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Predicting successful placements for youth in child welfare with machine learning

Author

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  • Trudeau, Kimberlee J.
  • Yang, Jichen
  • Di, Jiaming
  • Lu, Yi
  • Kraus, David R.

Abstract

Out-of-home placement decisions have extremely high stakes for the present and future well-being of children in care because some placement types, and multiple placements, are associated with poor outcomes. We propose that a clinical decision support system (CDSS) using existing data about children and their previous placement success could inform future placement decision-making for their peers. The objective of this study was to test the feasibility of developing machine learning models to predict the best level of care placement (i.e., the placement with the highest likelihood of doing well in treatment) based on each youth’s behavioral health needs and characteristics. We developed machine learning models to predict the probability of each youth’s treatment success in psychiatric residential care (i.e., Psychiatric Residential Treatment Facility [PRTF]) versus any other placement (AUROCs > 0.70) using data collected in standard care at a behavioral health organization. Placement recommendations based on these machine learning models distinguished between youth who did well in residential care versus non-residential care (e.g., 80% of those who received care in the recommended setting with the highest predicted likelihood of success had above average risk-adjusted outcomes). Then we developed and validated machine learning models to predict the probability of each youth’s treatment success across specific placement types in a state-wide system, achieving an average AUROC score of >0.75. Machine learning models based on risk-adjusted behavioral health and functional data show promise in predicting positive placement outcomes and informing future placement decisions for youth in care. Related ethical considerations are discussed.

Suggested Citation

  • Trudeau, Kimberlee J. & Yang, Jichen & Di, Jiaming & Lu, Yi & Kraus, David R., 2023. "Predicting successful placements for youth in child welfare with machine learning," Children and Youth Services Review, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:cysrev:v:153:y:2023:i:c:s0190740923003122
    DOI: 10.1016/j.childyouth.2023.107117
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    References listed on IDEAS

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    1. Sieracki, Jeffrey H. & Leon, Scott C. & Miller, Steven A. & Lyons, John S., 2008. "Individual and provider effects on mental health outcomes in child welfare: A three level growth curve approach," Children and Youth Services Review, Elsevier, vol. 30(7), pages 800-808, July.
    2. Kraus, David R. & Baxter, Elizabeth E. & Alexander, Pamela C. & Bentley, Jordan H., 2015. "The Treatment Outcome Package (TOP): A multi-dimensional level of care matrix for child welfare," Children and Youth Services Review, Elsevier, vol. 57(C), pages 171-178.
    3. Hee Yun Seol & Pragya Shrestha & Joy Fladager Muth & Chung-Il Wi & Sunghwan Sohn & Euijung Ryu & Miguel Park & Kathy Ihrke & Sungrim Moon & Katherine King & Philip Wheeler & Bijan Borah & James Moriar, 2021. "Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-16, August.
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