Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data

TL;DR

This study developed scalable machine learning tools to predict severe mental illnesses at a population level using large-scale healthcare claims and electronic health records, aiming for early detection and prevention.

Contribution

The paper introduces a novel, scalable machine learning framework for large-scale risk screening of SMIs using diverse real-world healthcare data sources.

Findings

Models achieved high predictive accuracy across data sources.

Effective risk prediction among young adults and substance-related cases.

Demonstrated feasibility of population-level mental health risk screening.

Abstract

Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at large scale could inform early prevention and treatment. Objective: A scalable machine learning based tool was developed to conduct population-level risk screening for SMIs, including schizophrenia, schizoaffective disorders, psychosis, and bipolar disorders,using 1) healthcare insurance claims and 2) electronic health records (EHRs). Design, setting and participants: Data from beneficiaries from a nationwide commercial healthcare insurer with 77.4 million members and data from patients from EHRs from eight academic hospitals based in the U.S. were used. First, the predictive models were constructed and tested using data in case-control cohorts from insurance claims or EHR data. Second, performance of the predictive models across data sources were analyzed. Third, as an illustrative application, the models were further trained to predict risks of SMIs among 18-year old young adults and individuals with substance associated conditions. Main outcomes and measures: Machine learning-based predictive models for SMIs in the general population were built based on insurance claims and EHR.