ReviewJournal of personalized medicine2021
Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- Passive Screening for Depressive Symptoms Using Daily Wrist Actigraphy and Deep Learning: Model Development and Validation Study.JMIR mHealth and uHealth · 2026Article
- Research on depression diagnosis method based on multi-scale analysis of frontal lead EEG.BMC medical informatics and decision making · 2026Article
- Comparison and validation of machine learning-based screening models for elevated depressive symptoms in peritoneal dialysis patients.Frontiers in public health · 2026Article
- What machine learning teaches us about depression prediction across the life course: An exploratory comparison of predictive models.SSM - population health · 2025Article
- Detection of Depressive Symptoms in College Students Using Multimodal Passive Sensing Data and Light Gradient Boosting Machine: Longitudinal Pilot Study.JMIR formative research · 2025Article
- Explainable Machine Learning in the Prediction of Depression.Diagnostics (Basel, Switzerland) · 2025Article
- Cognitive biases: potential behavioral marker for future development of postpartum depression and childbirth-related post-traumatic stress disorder.Frontiers in psychiatry · 2025Article
- Can Loneliness be Predicted? Development of a Risk Prediction Model for Loneliness among Elderly Chinese: A Study Based on CLHLS.Research square · 2024Article
- Developing prediction algorithms for late-life depression using wearable devices: a cohort study protocol.BMJ open · 2024Article
- Impact of mobile connectivity on students' wellbeing: Detecting learners' depression using machine learning algorithms.PloS one · 2023Article
- An Elevated FIB-4 Score Is Associated with an Increased Incidence of Depression among Outpatients in Germany.Journal of clinical medicine · 2022Article
- Wearable Sensing Systems for Monitoring Mental Health.Sensors (Basel, Switzerland) · 2022Review
- Empirical Investigation for Predicting Depression from Different Machine Learning Based Voice Recognition Techniques.Evidence-based complementary and alternative medicine : eCAM · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.