Evaluating Bias-Mitigated Predictive Models of Perinatal Mood and Anxiety Disorders.

ABSTRACT:

Importance

Machine learning for augmented screening of perinatal mood and anxiety disorders (PMADs) requires thorough consideration of clinical biases embedded in electronic health records (EHRs) and rigorous evaluations of model performance.

Objective

To mitigate bias in predictive models of PMADs trained on commonly available EHRs.

Design, setting, and participants

This diagnostic study collected data as part of a quality improvement initiative from 2020 to 2023 at Cedars-Sinai Medical Center in Los Angeles, California. The study inclusion criteria were birthing patients aged 14 to 59 years with live birth records and admission to the postpartum unit or the maternal-fetal care unit after delivery.

Exposure

Patient-reported race and ethnicity (7 levels) obtain

SUBMITTER: Wong EF 

PROVIDER: S-EPMC11615713 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature