Mental Health Risk Adjustment with Clinical Categories and Machine Learning.

ABSTRACT:

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

SUBMITTER: Shrestha A 

PROVIDER: S-EPMC6056578 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature