Mental Health Risk Adjustment with Clinical Categories and Machine Learning
研究用非参数集成机器学习方法预测精神健康和物质使用障碍的医疗支出，发现超级学习算法表现最佳，且临床分类软件（CCS）优于常用的层级条件类别（HCC）系统。
Objective To 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 Design We 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 2 and predictive ratios. Principal Findings Results 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. Conclusions Literature 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.