Predicting patient outcomes in psychiatric hospitals with routine data: a machine learning approach.

Journal: BMC medical informatics and decision making
Published Date: Feb 6, 2020

Abstract

BACKGROUND: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier.

Authors

  • J Wolff
    Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. jan.wolff@uniklinik-freiburg.de.
  • A Gary
    Department of Business Development, Forensic Commitment and Quality Management, Vitos GmbH, Kassel, Germany.
  • D Jung
    Vitos Hospital for Psychiatry und Psychotherapy, Kassel, Germany.
  • C Normann
    Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • K Kaier
    Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisgau, Germany.
  • H Binder
    Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Breisgau, Germany.
  • K Domschke
    Department of Psychiatry and Psychotherapy, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • A Klimke
    Vitos Hochtaunus, Friedrichsdorf, Germany.
  • M Franz
    Vitos Hospital Giessen-Marburg, Giessen, Germany.

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