Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?

Journal: PloS one
Published Date: Oct 16, 2018

Abstract

BACKGROUND AND OBJECTIVE: Efficiently capturing the severity of positive valence symptoms could aid in risk stratification for adverse outcomes among patients with psychiatric disorders and identify optimal treatment strategies for patient subgroups. Motivated by the success of convolutional neural networks (CNNs) in classification tasks, we studied the application of various CNN architectures and their performance in predicting the severity of positive valence symptoms in patients with psychiatric disorders based on initial psychiatric evaluation records.

Authors

  • Hong-Jie Dai
    Department of Computer Science and Information Engineering, National Taitung University, Taiwan. Electronic address: hjdai@nttu.edu.tw.
  • Jitendra Jonnagaddala
    School of Public Health and Community Medicine, University of New South Wales, Australia; Asia-Pacific Ubiquitous Healthcare Research Centre, University of New South Wales, Australia; Prince of Wales Clinical School, University of New South Wales, Australia.

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