Development of depression detection algorithm using text scripts of routine psychiatric interview.

Journal: Frontiers in psychiatry
Published Date: Jan 4, 2024

Abstract

BACKGROUND: A psychiatric interview is one of the important procedures in diagnosing psychiatric disorders. Through this interview, psychiatrists listen to the patient's medical history and major complaints, check their emotional state, and obtain clues for clinical diagnosis. Although there have been attempts to diagnose a specific mental disorder from a short doctor-patient conversation, there has been no attempt to classify the patient's emotional state based on the text scripts from a formal interview of more than 30 min and use it to diagnose depression. This study aimed to utilize the existing machine learning algorithm in diagnosing depression using the transcripts of one-on-one interviews between psychiatrists and depressed patients.

Authors

  • Jihoon Oh
    Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
  • Taekgyu Lee
    College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Eun Su Chung
    College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Hyonsoo Kim
    Acryl, Seoul, Republic of Korea.
  • Kyongchul Cho
    Acryl, Seoul, Republic of Korea.
  • Hyunkyu Kim
    Acryl, Seoul, Republic of Korea.
  • Jihye Choi
    Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
  • Hyeon-Hee Sim
    Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.
  • Jongseo Lee
    College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • In Young Choi
    Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Dai-Jin Kim
    Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals