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Natural Language Processing: Unlocking the Potential of Electronic Health Record Data to Support Transdiagnostic Psychiatric Research

  • Rashmi Patel
    Correspondence
    Address correspondence to Rashmi Patel, Ph.D.
    Affiliations
    Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

    Holmusk Technologies Inc., New York, New York
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  • Matthew Wickersham
    Affiliations
    Weill-Cornell/Rockefeller/Sloan-Kettering Tri-Institutional MD-PhD Program, New York, New York
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  • Rudolf N. Cardinal
    Affiliations
    Department of Psychiatry, University of Cambridge, Cambridgeshire, United Kingdom

    Peterborough NHS Foundation Trust and Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
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  • Paolo Fusar-Poli
    Affiliations
    Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

    Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Lombardy, Italy
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  • Christoph U. Correll
    Affiliations
    Department of Child and Adolescent Psychiatry, Psychosomatic Medicine and Psychotherapy, Charité – Universitaetsmedizin Berlin, corporate member of Freie Universitaet Berlin, Humboldt Universitaet zu Berlin, and Berlin Institute of Health, Berlin, Germany

    Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York

    Department of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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Published:September 08, 2022DOI:https://doi.org/10.1016/j.bpsc.2022.09.002
      Mental disorders bring a substantial burden of illness and disability, but their underlying etiology and pathophysiology remain poorly characterized. Here, we outline the potential for data derived from natural language processing (NLP) of electronic health records (EHRs) to support transdiagnostic approaches to characterize mental disorders.
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