Explainable Machine Learning for Predicting Incident Heart Failure from Routine Electronic Health Record Data
- DOI
- 10.1016/j.cardfail.2026.08.013
- Published
- 2026-11
- Container
- Journal of Cardiac Failure
- Publisher
- Elsevier BV
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1016/j.cardfail.2026.08.013,
title = {Explainable Machine Learning for Predicting Incident Heart Failure from Routine Electronic Health Record Data},
author = {Michael Van Ness and Alexander T. Sandhu and Tomas M. Bosschieter and Natasha Din and Andrew Ambrosy and Minh Nguyen and Madeleine Udell},
year = {2026},
journal = {Journal of Cardiac Failure},
doi = {10.1016/j.cardfail.2026.08.013},
url = {https://doi.org/10.1016/j.cardfail.2026.08.013}
}RIS
TY - JOUR TI - Explainable Machine Learning for Predicting Incident Heart Failure from Routine Electronic Health Record Data AU - Michael Van Ness AU - Alexander T. Sandhu AU - Tomas M. Bosschieter AU - Natasha Din AU - Andrew Ambrosy AU - Minh Nguyen AU - Madeleine Udell PY - 2026 JO - Journal of Cardiac Failure DO - 10.1016/j.cardfail.2026.08.013 UR - https://doi.org/10.1016/j.cardfail.2026.08.013 ER -
APA
Ness, M. V., Sandhu, A. T., Bosschieter, T. M., Din, N., Ambrosy, A., Nguyen, M., & Udell, M. (2026). Explainable Machine Learning for Predicting Incident Heart Failure from Routine Electronic Health Record Data. Journal of Cardiac Failure. https://doi.org/10.1016/j.cardfail.2026.08.013
Source records
- crossref · retrieved 2026-11-11T04:46:05.059Z