Using machine learning to predict future foster care admission

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.

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.

Using a gradient-boosted tree machine learning algorithm, we built a model that identifies 2408 children (1.32%) as at risk of foster care admission in a sample of 181 841, of whom 1599 entered foster care within 1 year, resulting in a positive predictive value (PPV) of 66.4% (F ₁ = 55.5%, specificity = 99.5%, sensitivity = 47.67%), outperforming logistic regression. Accuracy was substantially better when using SDOH data (PPV of 84.72% with SDOH data compared to 27.44% without).

These results highlight the importance of SDOH factors in predicting foster care admission. They also point to the potential of machine learning for facilitating early intervention to prevent foster care admissions.