首页|Reports Summarize Machine Learning Study Results from Washington University (Exa mining the Most Important Risk Factors for Predicting Youth Persistent and Distr essing Psychotic-like Experiences)
Reports Summarize Machine Learning Study Results from Washington University (Exa mining the Most Important Risk Factors for Predicting Youth Persistent and Distr essing Psychotic-like Experiences)
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Investigators publish new report on Ma chine Learning. According to news reporting from St. Louis, Missouri, by NewsRx journalists, research stated, "Persistence and distress distinguish more clinica lly significant psychotic-like experiences (PLEs) from those that are less likel y to be associated with impairment and/or need for care. Identifying risk factor s that identify clinically relevant PLEs early in development is important for i mproving our understanding of the etiopathogenesis of these experiences." Funders for this research include NIH National Institute on Drug Abuse (NIDA), N IH National Institute of Mental Health (NIMH), BrightFocus Foundation, National Institutes of Health (NIH) -USA, NIH National Institute of Mental Health (NIMH) , BrightFocus Foundation, NIH National Institute of Neurological Disorders & Stroke (NINDS).
St. LouisMissouriUnited StatesNort h and Central AmericaCyborgsEmerging TechnologiesMachine LearningRisk an d PreventionWashington University
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