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Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine

Sytske Wiegersma1, Maurice Hidajat2, Bart Schrieken2

  • 1Department of Research Methodology, Measurement and Data Analysis, University of Twente, Enschede, Netherlands.

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|April 11, 2022
PubMed
Summary
This summary is machine-generated.

A new Dutch text-classification model can screen patient responses for multiple mental health disorders, aiding in efficient patient referral and diagnosis. This machine learning approach improves the intake process for various conditions.

Keywords:
automated intake and referralcomputerized CBTmental health disordersmulti-class classificationscreeningsupervised text classification

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Area of Science:

  • Natural Language Processing
  • Machine Learning in Healthcare
  • Mental Health Diagnostics

Background:

  • Text mining and machine learning are increasingly applied in mental health research and practice.
  • Previous studies focused on single disorder detection, limiting simultaneous multi-disorder screening.
  • Efficient diagnosis and monitoring are crucial for mental healthcare.

Purpose of the Study:

  • Develop a Dutch multi-class text-classification model.
  • Screen for a range of mental disorders simultaneously.
  • Facilitate appropriate patient referral to treatment.

Main Methods:

  • Utilized textual responses from 5,863 patients.
  • Developed a 7-class classifier for anxiety, panic, PTSD, mood, eating, substance use, and somatic symptom disorders.
  • Employed a linear support vector machine with nested cross-validation.

Main Results:

  • Highest accuracy for eating disorders (82%).
  • Moderate accuracy for panic (55%), PTSD (52%), mood (50%), and somatic symptom disorders (50%).
  • Lower accuracy for anxiety (35%) and substance use disorders (33%) due to symptom overlap; overall accuracy 49%.

Conclusions:

  • A text-classification model for multiple mental health disorders was successfully developed.
  • The model provides an outcome score for diagnostic interviews and referrals.
  • Potential for a more efficient and standardized mental health intake process.