253 citations · 330 across the 13 of their papers we have counts for
13 papers
Doubly Robust Monte Carlo Tree Search
Manqing Liu, Andrew L. Beam
We present Doubly Robust Monte Carlo Tree Search (DR-MCTS), a novel algorithm that integrates Doubly Robust (DR) off-policy estimation into Monte Carlo Tree Search (MCTS) to enhanc…
Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data
Sophia M Rein, Jing Li, Miguel Hernan +1
The g-formula can be used to estimate causal effects of sustained treatment strategies using observational data under the identifying assumptions of consistency, positivity, and ex…
DAG-aware Transformer for Causal Effect Estimation
Manqing Liu, David R. Bellamy, Andrew L. Beam
Causal inference is a critical task across fields such as healthcare, economics, and the social sciences. While recent advances in machine learning, especially those based on the d…
The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem
Joe B Hakim, Jeffery L Painter, Darmendra Ramcharran +6
Large language models (LLMs) are useful tools with the capacity for performing specific types of knowledge work at an effective scale. However, LLM deployments in high-risk and saf…
Large Language Models in Mental Health Care: a Scoping Review
Yining Hua, Fenglin Liu, Kailai Yang +9
Objectieve:This review aims to deliver a comprehensive analysis of Large Language Models (LLMs) utilization in mental health care, evaluating their effectiveness, identifying chall…
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
David R. Bellamy, Bhawesh Kumar, Cindy Wang +1
In this work we introduce Labrador, a pre-trained Transformer model for laboratory data. Labrador and BERT were pre-trained on a corpus of 100 million lab test results from electro…