Opal: an implementation science tool for machine learning clinical decision support in anesthesia.

ABSTRACT: Opal is the first published example of a full-stack platform infrastructure for an implementation science designed for ML in anesthesia that solves the problem of leveraging ML for clinical decision support. Users interact with a secure online Opal web application to select a desired operating room (OR) case cohort for data extraction, visualize datasets with built-in graphing techniques, and run in-client ML or extract data for external use. Opal was used to obtain data from 29,004 unique OR cases from a single academic institution for pre-operative prediction of post-operative acute kidney injury (AKI) based on creatinine KDIGO criteria using predictors which included pre-operative demographic, past medical history, medications, and flowsheet information. To demonstrate utility with unsu

SUBMITTER: Bishara A 

PROVIDER: S-EPMC9275816 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature