Abstract
BackgroundMajor depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder.ObjectiveThis study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span.MethodsWe proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities.ResultsThe evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%.ConclusionsGiven the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.
Highlights
BackgroundMajor depressive disorder (MDD), known as depression, is among the most prevalent psychiatric disorders globally [1,2]
Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks. (J Med Internet Res 2019;21(6):e12554) doi:10.2196/12554
As detailed we mainly focus on 2 different methods, both of which are based on machine learning algorithms that use textual and semantic similarity features along with writing features (WFs) to predict a subject’s depression condition
Summary
Major depressive disorder (MDD), known as depression, is among the most prevalent psychiatric disorders globally [1,2]. As described in the World Health Organization’s Comprehensive Mental Health Action Plan 2013-2020 [3], depression alone affects more than 300 million people worldwide and is one of the largest single causes of disability worldwide, for women. It is vital to provide an early identification of subjects suffering from depression to intervene as soon as possible and minimize the impact on public health by potentially reducing the escalation of the disease. Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder
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