Machine Learning for Precision Treatments in Schizophrenia

项目摘要/摘要精神分裂症与精神病症状、情绪障碍、

Project Summary/Abstract Schizophrenia is associated with psychotic symptoms, mood disturbances, deficits in cognition, comorbidities, significant social and functional impairment and is a leading cause of disability in the U.S. and worldwide. Although antipsychotic medications and psychosocial treatments are effective for some symptoms of schizophrenia, effective regimens for all symptoms are not established. The primary limitation of treatment guidelines is reliance on RCTs that test limited treatments and their effects on few symptoms and comorbidities. Trials of treatments administered to address all aspects of impairment is prohibitively complex. Data driven machine learning (ML) can address this gap using large observational datasets with information about complex and effective regimens used in real-world practice. ML can cluster individuals with shared characteristics and identify unique regimens administered for their psychiatric and clinical comorbidities. These new treatment regimens are possible precision treatments. ML algorithms can then predict critical patient-centered outcomes for these different clusters (or classes) administered these treatment regimens. Examining the comparative effectiveness of these treatment regimens that predict critical outcomes is an essential next step. Unique pharmacoepidemiologic methods with observational data can simulate clinical trials. Propensity score methods address confounding, mimicking balance achieved by randomization in RCTs. These tools will determine which precision treatment regimens are the most effective for the classes in these datasets. Relevance of ML findings depends on data quality. Claims have the largest, most nationally representative samples reflecting real-world community practice patterns but use billing codes not originally designed for research. Electronic health records (EHR) are extensive but limited due to bias from incomplete records with uncertain accuracy and complexity due to their granular level of detail. This proposal will establish the strengths and limitations of these dataset types by conducting ML analyses on exemplar datasets, a Medicaid Analytic eXtract (MAX) national sample, and the Observational Health Data Sciences and Informatics (OHDSI) network New York-Presbyterian Hospital (iNYP) EHR. An enhancement to this project will compare more traditional multivariate and regression techniques to the ML findings identifying whether ML provides additional information. To address the “research-practice” gap the ML results will be translated into personalized treatment rules to inform clinical practice for schizophrenia treatment. After training in unsupervised and supervised learning in Training Aims A and B, Research Aim 1 will identify classes and their administered treatments in the datasets and Research Aim 2 will predict outcomes of those treatments: time to emergency department visit, time to re-admission and incidence of comorbidities. Research Aim 3 will use pharmacoepidemiologic methods learned in Training Aim C to compare effectiveness of the treatments, supporting an R01 submitted at the end of this K-award to test effectiveness in an international EHR dataset.