Accepted for/Published in: JMIR Mental Health
Date Submitted: Jun 5, 2020
Date Accepted: Sep 28, 2021
Improving Online Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning: Algorithm Development and Validation
ABSTRACT
Background:
Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but those focused on screening for one predefined disorder instead of multiple disorders simultaneously.
Objective:
This study developed a Dutch multiclass text classification model to screen for a range of mental disorders, in order to refer new patients to the most suitable treatment.
Methods:
Based on patients’ (N = 5,863) textual responses to a questionnaire currently used for intake and referral, a seven-class classifier was developed to distinguish between anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear Support Vector Machine (SVM) was fitted using nested cross-validation grid search.
Results:
The highest classification rate was found for eating disorders (82%). Scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely due to overlapping symptoms. The overall classification accuracy (49%) was reasonable for a seven-class classifier.
Conclusions:
Though this study enabled simultaneous screening for multiple disorders, performance for disorders other than eating needs to be improved before implementation in mental health practice.
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Copyright
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