Performance of machine learning algorithms for surgical site infection case detection and prediction: A systematic review and meta-analysis.

ABSTRACT:

Background

Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms.

Methods

MEDLINE, EMBASE, CINAHL, CENTRAL and Web of Science were searched from inception to March 25, 2021. Study characteristics and algorithm development information were extracted. Performance statistics (e.g., sensitivity, area under th

SUBMITTER: Wu G 

PROVIDER: S-EPMC9793260 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature