cirpublications.com-FLTV-1790899046-y806577347 1790899046 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Health Informatics and Digital Systems J. Health Inform. Digit. Syst. 3149-8973 2023 3 2 Chinedu Okafor Amina Bello 2023 10.68159/y806577347 https://cirpublications.com/pub/journal/2/article/y806577347 Veldhuis LI, Woittiez NJC, Ridderikhof ML, de Jonge E, Abu-Hanna A. Artificial intelligence for the prediction of in-hospital clinical deterioration: a systematic review. Crit Care Explor. 2022;4(9):e0744. 10.1097/CCE.0000000000000744 Peelen RV, Eddahchouri Y, van Goor H, van Klundert J, van der Sluijs AF, van der Voort PH. Algorithms for prediction of clinical deterioration on the general wards: a scoping review. J Hosp Med. 2021;16(4):225-30. 10.12788/jhm.3563 Holdsworth LM, Kling SMR, Smith M, Rothman MJ, Edelson DP. Predicting and responding to clinical deterioration in hospitalized patients by using artificial intelligence: protocol for a mixed methods, stepped wedge study. JMIR Res Protoc. 2021;10(7):e27532. 10.2196/27532 Cho KJ, Kwon O, Kwon JM, Lee Y, Park H, Jeon KN, et al. Detecting patient deterioration using artificial intelligence in a rapid response system. Crit Care Med. 2020;48(4):e285-e289. 10.1097/CCM.0000000000004236 Al-Shwaheen TI, Moghbel M, Hau YW, Sowtali SN. Use of learning approaches to predict clinical deterioration in patients based on various variables: a review of the literature. Artif Intell Life Sci. 2022;2:100038. 10.1016/j.ailsci.2022.100038 Mann KD, Good NM, Fatehi F, Khanna S, Campbell C, Doolan C. Predicting patient deterioration: a review of tools in the digital hospital setting. J Med Internet Res. 2021;23(9):e28209. 10.2196/28209 Jahandideh S, Ozavci G, Sahle BW, Kouzani AZ. Evaluation of machine learning-based models for prediction of clinical deterioration: a systematic literature review. Int J Med Inform. 2023;175:105084. 10.1016/j.ijmedinf.2023.105084 Muralitharan S, Nelson W, Di S, McGillion M, Devereaux PJ, Barr ML, et al. Machine learning-based early warning systems for clinical deterioration: systematic scoping review. J Med Internet Res. 2021;23(2):e25187. 10.2196/25187 Shamout FE, Shen Y, Wu N, Kaku A, Park J, Makino T, et al. An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department. npj Digit Med. 2021;4(1):80. 10.1038/s41746-021-00453-0 Gerry S, Bonnici T, Birks J, Kirtley S, Virdee PS, Watkinson PJ, et al. Early warning scores for detecting deterioration in adult hospital patients: systematic review and critical appraisal of methodology. BMJ. 2020;369:m1501. 10.1136/bmj.m1501 Peng J, Zou K, Zhou M, Teng Y, Zhu X, Zhang F, et al. An explainable artificial intelligence framework for the deterioration risk prediction of hepatitis patients. J Med Syst. 2021;45(5):61. 10.1007/s10916-021-01736-5 Escobar GJ, Liu VX, Schuler A, Lawson B, Greene JD, Kipnis P. Automated identification of adults at risk for in-hospital clinical deterioration. N Engl J Med. 2020;383(20):1951-60. 10.1056/NEJMsa2001090 Rohmetra H, Raghunath N, Narang P, Chamola V, Guizani M. AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges. Computing. 2023;105(4):733-61. 10.1007/s00607-021-00937-7 Yelne S, Chaudhary M, Dod K, Sayyad A, Sharma R. Harnessing the power of AI: a comprehensive review of its impact and challenges in nursing science and healthcare. Cureus. 2023;15(11):e49252. 10.7759/cureus.49252 Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emerg Med Australas. 2018;30(6):870-4. 10.1111/1742-6723.13145 Shaik T, Tao X, Higgins N, Gururajan R, Li Y, Zhou X, et al. FedStack: personalized activity monitoring using stacked federated learning. Knowl Based Syst. 2022;257:109929. 10.1016/j.knosys.2022.109929 Bates DW, Levine D, Syrowatka A, Kuznetsova M, Craig KJT, Rui A, et al. The potential of artificial intelligence to improve patient safety: a scoping review. npj Digit Med. 2021;4(1):54. 10.1038/s41746-021-00423-6 Ramgopal S, Sanchez-Pinto LN, Horvat CM. Artificial intelligence-based clinical decision support in pediatrics. Pediatr Res. 2023;93(2):334-41. 10.1038/s41390-022-02226-1 Shamout F, Zhu T, Clifton DA. Machine learning for clinical outcome prediction. IEEE Rev Biomed Eng. 2021;14:116-26. 10.1109/RBME.2020.3007816 Rush B, Celi LA, Stone DJ. Applying machine learning to continuously monitored physiological data. J Clin Monit Comput. 2019;33(5):887-93. 10.1007/s10877-018-0219-z Ai Y, He F, Lancaster E, Lee J. Application of machine learning for multi-community COVID-19 outbreak predictions with wastewater surveillance. PLoS One. 2022;17(11):e0277154. 10.1371/journal.pone.0277154 Di Martino F, Delmastro F. Explainable AI for clinical and remote health applications: a survey on tabular and time series data. Artif Intell Rev. 2023;56(6):5261-315. 10.1007/s10462-022-10304-3 Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, et al. Artificial intelligence for mental health care: clinical applications, barriers, facilitators, and artificial wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6(9):856-64. 10.1016/j.bpsc.2021.02.001 Finlayson SG, Subbaswamy A, Singh K, Bowers J, Kupke A, McMurry J, et al. The clinician and dataset shift in artificial intelligence. N Engl J Med. 2021;385(3):283-6. 10.1056/NEJMc2104626 Acosta JN, Falcone GJ, Rajpurkar P, Topol EJ. Multimodal biomedical AI. Nat Med. 2022;28(9):1773-84. 10.1038/s41591-022-01981-2 Macrae C. Governing the safety of artificial intelligence in healthcare. BMJ Qual Saf. 2019;28(6):495-8. 10.1136/bmjqs-2019-009484 Davahli MR, Karwowski W, Fiok K, Wan T, Parsaei HR. Controlling safety of artificial intelligence-based systems in healthcare. Symmetry. 2021;13(1):102. 10.3390/sym13010102 Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491-7. Habli I, Lawton T, Porter Z. Artificial intelligence in health care: accountability and safety. Bull World Health Organ. 2020;98(4):251-6. 10.2471/BLT.19.237487 Zhang J, Zhang ZM. Ethics and governance of trustworthy medical artificial intelligence. BMC Med Inform Decis Mak. 2023;23(1):7. 10.1186/s12911-023-02103-9 Taeihagh A. Governance of artificial intelligence. Policy Soc. 2021;40(2):137-57. 10.1080/14494035.2021.1928377