Machine learning models present an opportunity to accurately predict patient risk and support real-time clinical decision making. Andrew Bishara and colleagues, including Catherine Chiu, Elizabeth Whitlock, Jackie Leung, Anne Donovan, Mervyn Maze and Atul Butte, explore considerations and implications for the implementation of machine learning technologies in various healthcare settings.
Bishara A, Chiu C, Whitlock EL, Douglas VC, Lee S, Butte AJ, Leung JM, Donovan AL. Postoperative delirium prediction using machine learning models and preoperative electronic health record data. BMC Anesthesiol. 2022 Jan 3;22(1):8. https://pubmed.ncbi.nlm.nih.gov/34979919/
Bishara A, Maze EH, Maze M. Considerations for the implementation of machine learning into acute care settings. Br Med Bull. 2022 Jan 28;ldac001. https://pubmed.ncbi.nlm.nih.gov/35107127/
Alizadehsani R, Khosravi A, Roshanzamir M, Abdar M, Sarrafzadegan N, Shafie D, Khozeimeh F, Shoeibi A, Nahavandi S, Panahiazar M, Bishara A, Beygui RE, Puri R, Kapadia S, Tan RS, Acharya UR. Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020. Comput Biol Med. 2021 Jan;128:104095. https://pubmed.ncbi.nlm.nih.gov/33217660/
Bishara A, Wong A, Wang L, Chopra M, Fan W, Lin A, Fong N, Palacharla A, Spinner J, Armstrong R, Pletcher MJ, Lituiev D, Hadley D, Butte A. Opal: an implementation science tool for machine learning clinical decision support in anesthesia. J Clin Monit Comput. 2021 Nov 27. https://pubmed.ncbi.nlm.nih.gov/34837585/
Bishara AM, Lituiev DS, Adelmann D, Kothari RP, Malinoski DJ, Nudel JD, Sally MB, Hirose R, Hadley DD, Niemann CU. Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry. Transplant Direct. 2021 Sep 27;7(10):e771. https://pubmed.ncbi.nlm.nih.gov/34604507/
Nudel J, Bishara AM, de Geus SWL, Patil P, Srinivasan J, Hess DT, Woodson J. Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database. Surg Endosc. 2021 Jan;35(1):182-191. https://pubmed.ncbi.nlm.nih.gov/31953733/