit, S., & Gangwani, A. (2020). Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19. 2020 5th International Conference on Communication and Electronics Systems (ICCES), 870–875. https://doi.org/10.1109/ICCES48766.2020.9137944
  58. Ng, G., Tan, N., Bahadin, J., Shum, E., & Tan, S. W. (2016). Development of an Automated Healthcare Kiosk for the Management of Chronic Disease Patients in the Primary Care Setting. Journal of Medical Systems, 40(7), 169. https://doi.org/10.1007/s10916-016-0529-y
  59. Callahan, A., & Shah, N. H. (2017). Machine Learning in Healthcare. In A. Sheikh, K. M. Cresswell, A. Wright, & D. W. B. T.-K. A. in C. I. Bates (Eds.), Key Advances in Clinical Informatics: Transforming Health Care Through Health Information Technology (pp. 279–291). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-809523-2.00019-4
  60. Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3), 1–21. https://doi.org/10.1007/s42979-021-00592-x
  61. Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V, Brink, J. A., & Dreyer, K. J. (2018). Current Applications and Future Impact of Machine Learning in Radiology. Radiology, 288(2), 318–328. https://doi.org/10.1148/radiol.2018171820
  62. Gartner, D., & Padman, R. (2020). Machine learning for healthcare behavioural OR: Addressing waiting time perceptions in emergency care. Journal of the Operational Research Society, 71(7), 1087–1101. https://doi.org/10.1080/01605682.2019.1571005
  63. McCauley, N., & Ala, M. (1992). The use of expert systems in the healthcare industry. Information & Management, 22(4), 227–235. https://doi.org/https://doi.org/10.1016/0378-7206(92)90025-B
  64. Samarghitean, C., & Vihinen, M. (2008). Medical expert systems. Current Bioinformatics, 3(1), 56–65. https://doi.org/10.1007/s00521-019-04126-8
  65. Kunz, H., & Schaaf, T. (2011). General and specific formalization approach for a Balanced Scorecard: An expert system with application in health care. Expert Systems with Applications, 38(3), 1947–1955. https://doi.org/https://doi.org/10.1016/j.eswa.2010.07.127
  66. Abu-Nasser, B. (2017). Medical expert systems survey. International Journal of Engineering and Information Systems (IJEAIS), 1(7), 218–224.
  67. Saibene, A., Assale, M., & Giltri, M. (2021). Expert systems: Definitions, advantages and issues in medical field applications. Expert Systems with Applications, 177. https://doi.org/10.1016/j.eswa.2021.114900
  68. Neshat, M., Sargolzaei, M., Nadjaran Toosi, A., & Masoumi, A. (2012).Hepatitis disease diagnosis using hybrid case based reasoning and particle swarm optimization. ISRN Artificial Intelligence, 2012.
  69. Subanya, B., & Rajalaxmi, R. R. (2014). Artificial bee colony based feature selection for effective cardiovascular disease diagnosis. International Journal of Scientific & Engineering Research, 5(5), 606- 612.
  70. Dheeba, J., Singh, N. A., & Selvi, S. T. (2014). Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. Journal of biomedical informatics, 49, 45-52.
  71. Vijay, V., Kavitha, A. R., & Rebecca, S. R. (2016). Automated brain tumor segmentation and detection in MRI using enhanced Darwinian particle swarm optimization (EDPSO). Procedia Computer Science, 92, 475-480.
  72. Sawhney, R., Mathur, P., & Shankar, R. (2018, May). A firefly algorithm based wrapper-penalty feature selection method for cancer diagnosis. In International Conference on Computational Science and Its Applications (pp. 438-449).
  73. Rashid, T., & Abdullah, S. (2018). A Hybrid of Artificial Bee Colony, Genetic Algorithm, and Neural Network for Diabetic Mellitus Diagnosing. ARO-The Scientific Journal of Koya University, 6(1), 55-64.
  74. Holsapple, C. W. (2008). DSS Architecture and Types. In P. Bernus, J. Blazewicz, G. Schmidt, & M. Shaw (Eds.), International Handbook on Information Systems (pp. 163–180). Springer-Verlag Berlin Heidelberg.
  75. Bolton, L. B., Gassert, C. A., & Cipriano, P. F. (2008). Technology solutions can make nursing care safer and more efficient. Journal of Healthcare Information Management: JHIM, 22(4), 24–30.
  76. Shojania, K. G., Jennings, A., Mayhew, A., Ramsay, C., Eccles, M., & Grimshaw, J. (2010). Effect of point-of-care computer reminders on physician behaviour: a systematic review. Canadian Medical Association Journal, 182(5), E216 LP-E225. https://doi.org/10.1503/cmaj.090578
  77. Yuan, M. J., Finley, G. M., Long, J., Mills, C., & Johnson, R. K. (2013). Evaluation of User Interface and Workflow Design of a Bedside Nursing Clinical Decision Support System. Interact J Med Res, 2(1), e4. https://doi.org/10.2196/ijmr.2402
  78. Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: management, analysis and future prospects. Journal of Big Data, 6(1), 54. https://doi.org/10.1186/s40537-019-0217-0
  79. Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3(1), 160035. https://doi.org/10.1038/sdata.2016.35
  80. Abouelmehdi, K., Beni-Hssane, A., Khaloufi, H., & Saadi, M. (2017). Big data security and privacy in healthcare: A Review. Procedia Computer Science, 113, 73–80. https://doi.org/https://doi.org/10.1016/j.procs.2017.08.292
  81. Miller, K., Mosby, D., Capan, M., Kowalski, R., Ratwani, R., Noaiseh, Y., Kraft, R., Schwartz, S., Weintraub, W. S., & Arnold, R. (2018). Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. Journal of the American Medical Informatics Association, 25(5), 585–592. https://doi.org/10.1093/jamia/ocx118
  82. Callahan, A., & Shah, N. H. (2017). Machine Learning in Healthcare. In A. Sheikh, K. M. Cresswell, A. Wright, & D. W. B. T.-K. A. in C. I. Bates (Eds.), Key Advances in Clinical Informatics: Transforming Health Care Through Health Information Technology (pp. 279–291). Academic Press. https://doi.org/https://doi.org/10.1016/B978-0-12-809523-2.00019-4