This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2407) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (563) mHealth for Data Collection and Research (954) Depression and Mood Disorders; Suicide Prevention (1360) Artificial Intelligence (1363) Diagnostic Tools in Mental Health (242) Machine Learning (1459)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28848, first published .
A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach

Authors of this article:

Md Sabbir Ahmed1 Author Orcid Image ;   Nova Ahmed1 Author Orcid Image
Article Authors Cited by (4) Tweetations (3) Metrics

Journals

  1. Ahmed M, Hasan T, Islam S, Ahmed N. Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study. JMIR Research Protocols 2024;13:e51540 View
  2. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  3. Turjo M, Mundada K, Haque N, Ahmed N. Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study. JMIR Research Protocols 2024;13:e55511 View

Books/Policy Documents

  1. Dey S, Singh K. Exploring the Micro World of Robotics Through Insect Robots. View