Page 65 - AIH-2-2

Artificial Intelligence in Health Predicting ICU mortality: A stacked ensemble model

for variable selection under dependence among covariates. risk prediction tool integrated in electronic health records in
Int Stat Rev. 2022;90(1):118-145. primary care settings. Bioelectron Med. 2024;10(1):24.
doi: 10.1111/insr.12469 doi: 10.1186/s42234-024-00156-3
42. Ren N, Zhao X, Zhang X. Mortality prediction in ICU using 46. BuHamra SS, Almutairi AN, Buhamrah AK, Almadani SH,
a stacked ensemble model. Comput Math Methods Med. Alibrahim YA. An NLP tool for data extraction from
2022;2022:3938492. electronic health records: COVID-19 mortalities and
doi: 10.1155/2022/3938492 comorbidities. Front Public Health. 2022;10:1070870.
43. Wong J, Murray Horwitz M, Zhou L, Toh S. Using machine doi: 10.3389/fpubh.2022.1070870
learning to identify health outcomes from electronic health 47. Kuo ZM, Chen KF, Tseng YJ. MoCab: A framework for
record data. Curr Epidemiol Rep. 2018;5(4):331-342. the deployment of machine learning models across health
doi: 10.1007/s40471-018-0165-9 information systems. Comput Methods Programs Biomed.
2024;255:108336.
44. Si Y, Du J, Li Z, et al. Deep representation learning of patient
data from Electronic Health Records (EHR): A systematic doi: 10.1016/j.cmpb.2024.108336
review. J Biomed Inform. 2021;115:103671. 48. Kawamoto K, Finkelstein J, Del Fiol G. Implementing
doi: 10.1016/j.jbi.2020.103671 machine learning in the electronic health record: Checklist of
essential considerations. Mayo Clin Proc. 2023;98(3):366-369.
45. Nguyen K, Wilson DL, Diiulio J, et al. Design and
development of a machine-learning-driven opioid overdose doi: 10.1016/j.mayocp.2023.01.013

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