Using machine learning to predict future foster care admission.

ABSTRACT:

Introduction

Foster care admissions are highly traumatic for children and their families, often causing serious adverse outcomes. We seek to assess the viability of machine learning methods to identify children at risk of future foster care admission to facilitate diversion.

Methods

We use claims data for children enrolled in a Medicaid health plan in Ohio as well as for linked adults, along with data on individual and geographic social determinants of health (SDOH) factors. We test the performance of a gradient-boosted tree machine learning algorithm as compared to logistic regression. Of the children, 85% have SDOH data available.

Results

Using a gradient-boosted tree machine learning algorithm, we built a model that identifies 2408 children (1.32%) as at risk of fo

SUBMITTER: Ne'eman A 

PROVIDER: S-EPMC12629229 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature