Automated interpretable discovery of heterogeneous treatment effectiveness: A COVID-19 case study

Testing multiple treatments for heterogeneous (varying) effectiveness with respect to many underlying risk factors requires many pairwise tests; we would like to instead automatically discover and visualize patient archetypes and predictors of treatment effectiveness using multitask machine learning...

Descripción completa

Detalles Bibliográficos
Autores principales: Lengerich, Benjamin J., Nunnally, Mark E., Aphinyanaphongs, Yin, Ellington, Caleb, Caruana, Rich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2022
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9055753/
https://www.ncbi.nlm.nih.gov/pubmed/35504543
http://dx.doi.org/10.1016/j.jbi.2022.104086

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9055753/
https://www.ncbi.nlm.nih.gov/pubmed/35504543
http://dx.doi.org/10.1016/j.jbi.2022.104086

Ejemplares similares