Explainable temporal machine learning of multimorbidity trajectories after acute myocardial infarction: complementing clinical risk scores with mechanistic phenotypes
- DOI
- 10.1093/jamia/ocag135
- Published
- 2026-08-06
- Container
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1093/jamia/ocag135,
title = {Explainable temporal machine learning of multimorbidity trajectories after acute myocardial infarction: complementing clinical risk scores with mechanistic phenotypes},
author = {Anthony Onoja and Kris Elomaa and Anthony D Whetton and Nophar Geifman},
year = {2026},
journal = {Journal of the American Medical Informatics Association},
doi = {10.1093/jamia/ocag135},
url = {https://doi.org/10.1093/jamia/ocag135}
}RIS
TY - JOUR TI - Explainable temporal machine learning of multimorbidity trajectories after acute myocardial infarction: complementing clinical risk scores with mechanistic phenotypes AU - Anthony Onoja AU - Kris Elomaa AU - Anthony D Whetton AU - Nophar Geifman PY - 2026 JO - Journal of the American Medical Informatics Association DO - 10.1093/jamia/ocag135 UR - https://doi.org/10.1093/jamia/ocag135 ER -
APA
Onoja, A., Elomaa, K., Whetton, A. D., & Geifman, N. (2026). Explainable temporal machine learning of multimorbidity trajectories after acute myocardial infarction: complementing clinical risk scores with mechanistic phenotypes. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocag135
Source records
- crossref · retrieved 2026-09-25T08:36:33.352Z