Variational Bayes machine learning for risk adjustment of general outcome indicators with examples in urology.

ABSTRACT: Risk adjustment is often necessary for outcome quality indicators (QIs) to provide fair and accurate feedback to healthcare professionals. However, traditional risk adjustment models are generally oversimplified and not equipped to disentangle complex factors influencing outcomes that are out of a healthcare professional's control. We present VIRGO, a novel variational Bayes model trained on routinely collected, large administrative datasets to risk-adjust outcome QIs. VIRGO uses detailed demographics, diagnosis, and procedure codes to provide individualized risk adjustment and explanations on patient factors affecting outcomes. VIRGO achieves state-of-the-art on external datasets and features capabilities of uncertainty expression, explainable features, and counterfactual analysis capabil

SUBMITTER: Koh HJW 

PROVIDER: S-EPMC11401950 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature