Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review

•Excellent performance metrics of ML do not guarantee adoption in clinical practice.•To date, most studies have fallen short in terms of external validation of ML models.•Studies on ML have a high to moderate risk of bias in reporting.•ML algorithms deployed for clinical use are much fewer than expe...

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Bibliographic Details
Published in:	International journal of medical informatics (Shannon, Ireland) Vol. 159; p. 104679
Main Authors:	Nwanosike, Ezekwesiri Michael, Conway, Barbara R, Merchant, Hamid A, Hasan, Syed Shahzad
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier B.V 01.03.2022
Subjects:	Algorithms AUROC Clinical practice Clinical studies COVID-19 Electronic health records (EHRs) Humans Logistic Models Machine Learning Model deployment Prediction SARS-CoV-2 EHRs Model deployment LR TRIPOD Prediction AUROC SVM Electronic health records (EHRs) Clinical practice COVID-19 RF PROBAST XGBoost Machine learning Clinical studies CDSS ML
ISSN:	1386-5056, 1872-8243, 1872-8243
Online Access:	Get full text
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