Bayesian Machine Learning Model Guiding Iterative, Personalized Anticoagulant Dosing Decision-Making
Adele represents a state-of-the-art Bayesian framework, using clinical factors and PK data, to enable longitudinally adaptive, patient-centric, preference-weighted predictions.
Authors
- Eva-Maria Fronk
- Martin Unverdorben (ORCID: https://orcid.org/0000-0002-3601-8187)
- Elliott M. Antman (ORCID: https://orcid.org/0000-0002-0808-9199)
- Robert P. Giugliano (ORCID: https://orcid.org/0000-0003-4110-7675)
- Cathy Chen (ORCID: https://orcid.org/0000-0001-8120-1362)
- Jacqueline Buros‐Novik
- Juho Timonen (ORCID: https://orcid.org/0000-0003-2341-6765)
- Matthew M. Clasen (ORCID: https://orcid.org/0000-0001-7137-6492)
- Eric Novik
- C. Michael Gibson
- Eugene Braunwald
Institutions
- Brigham and Women's Hospital (US)
- Daiichi Sankyo (Germany) (DE)
- Barnes & Noble (United States) (US)
- Baim Institute for Clinical Research (US)
- Thrombolysis in Myocardial Infarction Study Group (US)
- Daiichi Sankyo (United States) (US)
- New York University (US)
Publication Details
- Journal
- JACC Advances
- Published
- 2025-12-09
- DOI
- https://doi.org/10.1016/j.jacadv.2025.102504
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00