Accepted for/Published in: JMIR Mental Health
Date Submitted: Jul 11, 2023
Date Accepted: Dec 1, 2023
Identification of predictors of mood disorder misdiagnosis and subsequent help-seeking behaviour in individuals with depressive symptoms: a gradient-boosted tree machine learning approach
ABSTRACT
Background:
Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder (MDD) and bipolar disorder (BD). Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with MDD and BD due to the overlap of symptoms with other mental and physical health conditions, as well as stigma and insufficient understanding of these disorders.
Objective:
In the present study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking.
Methods:
Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals.
Results:
The analysis included data from 924 symptomatic individuals. Models achieved a good predictive power, with area under the receiver operating characteristic curve (AUC) of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behaviour, the strongest predictors included shorter time elapsed since last speaking to a general practitioner (GP) about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages.
Conclusions:
This study provides a novel, machine learning-based approach to understand the interplay of factors that contribute to misdiagnosis and subsequent help-seeking in patients suffering from low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.
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Copyright
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