cirpublications.com-kVB2-1790877875-o899413995 1790877875 Clinical Intelligence Research Press info@cirpublications.com Clinical Intelligence Research Press Journal of Artificial Intelligence for Healthcare Systems J. Artif. Intell. Healthc. Syst. 3149-8981 2022 1 2 Lucas Meyer Anna Schmid Stefan Braun 2022 10.68159/o899413995 https://cirpublications.com/pub/journal/1/article/o899413995 Johnson KW, Yahi A, Miotto R, Li L, Ricks D, Jebakaran J, et al. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai heart failure cohort. Pac Symp Biocomput. 2017;22:276-87. 10.1142/9789813207813_0027 Marafino BJ, Schuler A, Liu VX, Escobar GJ, Baiocchi M. Predicting preventable hospital readmissions with causal machine learning. Health Serv Res. 2020;55(6):993-1002. 10.1111/1475-6773.13586 Kalagara S, Nakhla J, Chang V, Skovrlj B, Caridi JM, Hilibrand AS, et al. Machine learning modeling for predicting hospital readmission following lumbar laminectomy. J Neurosurg Spine. 2019;30(3):344-52. 10.3171/2018.8.SPINE1869 Alzeer AH, Alshaygi A. Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia. Int J Med Inform. 2021;154:104565. 10.1016/j.ijmedinf.2021.104565 Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak. 2018;18(1):44. 10.1186/s12911-018-0620-z Morgan DJ, Bame B, Zimberg P, Pimienta J, Scully K, Desai R, et al. Assessment of machine learning vs standard prediction rules for predicting hospital readmissions. JAMA Netw Open. 2019;2(3):e190348. 10.1001/jamanetworkopen.2019.0348 Theek B, Roeder I, Grimm L, Schmitz F, Schrader J, Steuw M, et al. Automation of data analysis in molecular cancer imaging and its potential impact on future clinical practice. Methods. 2021;188:30-6. 10.1016/j.ymeth.2020.06.019 Amritphale A, Gurm HS, Gul F, Amritphale N. Predictors of 30-day unplanned readmission after carotid artery stenting using artificial intelligence. Adv Ther. 2021;38(6):2954-72. 10.1007/s12325-021-01709-7 Rojas JC, Carey KA, Edelson DP, Venable LR, Howell MD, Churpek MM, et al. Predicting intensive care unit readmission with machine learning using electronic health record data. Ann Am Thorac Soc. 2018;15(7):846-53. 10.1513/AnnalsATS.201710-787OC Hung M, Li W, He Y, Li C, Hon ES, Lipsky MS, et al. Prediction of 30-day hospital readmissions for all-cause dental conditions using machine learning. BMC Oral Health. 2020;20(1):307. 10.1186/s12903-020-01302-2 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. Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: a mapping review. Soc Sci Med. 2020;260:113172. 10.1016/j.socscimed.2020.113172 Price WN II, Cohen IG. Privacy in the age of medical big data. Nat Med. 2019;25(1):37-43. 10.1038/s41591-018-0272-7 Stanfill MH, Marc DT. Health information management: implications of artificial intelligence on healthcare data and information management. Yearb Med Inform. 2019;28(1):56-64. 10.1055/s-0039-1677913 Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine learning and deep learning in medical imaging: intelligent imaging. J Med Imaging Radiat Sci. 2019;50(4):477-87. 10.1016/j.jmir.2019.09.005 Jia H. Yi Zeng: promoting good governance of artificial intelligence. Natl Sci Rev. 2020;7(12):1954-6. Alruwaili FF. Artificial intelligence and multi agent based distributed ledger system for better privacy and security of electronic healthcare records. PeerJ Comput Sci. 2020;6:e323. 10.7717/peerj-cs.323 Stewart C, Cristofaro S, Schneider P, Bauer C, Cortellesi P. Mapping ethico-legal principles for the use of artificial intelligence in gastroenterology. J Gastroenterol Hepatol. 2021;36(9):2413-9. 10.1111/jgh.15494 Mišić VV, Gabel E, Hofer I, Raji MA, Mahajan A. Machine learning prediction of postoperative emergency department hospital readmission. Anesthesiology. 2020;132(4):663-77. 10.1097/ALN.0000000000003140 Huang Y, Talwar A, Chatterjee S, Aparicio HJ. Application of machine learning in predicting hospital readmissions: a scoping review of the literature. BMC Med Res Methodol. 2021;21(1):96. 10.1186/s12874-021-01285-z Li Q, Yao X, Eilerman BS, Xu Y. How good is machine learning in predicting all-cause 30-day hospital readmission? Evidence from administrative data. Inquiry. 2020;57:46958020962970. 10.1177/0046958020962970 Morel D, Yu F, Kalathil S, Ferrand E, Boucekine M, Falissard B, et al. Predicting hospital readmission in patients with mental or substance use disorders: a machine learning approach. Int J Med Inform. 2020;139:104136. 10.1016/j.ijmedinf.2020.104136 Artetxe A, Beristain A, Graña M. Predictive models for hospital readmission risk: a systematic review of methods. Comput Methods Programs Biomed. 2018;164:49-64. 10.1016/j.cmpb.2018.06.006 Romero-Brufau S, Huddleston JM, Escobar GJ, Liebow M. Implementation of artificial intelligence-based clinical decision support to reduce hospital readmissions at a regional hospital. Appl Clin Inform. 2020;11(4):570-7. 10.1055/s-0040-1715826 Baig MM, GholamHosseini H, Moqeem AA, Mirza F, Lindén M. A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model. Med Biol Eng Comput. 2020;58(3):545-56. 10.1007/s11517-019-02105-4 Hung M, Li W, Hon ES, Su S, Zhou G, Lipsky MS, et al. Machine learning approach to predict risk of 90-day hospital readmissions in patients with atrial fibrillation: implications for quality improvement in healthcare. Heart Mind. 2020;4(3):59-65. 10.4103/hm.hm_30_20 Gruendner J, Schwachhofer T, Sippl P, Gulden C, Kapsner LA, Zierk J, et al. Integrating genomics and clinical data for statistical analysis by using GEnome MINIng (GEMINI) and fast healthcare interoperability resources (FHIR): system design and implementation. J Med Internet Res. 2020;22(10):e19830. 10.2196/19830