Leveraging explainable artificial intelligence to optimize clinical decision support.

ABSTRACT:

Objective

To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Methods

We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful.

Results

The final dataset included 2 991 823 firings

SUBMITTER: Liu S 

PROVIDER: S-EPMC10990514 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature