ArticleHealth services research2018
Mental Health Risk Adjustment with Clinical Categories and Machine Learning.
Article in Health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- A Multi-Schedule Machine Learning Pipeline for Medicare Reimbursement Change Prediction and Operational Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Machine learning based prediction of medication adherence in heart failure using large electronic health record cohort with linkages to pharmacy-fill and neighborhood-level data.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Risk adjustment for regional healthcare funding allocations with ensemble methods: an empirical study and interpretation.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Article
- Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA).Health services research · 2024Article
- Using machine-learning algorithms to improve imputation in the medical expenditure panel survey.Health services research · 2023Article
- A two-stage super learner for healthcare expenditures.Health services & outcomes research methodology · 2022Article
- Predictive modeling of service discontinuation in transitional age youth with recent behavioral health service use.Health services research · 2022Article
- Association of Follow-Up After an Emergency Department Visit for Mental Illness with Utilization Based Outcomes.Administration and policy in mental health · 2021Article
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study.Journal of clinical medicine · 2020Article
- Fair regression for health care spending.Biometrics · 2020Article
- Does machine learning improve prediction of VA primary care reliance?The American journal of managed care · 2020Observational
- Data transformations to improve the performance of health plan payment methods.Journal of health economics · 2019Article
- Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment.The American statistician · 2019Article
- Article
- Assessment of the Medicare Advantage Risk Adjustment Model for Measuring Veterans Affairs Hospital Performance.JAMA network open · 2018Article
- Using Predictive Analytics for Early Identification of Short-Term Disability Claimants Who Exhaust Their Benefits.Journal of occupational rehabilitation · 2018Article
- Sample Selection for Medicare Risk Adjustment Due to Systematically Missing Data.Health services research · 2018Article
- Robust Machine Learning Variable Importance Analyses of Medical Conditions for Health Care Spending.Health services research · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveTo propose nonparametric ensemble machine learning for mental health and substance use disorders (MHSUD) spending risk adjustment formulas, including considering Clinical Classification Software (CCS) categories as diagnostic covariates over the commonly used Hierarchical Condition Category (HCC) system. DATA SOURCES: 2012-2013 Truven MarketScan database. STUDY
designWe implement 21 algorithms to predict MHSUD spending, as well as a weighted combination of these algorithms called super learning. The algorithm collection included seven unique algorithms that were supplied with three differing sets of MHSUD-related predictors alongside demographic covariates: HCC, CCS, and HCC + CCS diagnostic variables. Performance was evaluated based on cross-validated R PRINCIPAL
findingsResults show that super learning had the best performance based on both metrics. The top single algorithm was random forests, which improved on ordinary least squares regression by 10 percent with respect to relative efficiency. CCS categories-based formulas were generally more predictive of MHSUD spending compared to HCC-based formulas.
conclusionsLiterature supports the potential benefit of implementing a separate MHSUD spending risk adjustment formula. Our results suggest there is an incentive to explore machine learning for MHSUD-specific risk adjustment, as well as considering CCS categories over HCCs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.