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

Tools, Programs and Algorithms (358) Digital Health Reviews (1652) Machine Learning (1971)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38266, first published .
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine

Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine

Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine

Authors of this article:

Pulakesh Upadhyaya1, 2 Author Orcid Image ;   Kai Zhang1 Author Orcid Image ;   Can Li1 Author Orcid Image ;   Xiaoqian Jiang1 Author Orcid Image ;   Yejin Kim1 Author Orcid Image
Article Authors Cited by (8) Tweetations (7) Metrics

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  1. Colineaux H, Lepage B, Chauvin P, Dimeglio C, Delpierre C, Lefèvre T. Contribution of Structure Learning Algorithms in Social Epidemiology: Application to Real-World Data. International Journal of Environmental Research and Public Health 2025;22(3):348 View
  2. Fu M, Sankararaman S, Vossel K, Chang T. Identifying Common Disease Trajectories of Progressive Supranuclear Palsy with Electronic Health Records. Movement Disorders Clinical Practice 2025 View
  3. Fu M, Sankararaman S, Pasaniuc B, Vossel K, Chang T. Identifying common disease trajectories of Alzheimer’s disease with electronic health records. eBioMedicine 2025;118:105831 View
  4. Mulomba C, Kiketa V, Kutangila D, Mampuya P, Mukenze J, Kasunzi L, Kyamakya K, Tashev T, Kasereka S. Applying Causal Machine Learning to Spatiotemporal Data Analysis: An Investigation of Opportunities and Challenges. IEEE Access 2025;13:141832 View

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  3. Hou Y, Rabbani S, Hong L, Diawara N, Samad M. 2025 IEEE International Conference on Information Reuse and Integration and Data Science (IRI). Causal Explainability of Machine Learning in Heart Failure Prediction from Electronic Health Records View