ArticleHealth services research2022
Predictive modeling of service discontinuation in transitional age youth with recent behavioral health service use.
Article in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- Readiness to change among justice-involved young adults in an alternative sentencing program who screened positive for alcohol or drug risk.Addictive behaviors reports · 2022Article
- Predictive modeling of service discontinuation in transitional age youth with recent behavioral health service use.Health services research · 2022Article
- Deep Learning-Based Mental Health Model on Primary and Secondary School Students' Quality Cultivation.Computational intelligence and neuroscience · 2022Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo develop and test predictive models of discontinuation of behavioral health service use within 12 months in transitional age youth with recent behavioral health service use. DATA SOURCES: Administrative claims for Medicaid beneficiaries aged 15-26 years in Connecticut. STUDY
designWe compared the performance of a decision tree, random forest, and gradient boosting machine learning algorithms to logistic regression in predicting service discontinuation within 12 months among beneficiaries using behavioral health services. DATA EXTRACTION: We identified 33,532 transitional age youth with ≥1 claim for a primary behavioral health diagnosis in 2016 and Medicaid enrollment of ≥11 months in 2016 and ≥11 months in 2017. PRINCIPAL
findingsClassification accuracy for identifying youth who discontinued behavioral health service use was highest for gradient boosting (80%, AUC = 0.86), decision tree (79%, AUC = 0.84), and random forest (79%, AUC = 0.86), as compared with logistic regression (71%, AUC = 0.71).
conclusionsPredictive models based on Medicaid claims can assist in identifying transitional age youth who are at risk of discontinuing from behavioral health care within 12 months, thus allowing for proactive assessment and outreach to promote continuity of care for younger persons who have behavioral health needs.
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