Navigating extreme class imbalance in suicide risk prediction.

ABSTRACT:

Background

The implementation of suicide risk models is challenging because the conditions in which they are developed often do not reflect those in which they are being used. The setting of an arbitrary classification threshold limits the interpretability of predictions, and their associated performance statistics. This work endeavors to explore different class imbalance ratios, across training sample compositions, time horizons and patient characteristics to understand how degree of imbalance affects the associated performance of regression-based predictive models of suicide.

Patients and setting

The study population included 1,649,577 patients who were selected from the Maryland Suicide Data Warehouse (MSDW) between 2016 and 2020. The MSDW contains clinical and demographi

SUBMITTER: Kitchen C 

PROVIDER: S-EPMC12833411 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature