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A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
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Computer-aided therapeutic diagnosis for anorexia.

Dominik Spinczyk1, Mateusz Bas2, Mariusz Dzieciątko3

  • 1Faculty of Biomedical Engineering, Silesian University of Technology, 40 Roosevelta, 41-800, Zabrze, Poland. dspinczyk@polsl.pl.

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Summary

This study introduces a computer-aided diagnostic tool for anorexia nervosa, analyzing patient statements for body image and emotions. This method aids in rapid diagnosis and treatment initiation for anorexia nervosa.

Keywords:
Affective verbal stimuliAnorexia nervosaEmotionNencki Affective Word ListSentiment analysisSentiment dictionaryText classifiersText mining

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Area of Science:

  • Psychiatry
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Anorexia nervosa etiology is not fully understood, necessitating effective diagnostic tools.
  • Clinical diagnosis involves identifying causes like body image issues, food, family, and peer influences.
  • Computer-aided tools are recommended to assist psychologists in diagnosis and treatment planning.

Purpose of the Study:

  • To present a novel computer-aided therapeutic diagnosis method for anorexia nervosa.
  • To enhance the diagnostic process for anorexia nervosa using automated analysis.
  • To facilitate timely intervention and improve patient prognosis.

Main Methods:

  • The proposed method involves four stages: patient free statements, Recurrent Neural Network (RNN) sentiment analysis, basic emotion intensity assessment (Nencki Affective Word List), and dictionary-based sentiment analysis for difficulty areas.
  • Utilized RNN for general sentiment analysis and a dictionary approach for specific emotion and difficulty assessment.
  • Employed natural language processing techniques for analyzing patient-reported data.

Main Results:

  • Sentiment analysis achieved 72% effectiveness with RNN and 51% with the dictionary-based method.
  • A significant 10% difference in sadness intensity was observed between control and research groups using the dictionary method.
  • The study demonstrates the potential of automated analysis in quantifying patient-reported experiences.

Conclusions:

  • Rapid sentiment analysis of patient statements can expedite anorexia nervosa diagnosis and therapy initiation.
  • The automated method may reduce patient aversion to therapy, improving communication and engagement.
  • This approach offers a promising avenue for improving treatment outcomes and prognosis in anorexia nervosa.