From marginal gains to clinical utility: machine learning-based percutaneous coronary intervention risk prediction models

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dc.contributor.authorQadir, Muhammad Ibtsaam
dc.contributor.authorHira, Ravi S.
dc.contributor.authorKolbinger, Fiona R.
dc.contributor.departmentBiostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
dc.date.accessioned2025-04-18T11:28:42Z
dc.date.available2025-04-18T11:28:42Z
dc.date.issued2025-01-16
dc.eprint.versionFinal published version
dc.identifier.citationQadir MI, Hira RS, Kolbinger FR. From marginal gains to clinical utility: machine learning-based percutaneous coronary intervention risk prediction models. Eur Heart J Digit Health. 2025;6(2):159-161. Published 2025 Jan 16. doi:10.1093/ehjdh/ztaf001
dc.identifier.urihttps://hdl.handle.net/1805/47168
dc.language.isoen_US
dc.publisherOxford University Press
dc.relation.isversionof10.1093/ehjdh/ztaf001
dc.relation.journalEuropean Heart Journal: Digital Health
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.sourcePMC
dc.subjectPercutaneous coronary intervention (PCI)
dc.subjectPost-procedural complications
dc.subjectArtificial intelligence (AI)
dc.titleFrom marginal gains to clinical utility: machine learning-based percutaneous coronary intervention risk prediction models
dc.typeArticle
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