ArticleHealth services research2025
Comparison of Machine Learning Algorithms Identifying Children at Increased Risk of Out-of-Home Placement: Development and Practical Considerations.
Article in Health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Predicting knowledge of cervical cancer screening among reproductive-age women in sub-saharan africa using machine learning algorithms.Scientific reports · 2026Article
- Using machine learning to predict future foster care admission.Health affairs scholar · 2025Article
- Comparison of Machine Learning Algorithms Identifying Children at Increased Risk of Out-of-Home Placement: Development and Practical Considerations.Health services research · 2025Article
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Authors and funding
4 authors.
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Abstract
objectiveTo develop a machine learning (ML) algorithm capable of identifying children at risk of out-of-home placement among a Medicaid-insured population. STUDY SETTING AND
designThe study population includes children enrolled in a Medicaid accountable care organization between 2018 and 2022 in two nonurban Ohio counties served by the Centers for Medicare and Medicaid Services-funded Integrated Care for Kids Model. Using a retrospective cohort, we developed and compared a set of ML algorithms to identify children at risk of out-of-home placement within one year. ML algorithms tested include least absolute shrinkage and selection operator (LASSO)-regularized logistic regression and eXtreme gradient-boosted trees (XGBoost). We compared both modeling approaches with and without race as a candidate predictor. Performance metrics included the area under the receiver operating characteristic curve (AUROC) and the corrected partial AUROC at specificities ≥ 90% (pAUROC DATA SOURCES AND ANALYTIC SAMPLE: The modeling dataset was comprised of Medicaid claims and patient demographics data from Partners For Kids, a pediatric accountable care organization. PRINCIPAL
findingsOverall, XGBoost models outperformed LASSO models. When race was included in the model, XGBoost had an AUROC of 0.78 (95% confidence interval [CI]: 0.77-0.79) while the LASSO model had an AUROC of 0.75 (95% CI: 0.74-0.77). When race was excluded from the model, XGBoost had an AUROC of 0.76 (95% CI: 0.74-0.77) while LASSO had an AUROC of 0.73 (95% CI: 0.72-0.74).
conclusionsThe more complex XGBoost outperformed the simpler LASSO in predicting out-of-home placement and had less evidence of racial bias. This study highlights the complexities of developing predictive models in systems with known racial disparities and illustrates what can be accomplished when ML developers and policy leaders collaborate to maximize data to meet the needs of children and families.
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