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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:

Finding the Best Match — a Case Study on the (Text-)Feature and Model Choice in Digital Mental Health Interventions
Kirsten Zantvoort, Jonas Scharfenberger, Leif Boß, et al.
Journal of Healthcare Informatics Research (2023) Vol. 7, Iss. 4, pp. 447-479
Open Access | Times Cited: 13

Showing 13 citing articles:

Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions
Kirsten Zantvoort, Nils Hentati Isacsson, Burkhardt Funk, et al.
Digital Health (2024) Vol. 10
Open Access | Times Cited: 5
A procedural overview of why, when and how to use machine learning for psychiatry
Christopher Lucasius, Muhammad Amjad Ali, Tejal Patel, et al.
Nature Mental Health (2025) Vol. 3, Iss. 1, pp. 8-18
Closed Access
Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing
Silvan Hornstein, Jonas Scharfenberger, Ulrike Lueken, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 3
Sentiment Analysis of Google Classroom Application Using Machine Learning Techniques
Kiko Taqiy Shandana, Afrig Aminuddin, Erik Hadi Saputra, et al.
(2023), pp. 954-959
Closed Access | Times Cited: 4
Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions
Kirsten Zantvoort, Barbara Nacke, Dennis Görlich, et al.
npj Digital Medicine (2024) Vol. 7, Iss. 1
Open Access | Times Cited: 1
A Machine Learning Based Momentum Prediction Model for Tennis Matches
Siqi Bo, Liqiang Diao, Xinyu Liu, et al.
(2024), pp. 162-166
Closed Access
Making the Most Out of Timeseries Symptom Data: A Machine Learning Study on Symptom Predictions of Internet-Based Cbt
Nils Hentati Isacsson, Kirsten Zantvoort, Erik Forsell, et al.
(2024)
Closed Access
Predicting Satisfaction with Chat-Counseling at a 24/7 Chat Hotline for the Youth: A Natural Language Processing Study (Preprint)
Silvan Hornstein, Ulrike Lueken, Richard Wundrack, et al.
(2024)
Closed Access
Making the most out of timeseries symptom data: A machine learning study on symptom predictions of internet-based CBT
Nils Hentati Isacsson, Kirsten Zantvoort, Erik Forsell, et al.
Internet Interventions (2024) Vol. 38, pp. 100773-100773
Open Access
Predicting Satisfaction with Chat-Counseling at a 24/7 Chat Hotline for the Youth: A Natural Language Processing Study (Preprint)
Silvan Hornstein, Ulrike Lueken, Richard Wundrack, et al.
JMIR AI (2024) Vol. 4, pp. e63701-e63701
Open Access
Predicting Recurrent Chat Contact in a Low-Threshold Psychological Intervention for Children and Young Adults: A Natural Language Processing Approach
Silvan Hornstein, Jonas Scharfenberger, Ulrike Lueken, et al.
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
Which Smartphone-Based Sensing Features Matter in Depression Prediction? Results from an observation study. (Preprint)
Yannik Terhorst, Eva-Maria Meßner, Kennedy Opoku Asare, et al.
(2023)
Closed Access
Which Smartphone-Based Sensing Features Matter in Depression Severity Prediction? Results from an Observation Study. (Preprint)
Yannik Terhorst, Eva-Maria Meßner, Kennedy Opoku Asare, et al.
Journal of Medical Internet Research (2023)
Open Access
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