Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

BACKGROUND: Decision making for the “best” treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding event...

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Detalles Bibliográficos
Autores principales: Meid, Andreas D., Wirbka, Lucas, Groll, Andreas, Haefeli, Walter E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Original Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189725/
https://www.ncbi.nlm.nih.gov/pubmed/34911402
http://dx.doi.org/10.1177/0272989X211064604

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189725/
https://www.ncbi.nlm.nih.gov/pubmed/34911402
http://dx.doi.org/10.1177/0272989X211064604

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