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Importance: The prevalence of severe mental illnesses (SMIs) in the UnitedStates is approximately 3% of the whole population. The ability to conduct riskscreening of SMIs at large scale could inform early prevention and treatment.  Objective: A scalable machine learning based tool was developed to conductpopulation-level risk screening for SMIs, including schizophrenia,schizoaffective disorders, psychosis, and bipolar disorders,using 1) healthcareinsurance claims and 2) electronic health records (EHRs).  Design, setting and participants: Data from beneficiaries from a nationwidecommercial healthcare insurer with 77.4 million members and data from patientsfrom EHRs from eight academic hospitals based in the U.S. were used. First, thepredictive models were constructed and tested using data in case-controlcohorts from insurance claims or EHR data. Second, performance of thepredictive models across data sources were analyzed. Third, as an illustrativeapplication, the models were further trained to predict risks of SMIs among18-year old young adults and individuals with substance associated conditions.  Main outcomes and measures: Machine learning-based predictive models for SMIsin the general population were built based on insurance claims and EHR.

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