2 citations · 4 across the 4 of their papers we have counts for
5 papers
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes
Richard D Riley, Gary S Collins, Lucinda Archer +9
Background: When developing a clinical prediction model using time-to-event data, previous research focuses on the sample size to minimise overfitting and precisely estimate the ov…
Programmable Interface for Statistical & Simulation Models (PRISM): Towards Greater Accessibility of Clinical and Healthcare Decision Models
Amin Adibi, Stephanie Harvard, Mohsen Sadatsafavi
Background: Increasingly, decision-making in healthcare relies on computer models, be it clinical prediction models at point of care or decision-analytic models at the policymaking…
Concentration of Benefit index: A threshold-free summary metric for quantifying the capacity of covariates to yield efficient treatment rules
Mohsen Sadatsafavi, Mohammad Ali Mansournia, Paul Gustafson
When data on treatment assignment, outcomes, and covariates from a randomized trial are available, a question of interest is to what extent covariates can be used to optimize treat…
A threshold-free summary index for quantifying the capacity of covariates to yield efficient treatment rules
Mohsen Sadatsafavi, Mohammad Mansournia, Paul Gustafson
The focus of this paper is on quantifying the capacity of covariates in devising efficient treatment rules when data from a randomized trial are available. Conventional one-variabl…