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Implementation of a machine learning algorithm for automated thematic annotations in avatar: A linear support vector

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Avatar Therapy (AT) uses automated text classification for analyzing schizophrenia treatment transcripts. This method shows potential for efficient analysis, comparable to human coders, opening doors for future research.

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Area of Science:

  • Psychiatry and Mental Health
  • Computational Linguistics
  • Artificial Intelligence in Healthcare

Background:

  • Avatar Therapy (AT) is an emerging treatment for persistent auditory verbal hallucinations in schizophrenia.
  • Current qualitative analysis of AT session transcripts relies on time-consuming human coding.
  • There is a need for automated methods capable of analyzing small datasets efficiently.

Purpose of the Study:

  • To develop and implement automated text classification for interactions within Avatar Therapy sessions.
  • To evaluate the performance of automated classification against human coder assessments.

Main Methods:

  • A Linear Support Vector Classifier was employed for automated theme classification.
  • The model was trained and tested on a limited dataset of Avatar Therapy session transcripts.

Main Results:

  • The automated text classification achieved an accuracy of 66.02%.
  • A substantial classification agreement of 0.647 was observed when compared to human coders.
  • The findings suggest the feasibility of using machine learning for analyzing therapeutic interactions.

Conclusions:

  • Automated text classification offers a viable and potentially more efficient alternative to manual transcript annotation in Avatar Therapy.
  • The study demonstrates the comparability of algorithmic classification to human judgment, supporting its clinical application.
  • This research paves the way for further investigations, including predicting therapy outcomes through automated analysis.