The potential for leveraging machine learning to filter medication alerts.

ABSTRACT:

Objective

To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view.

Materials and methods

We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion

SUBMITTER: Liu S 

PROVIDER: S-EPMC9006688 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature