Bayesian neural networks for detecting epistasis in genetic association studies. - Andrew L. Beam; Alison A. Motsinger-Reif; Jon Doyle (BMC Bioinform., 2014)

An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics. - Andrew L. Beam; Alison A. Motsinger-Reif; Jon Doyle (BioData Min., 2015)

The Spectrum of Insomnia-Associated Comorbidities in an Electronic Medical Records Cohort. - Uri Kartoun; Andrew Beam; Jennifer Pai; Arnaub Chatterjee; Timothy P. Fitzgerald; Isaac S. Kohane; Stanley Y. Shaw (AMIA, 2016)

Improving EHR Chart Review Efficiency via Semantic Similarity Assessment. - Tianrun A. Cai; Andrew Beam; Stephanie Chan 002; Jacqueline Honerlaw; Yichi Zhang; David R. Gagnon; Kelly Cho; J. Michael Gaziano; Katherine P. Liao; Tianxi Cai (AMIA, 2017)

Clinical Concept Embeddings Learned from Massive Sources of Medical Data. - Andrew L. Beam; Benjamin Kompa; Inbar Fried; Nathan P. Palmer; Xu Shi; Tianxi Cai; Isaac S. Kohane (CoRR, 2018)

Adversarial Attacks Against Medical Deep Learning Systems. - Samuel G. Finlayson; Isaac S. Kohane; Andrew L. Beam (CoRR, 2018)

Opportunities in Machine Learning for Healthcare. - Marzyeh Ghassemi; Tristan Naumann; Peter Schulam; Andrew L. Beam; Rajesh Ranganath (CoRR, 2018)

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. - Natalia Antropova; Andrew L. Beam; Brett K. Beaulieu-Jones; Irene Chen; Corey Chivers; Adrian V. Dalca; Samuel G. Finlayson; Madalina Fiterau; Jason Alan Fries; Marzyeh Ghassemi; Mike Hughes; Bruno Jedynak; et al. (CoRR, 2018)

Learning to Estimate Nutrition Facts from Food Descriptions. - Hadi Amiri; Andrew Beam; Isaac S. Kohane (AMIA, 2019)

Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? - Adrian V. Dalca; Matthew B. A. McDermott; Emily Alsentzer; Samuel G. Finlayson; Michael Oberst; Fabian Falck; Corey Chivers; Andrew Beam; Tristan Naumann; Brett K. Beaulieu-Jones (ML4H@NeurIPS, 2019)

Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes. - Brett K. Beaulieu-Jones; Isaac S. Kohane; Andrew L. Beam (PSB, 2019)

Feature extraction for phenotyping from semantic and knowledge resources. - Wenxin Ning; Stephanie Chan 002; Andrew Beam; Ming Yu 003; Alon Geva; Katherine P. Liao; Mary Mullen; Kenneth D. Mandl; Isaac S. Kohane; Tianxi Cai; Sheng Yu 002 (J. Biomed. Informatics, 2019)

Automated grouping of medical codes via multiview banded spectral clustering. - Luwan Zhang; Yichi Zhang; Tianrun A. Cai; Yuri Ahuja; Zeling He; Yuk-Lam Ho; Andrew Beam; Kelly Cho; Robert J. Carroll; Joshua C. Denny; Isaac S. Kohane; Katherine P. Liao; et al. (J. Biomed. Informatics, 2019)

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data. - Andrew L. Beam; Benjamin Kompa; Allen Schmaltz; Inbar Fried; Griffin M. Weber; Nathan P. Palmer; Xu Shi; Tianxi Cai; Isaac S. Kohane (PSB, 2020)

Exemplar Auditing for Multi-Label Biomedical Text Classification. - Allen Schmaltz; Andrew Beam (CoRR, 2020)

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data. - David R. Bellamy; Leo A. Celi; Andrew L. Beam (CoRR, 2020)

Coarse-to-Fine Memory Matching for Joint Retrieval and Classification. - Allen Schmaltz; Andrew Beam (CoRR, 2020)

MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning. - Akshay Smit; Damir Vrabac; Yujie He; Andrew Y. Ng; Andrew L. Beam; Pranav Rajpurkar (CoRR, 2021)

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. - Benjamin Kompa; Jasper Snoek; Andrew L. Beam (Entropy, 2021)

Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians? - Brett K. Beaulieu-Jones; William Yuan; Gabriel A. Brat; Andrew L. Beam; Griffin M. Weber; Marshall Ruffin; Isaac S. Kohane (npj Digit. Medicine, 2021)

Second opinion needed: communicating uncertainty in medical machine learning. - Benjamin Kompa; Jasper Snoek; Andrew L. Beam (npj Digit. Medicine, 2021)

MedSelect: Selective Labeling for Medical Image Classification Using Meta-Learning. - Damir Vrabac; Akshay Smit; Yujie He; Andrew Y. Ng; Andrew L. Beam; Pranav Rajpurkar (MIDL, 2022)

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. - Benjamin Kompa; David R. Bellamy; Thomas Kolokotrones; James M. Robins; Andrew Beam (NeurIPS, 2022)

Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features. - Anil Palepu; Andrew L. Beam (CoRR, 2022)

Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction. - Bhawesh Kumar; Anil Palepu; Rudraksh Tuwani; Andrew Beam (CoRR, 2022)

TIER: Text-Image Entropy Regularization for CLIP-style models. - Anil Palepu; Andrew L. Beam (CoRR, 2022)

TIER: Text-Image Entropy Regularization for Medical CLIP-style models. - Anil Palepu; Andrew Beam (MLHC, 2023)

Conformal Prediction with Large Language Models for Multi-Choice Question Answering. - Bhawesh Kumar; Charlie Lu; Gauri Gupta; Anil Palepu; David R. Bellamy; Ramesh Raskar; Andrew Beam (CoRR, 2023)

Labrador: Exploring the limits of masked language modeling for laboratory data. - David R. Bellamy; Bhawesh Kumar; Cindy Wang; Andrew Beam (ML4H@NeurIPS, 2024)

Large Language Models in Mental Health Care: a Scoping Review. - Yining Hua; Fenglin Liu; Kailai Yang; Zehan Li; Yi-han Sheu; Peilin Zhou; Lauren V. Moran; Sophia Ananiadou; Andrew Beam (CoRR, 2024)

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; Vijay Kara; Greg Powell; Paulina Sobczak; Chiho Sato; Andrew Bate; Andrew Beam (CoRR, 2024)

DAG-aware Transformer for Causal Effect Estimation. - Manqing Liu; David R. Bellamy; Andrew L. Beam (CoRR, 2024)

Deep Learning Methods for the Noniterative Conditional Expectation G-Formula for Causal Inference from Complex Observational Data. - Sophia M. Rein; Jing Li; Miguel Hernan; Andrew Beam (CoRR, 2024)

Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy. - Neslihan Dilruba Koseoglu; Eric Chen; Rudraksh Tuwani; Benjamin Kompa; Stephanie M. Cox; M. Cuneyt Ozmen; Mina Massaro-Giordano; Andrew L. Beam; Pedram Hamrah (npj Digit. Medicine, 2025)

Doubly Robust Monte Carlo Tree Search. - Manqing Liu; Andrew L. Beam (CoRR, 2025)