Cargando…
An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials
Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential...
Autores principales: | Oikonomou, Evangelos K., Thangaraj, Phyllis M., Bhatt, Deepak L., Ross, Joseph S., Young, Lawrence H., Krumholz, Harlan M., Suchard, Marc A., Khera, Rohan |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10673945/ https://www.ncbi.nlm.nih.gov/pubmed/38001154 http://dx.doi.org/10.1038/s41746-023-00963-z |
Ejemplares similares
-
An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials
por: Oikonomou, Evangelos K, et al.
Publicado: (2023) -
Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials
por: Oikonomou, Evangelos K, et al.
Publicado: (2022) -
Machine learning in precision diabetes care and cardiovascular risk prediction
por: Oikonomou, Evangelos K., et al.
Publicado: (2023) -
Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices
por: Khunte, Akshay, et al.
Publicado: (2023) -
Use of Wearable Devices in Individuals With or at Risk for Cardiovascular Disease in the US, 2019 to 2020
por: Dhingra, Lovedeep S., et al.
Publicado: (2023)