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A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
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Development of depression detection algorithm using text scripts of routine psychiatric interview.

Jihoon Oh1, Taekgyu Lee2, Eun Su Chung2

  • 1Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.

Frontiers in Psychiatry
|January 19, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning accurately diagnosed depression by analyzing patient interview transcripts and identifying emotional patterns. This approach offers a novel tool for psychiatrists to aid in depression diagnosis using text-based analysis.

Keywords:
depressionemotionsmachine learningpsychological interviewsentiment analysis

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Area of Science:

  • Psychiatry
  • Computational Linguistics
  • Machine Learning

Background:

  • Psychiatric interviews are crucial for diagnosing mental health disorders.
  • Classifying patient emotions from long interviews using text scripts is an underexplored area for depression diagnosis.
  • This study explores using machine learning on interview transcripts to diagnose depression.

Purpose of the Study:

  • To develop a machine learning model for diagnosing depression using text transcripts from psychiatric interviews.
  • To analyze emotional characteristics in depressed patients compared to non-depressed individuals.

Main Methods:

  • Utilized text scripts from 77 clinical patients (60 with depression, 17 without).
  • Employed a text emotion recognition module to identify emotions in each sentence.
  • Applied a machine learning algorithm to differentiate between depressed and non-depressed patients based on interview transcripts.

Main Results:

  • The machine learning model achieved an acceptable accuracy (AUC of 0.85) in classifying depression.
  • Significant differences in emotion distribution were observed between depressed and non-depressed groups (p < 0.001).
  • Disgust was the most influential emotion in distinguishing between the two groups (p < 0.001).

Conclusions:

  • A novel, practical approach for depression detection using machine learning on psychiatric interview text scripts.
  • The model can assist psychiatrists in clinical settings by analyzing patient interview transcripts.
  • Highlights the potential of understanding emotional characteristics for depression diagnosis.