Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.

Journal: Journal of biomedical informatics
Published Date: Oct 1, 2018

Abstract

OBJECTIVE: Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supplementary means of risk prediction is from the automated analysis of case notes in Electronic Health Records (EHRs) using Natural Language Processing (NLP). This exploratory study rated presence or absence and frequency of words in a forensic EHR dataset, comparing four reference dictionaries. Seven machine learning algorithms and different time periods of EHR analysis were used to probe which dictionary and which time period were most predictive of risk assessment scores on validated instruments.

Authors

  • Duy Van Le
    School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Private Bag 87, Hobart 7001, TAS, Australia. Electronic address: duyvan.le@utas.edu.au.
  • James Montgomery
    School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Private Bag 87, Hobart 7001, TAS, Australia.
  • Kenneth C Kirkby
    School of Medicine, College of Health and Medicine, University of Tasmania, Private Bag 87, Hobart 7001, TAS, Australia.
  • Joel Scanlan
    School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, Private Bag 87, Hobart 7001, TAS, Australia.

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