10 citations · 30 across the 6 of their papers we have counts for
6 papers
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Jiageng Wu, Bowen Gu, Ren Zhou +14
Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, benchmarking on l…
High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates
Janick Weberpals, Pamela A. Shaw, Kueiyu Joshua Lin +17
Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop a…
A Causal Roadmap for Generating High-Quality Real-World Evidence
Lauren E Dang, Susan Gruber, Hana Lee +23
Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks…
Collaborative-controlled LASSO for Constructing Propensity Score-based Estimators in High-Dimensional Data
Cheng Ju, Richard Wyss, Jessica M. Franklin +3
Propensity score (PS) based estimators are increasingly used for causal inference in observational studies. However, model selection for PS estimation in high-dimensional data has…
Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods
Cheng Ju, Mary Combs, Samuel D Lendle +4
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses c…
Scalable Collaborative Targeted Learning for High-Dimensional Data
Cheng Ju, Susan Gruber, Samuel D. Lendle +5
Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typ…