Machine learning applied to digital phenotyping: A systematic literature review and taxonomy

MP dos Santos, WF Heckler, RS Bavaresco… - Computers in Human …, 2024 - Elsevier
Health conditions, encompassing both physical and mental aspects, hold an influence that
extends beyond the individual. These conditions affect personal well-being, relationships …

[HTML][HTML] Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review

E Todd, R Orr, E Gamage, E West, T Jabeen… - Computers in Biology …, 2025 - Elsevier
Abstract Background Machine Learning (ML) models have been used to predict common
mental disorders (CMDs) and may provide insights into the key modifiable factors that can …

[HTML][HTML] Depression recognition using daily wearable-derived physiological data

X Shui, H Xu, S Tan, D Zhang - Sensors, 2025 - mdpi.com
The objective identification of depression using physiological data has emerged as a
significant research focus within the field of psychiatry. The advancement of wearable …

[HTML][HTML] SAD: Self-assessment of depression for Bangladeshi university students using machine learning and NLP

MS Azad, SI Leeon, R Khan, N Mohammed, S Momen - Array, 2025 - Elsevier
Depressive illness, influenced by social, psychological, and biological factors, is a significant
public health concern that necessitates accurate and prompt diagnosis for effective …

How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review

BL Schaab, PÜ Calvetti, S Hoffmann… - Cadernos de Saúde …, 2024 - SciELO Public Health
Undergraduate students are often impacted by depression, anxiety, and stress. In this
context, machine learning may support mental health assessment. Based on the following …

Predicting dimensions of depression from smartphone data

VL Holstein, S Akre, R Leenings, Y Chung, T Hahn… - medRxiv, 2024 - medrxiv.org
Depressive disorders are highly prevalent but demand nuanced personalized treatment that
traditional approaches in psychiatry cannot address. This gap has prompted a surge of …

[HTML][HTML] Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study

MD Turjo, KS Mundada, NJ Haque… - JMIR Research …, 2024 - researchprotocols.org
Background: Suicide stands as a global public health concern with a pronounced impact,
especially in low-and middle-income countries, where it remains largely unnoticed as a …

[HTML][HTML] Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a …

MS Ahmed, T Hasan, S Islam… - JMIR Research …, 2024 - researchprotocols.org
Background Understanding a student's depressive symptoms could facilitate significantly
more precise diagnosis and treatment. However, few studies have focused on depressive …

Early Depression Detection Using Modern AI Techniques: Issues, Opportunities, and Challenges

S Dey, KV Singh - Exploring the Micro World of Robotics Through …, 2025 - igi-global.com
Depression is a widespread and debilitating mental health disorder, impacting over 300
million individuals globally, as reported by the World Health Organization. Early detection …

Mobile Phone Intelligent Recognition of Human Activity Based on Deep Neural Network

Z Zhao - Applied and Computational Engineering, 2024 - ewadirect.com
Because of the rapid development of smartphone sensor technology and the continuous
progress of machine learning algorithms, it has become possible to use smartphones for …