TY - JOUR
T1 - Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning
T2 - Algorithm Development and Validation
AU - Wiegersma, Sytske
AU - Hidajat, Maurice
AU - Schrieken, Bart
AU - Veldkamp, Bernard
AU - Olff, Miranda
N1 - Funding Information: This work has been conducted partly in the framework of the research project ??ZeEUS? co-funded by the European Commission under the 7th Research & Innovation Framework Programme, Mobility & Transport Directorate General under grant agreement n? 605485. Publisher Copyright: ©Sytske Wiegersma, Maurice Hidajat, Bart Schrieken, Bernard Veldkamp, Miranda Olff. Originally published in JMIR Mental Health (https://mental.jmir.org), 11.04.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on https://mental.jmir.org/, as well as this copyright and license information must be included.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - 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 they focused on screening for a single predefined disorder instead of multiple disorders simultaneously. Objective: The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment. Methods: On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search. Results: The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier. Conclusions: A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process.
AB - 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 they focused on screening for a single predefined disorder instead of multiple disorders simultaneously. Objective: The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment. Methods: On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search. Results: The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier. Conclusions: A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process.
KW - KEYWORDS supervised text classification
KW - automated intake
KW - computerized CBT
KW - mental health disorders
KW - multi-class classification
KW - referral
KW - screening
UR - http://www.scopus.com/inward/record.url?scp=85128840589&partnerID=8YFLogxK
U2 - https://doi.org/10.2196/21111
DO - https://doi.org/10.2196/21111
M3 - Article
C2 - 35404261
SN - 2368-7959
VL - 9
JO - JMIR Mental Health
JF - JMIR Mental Health
IS - 4
M1 - e21111
ER -