Challenges in developing and validating machine learning models for TAVI mortality risk prediction: reply.

Leha, Andreas; Huber, Cynthia; Friede, Tim; Bauer, Timm; Beckmann, Andreas; Bekeredjian, Raffi; Bleiziffer, Sabine; Herrmann, Eva; Möllmann, Helge; Walther, Thomas; Beyersdorf, Friedhelm; Hamm, Christian; Künzi, Arnaud; Windecker, Stephan; Stortecky, Stefan; Kutschka, Ingo; Hasenfuß, Gerd; Ensminger, Stephan; Frerker, Christian and Seidler, Tim (2024). Challenges in developing and validating machine learning models for TAVI mortality risk prediction: reply. European heart journal. Digital health, 5(1), pp. 3-5. Oxford University Press 10.1093/ehjdh/ztad065

[img]
Preview
Text
ztad065.pdf - Published Version
Available under License Creative Commons: Attribution-Noncommercial (CC-BY-NC).

Download (159kB) | Preview

Item Type:

Journal Article (Further Contribution)

Division/Institute:

04 Faculty of Medicine > Department of Cardiovascular Disorders (DHGE) > Clinic of Cardiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Department of Clinical Research (DCR)

UniBE Contributor:

Künzi, Arnaud Yi-Yao, Windecker, Stephan, Stortecky, Stefan

Subjects:

600 Technology > 610 Medicine & health

ISSN:

2634-3916

Publisher:

Oxford University Press

Language:

English

Submitter:

Pubmed Import

Date Deposited:

24 Jan 2024 16:13

Last Modified:

20 Feb 2024 14:15

Publisher DOI:

10.1093/ehjdh/ztad065

PubMed ID:

38264698

BORIS DOI:

10.48350/192100

URI:

https://boris.unibe.ch/id/eprint/192100

Actions (login required)

Edit item Edit item
Provide Feedback