Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression.

ABSTRACT:

Importance

The lack of standards in methods to reduce bias for clinical algorithms presents various challenges in providing reliable predictions and in addressing health disparities.

Objective

To evaluate approaches for reducing bias in machine learning models using a real-world clinical scenario.

Design, setting, and participants

Health data for this cohort study were obtained from the IBM MarketScan Medicaid Database. Eligibility criteria were as follows: (1) Female individuals aged 12 to 55 years with a live birth record identified by delivery-related codes from January 1, 2014, through December 31, 2018; (2) greater than 80% enrollment through pregnancy to 60 days post partum; and (3) evidence of coverage for depression screening and mental health services. Statis

SUBMITTER: Park Y 

PROVIDER: S-EPMC8050742 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature