Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians' needs.

Journal: BMC psychiatry
Published Date:

Abstract

BACKGROUND: Developing predictive models for precision psychiatry is challenging because of unavailability of the necessary data: extracting useful information from existing electronic health record (EHR) data is not straightforward, and available clinical trial datasets are often not representative for heterogeneous patient groups. The aim of this study was constructing a natural language processing (NLP) pipeline that extracts variables for building predictive models from EHRs. We specifically tailor the pipeline for extracting information on outcomes of psychiatry treatment trajectories, applicable throughout the entire spectrum of mental health disorders ("transdiagnostic").

Authors

  • Rosanne J Turner
    University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands. r.j.turner@umcutrecht.nl.
  • Femke Coenen
    University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands.
  • Femke Roelofs
    University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands.
  • Karin Hagoort
    University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands.
  • Aki Härmä
    Philips Research, Eindhoven, The Netherlands.
  • Peter D Grünwald
    Machine Learning Group, CWI, Amsterdam, Netherlands.
  • Fleur P Velders
    University Medical Center Utrecht, Brain Center, Amsterdam, Netherlands.
  • Floortje E Scheepers
    Department of Psychiatry, University Medical Centre Utrecht, Utrecht, The Netherlands.