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…
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
Bhawesh Kumar, Charlie Lu, Gauri Gupta +4
As large language models continue to be widely developed, robust uncertainty quantification techniques will become crucial for their safe deployment in high-stakes scenarios. In th…
TIER: Text-Image Entropy Regularization for CLIP-style models
Anil Palepu, Andrew L. Beam
In this paper, we introduce a novel regularization scheme on contrastive language-image pre-trained (CLIP) medical vision models. Our approach is based on the observation that on m…
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction
Bhawesh Kumar, Anil Palepu, Rudraksh Tuwani +1
Self-supervised models trained with a contrastive loss such as CLIP have shown to be very powerful in zero-shot classification settings. However, to be used as a zero-shot classifi…
Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features
Anil Palepu, Andrew L Beam
Deep learning models trained in a fully supervised manner have been shown to rely on so-called "shortcut" features. Shortcut features are inputs that are associated with the outcom…
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning
Akshay Smit, Damir Vrabac, Yujie He +3
We propose a selective learning method using meta-learning and deep reinforcement learning for medical image interpretation in the setting of limited labeling resources. Our method…
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data
David Bellamy, Leo Celi, Andrew L. Beam
The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled oth…
A Review of Challenges and Opportunities in Machine Learning for Health
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam +3
Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. Howe…