Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research.

Padmanabhan K, Lu M, Feng D, Kan-Dobrosky N, Konduri S, Litman HJ, Livieratos A

DOI: 10.1093/jamia/ocag077
Published: 2026 Aug 1
Container: Journal of the American Medical Informatics Association : JAMIA
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/jamia/ocag077,
  title = {Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research.},
  author = {Padmanabhan K and Lu M and Feng D and Kan-Dobrosky N and Konduri S and Litman HJ and Livieratos A},
  year = {2026},
  journal = {Journal of the American Medical Informatics Association : JAMIA},
  doi = {10.1093/jamia/ocag077},
  url = {https://doi.org/10.1093/jamia/ocag077}
}

RIS

TY  - JOUR
TI  - Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research.
AU  - Padmanabhan K
AU  - Lu M
AU  - Feng D
AU  - Kan-Dobrosky N
AU  - Konduri S
AU  - Litman HJ
AU  - Livieratos A
PY  - 2026
JO  - Journal of the American Medical Informatics Association : JAMIA
DO  - 10.1093/jamia/ocag077
UR  - https://doi.org/10.1093/jamia/ocag077
ER  -

APA

K, P., M, L., D, F., N, K., S, K., HJ, L., & A, L. (2026). Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research.. Journal of the American Medical Informatics Association : JAMIA. https://doi.org/10.1093/jamia/ocag077

Source records

pubmed · retrieved 2026-09-25T11:20:50.448Z