Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

Abstract

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques

“…In the field of psychiatry, diagnostic measures and its treatment tend to be a tedious task. To make the prediction quicker and simpler, behavioral machine learning methods [ 32 ] are contributing a lot for the specificity and sensitivity of the depression and anxiety diagnosis. Machine-based assessments always seem to be the better decision while comparing with the perspective of well-trained clinicians, and it helps in identifying the suitable treatments.…”

Which features of repetitive negative thinking and positive reappraisal predict depression? An in-depth investigation using artificial neural networks with feature selection.

“…Moreover, the present findings may also improve clinical decision-making by informing behavioral diagnostic tools for depression (Richter et al, 2021) and informing the selection of treatment targets once depressive symptoms have been developed using a process-based therapy approach (Hofmann & Hayes, 2019). That is, neural network modeling tools integrating behavioral data (e.g., emotion regulation strategy use) could be harnessed to accurately diagnose depressive symptoms.…”

“…Artificial neural networks map input features into outputs through a multilayer network structure that captures hidden patterns within the data. Such neural networks are highly successful in machine learning tasks such as prediction, forecasting, or classification, and are increasingly used in research on mental health (Ophir et al, 2020; Richter et al, 2021; Schultebraucks et al, 2020; Sheetal et al, 2020). In contrast to traditional regression-based methods that detect only linear relationships, artificial neural networks effectively model interactions and other nonlinearities as well as multicollinear and high-dimensional data.…”

Wearable Sensing Systems for Monitoring Mental Health

“…These methods require trained clinicians for data collection and interpretation, inevitably limiting their accessibility. And these methods are subject to reporting unreliable results due to personal bias, possibly confounding the subsequent clinical decision making for mental health treatments [ 9 , 10 ]. In this context, there has been an increasing interest in wearable sensors that can collect in real time the biosignals associated with mental status [ 11 ].…”