{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T22:50:51Z","timestamp":1773096651987,"version":"3.50.1"},"reference-count":44,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2021,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof\u2010of\u2010concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either \u201cimprovement,\u201d defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or \u201cno improvement,\u201d defined as an inadequate treatment response (&lt;20% reduction in total PANSS). A random forest algorithm performed best relative to other tree\u2010based approaches in model ability to classify patients after 6\u00a0months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (&lt;0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof\u2010of\u2010concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development.<\/jats:p>","DOI":"10.1111\/cts.13035","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T14:28:11Z","timestamp":1620052091000},"page":"1864-1874","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials"],"prefix":"10.1111","volume":"14","author":[{"given":"Jagdeep T.","family":"Podichetty","sequence":"first","affiliation":[{"name":"Division of Clinical Pharmacology Department of Medicine Indiana University School of Medicine Indianapolis Indiana USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0493-0603","authenticated-orcid":false,"given":"Rebecca 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