aceUtilizing categorical boosting and SHAP to understand key predictors of frequency of use at discharge for completed substance abuse treatments in TEDS-D
Background: Substance use disorder is a pressing US public health crisis, with 48.7 million people reporting past-year substance use and over 100 000 overdose deaths in 2022. Building on a growing body of machine learning research using the SAMHSA treatment episode Data set-discharges (TEDS-D), this study introduces frequency of use at discharge as an outcome variable and the inclusion of U.S. state as a predictor. Research Design and Methods: First, reason for discharge was evaluated as a dependent variable and…
A boosted-tree model with SHAP explanations predicted substance use frequency at discharge with strong accuracy and showed that treatment and geographic factors, not patient attributes, mattered most, giving a basis for tailoring treatment.
State-level models agreed only moderately with the national model, and federal data substantially underreported some treatment modalities in Hawai'i, which complicates national benchmarking and may obscure true drivers of recovery.
State-level models showed only moderate agreement with the national model, and underreporting in federal data means national benchmarks may not reflect the true drivers of recovery.
Evidence
- Peer-reviewedMachine Learning: Health2026-10-07
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Truvace Impact Record TRV-2026-1482, v1: “Utilizing categorical boosting and SHAP to understand key predictors of frequency of use at discharge for completed substance abuse treatments in TEDS-D.” Truvace, 2026-10-15. /record/TRV-2026-1482 (accessed at citation time). sha256 49f5b61f532fc808…
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