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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach
Md. Sabbir Ahmed, Nova Ahmed
JMIR Formative Research (2023) Vol. 7, pp. e28848-e28848
Open Access | Times Cited: 10

Showing 10 citing articles:

Depression Recognition Using Daily Wearable-Derived Physiological Data
Xinyu Shui, Hao Xu, Shuping Tan, et al.
Sensors (2025) Vol. 25, Iss. 2, pp. 567-567
Open Access | Times Cited: 1
Machine learning applied to digital phenotyping: A systematic literature review and taxonomy
Marília Pit dos Santos, Wesllei Felipe Heckler, Rodrigo Simon Bavaresco, et al.
Computers in Human Behavior (2024) Vol. 161, pp. 108422-108422
Closed Access | Times Cited: 3
Predicting dimensions of depression from smartphone data
Vincent Holstein, Samir Akre, Ramona Leenings, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access | Times Cited: 1
Predicting the Transition From Depression to Suicidal Ideation Using Facebook Data Among Indian-Bangladeshi Individuals: Protocol for a Cohort Study
Manoshi Das Turjo, Khushboo Suchit Mundada, Nuzhat Jabeen Haque, et al.
JMIR Research Protocols (2024) Vol. 13, pp. e55511-e55511
Open Access
Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study
Md. Sabbir Ahmed, Tanvir Hasan, Salekul Islam, et al.
JMIR Research Protocols (2024) Vol. 13, pp. e51540-e51540
Open Access
Early Depression Detection Using Modern AI Techniques
Sharmistha Dey, K. Singh
Advances in computational intelligence and robotics book series (2024), pp. 119-138
Closed Access
SAD: Self-assessment of depression for Bangladeshi university students using machine learning and NLP
Md Shawmoon Azad, Shakirul Islam Leeon, Riasat Khan, et al.
Array (2024) Vol. 25, pp. 100372-100372
Open Access
Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review
Emma Todd, Rebecca Orr, Elizabeth Gamage, et al.
Computers in Biology and Medicine (2024) Vol. 185, pp. 109521-109521
Open Access
How do machine learning models perform in the detection of depression, anxiety, and stress among undergraduate students? A systematic review
Bruno Luis Schaab, Prisla Ücker Calvetti, Sofia Hoffmann, et al.
Cadernos de Saúde Pública (2024) Vol. 40, Iss. 11
Open Access
Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study (Preprint)
Md. Sabbir Ahmed, Tanvir Hasan, Salekul Islam, et al.
(2023)
Open Access
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