Andrew L. Beam

According to our database, Andrew L. Beam authored at least 30 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

2023

Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data.

David R. Bellamy, Bhawesh Kumar, Cindy Wang, Andrew Beam

CoRR, 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

TIER: Text-Image Entropy Regularization for Medical CLIP-style models.

Anil Palepu, Andrew Beam

Proceedings of the Machine Learning for Healthcare Conference, 2023

2022

TIER: Text-Image Entropy Regularization for CLIP-style models.

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

Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features.

Anil Palepu, Andrew L. Beam

CoRR, 2022

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction.

Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew Beam

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

MedSelect: Selective Labeling for Medical Image Classification Using Meta-Learning.

Damir Vrabac, Akshay Smit, Yujie He, Andrew Y. Ng, Andrew L. Beam, Pranav Rajpurkar

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

2021

Second opinion needed: communicating uncertainty in medical machine learning.

Benjamin Kompa, Jasper Snoek, Andrew L. Beam

npj Digit. Medicine, 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

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures.

Benjamin Kompa, Jasper Snoek, Andrew L. Beam

Entropy, 2021

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

2020

Coarse-to-Fine Memory Matching for Joint Retrieval and 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

Exemplar Auditing for Multi-Label Biomedical Text Classification.

Allen Schmaltz, Andrew Beam

CoRR, 2020

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

Proceedings of the Pacific Symposium on Biocomputing 2020, 2020

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, Tianxi Cai

J. Biomed. Informatics, 2019

Feature extraction for phenotyping from semantic and knowledge resources.

Wenxin Ning, Stephanie Chan, Andrew Beam, Ming Yu, Alon Geva, Katherine P. Liao, Mary Mullen, Kenneth D. Mandl, Isaac S. Kohane, Tianxi Cai, Sheng Yu

J. Biomed. Informatics, 2019

Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes.

Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam

Proceedings of the Biocomputing 2019: Proceedings of the Pacific Symposium, 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

Proceedings of the Machine Learning for Health Workshop, 2019

Learning to Estimate Nutrition Facts from Food Descriptions.

Hadi Amiri, Andrew Beam, Isaac S. Kohane

Proceedings of the AMIA 2019, 2019

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, Jasvinder S. Kandola, Matthew B. A. McDermott, Tristan Naumann, Peter Schulam, Farah Shamout, Alexandre Yahi

CoRR, 2018

Opportunities in Machine Learning for Healthcare.

Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath

CoRR, 2018

Adversarial Attacks Against Medical Deep Learning Systems.

Samuel G. Finlayson, Isaac S. Kohane, Andrew L. Beam

CoRR, 2018

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

2017

Improving EHR Chart Review Efficiency via Semantic Similarity Assessment.

Tianrun A. Cai, Andrew Beam, Stephanie Chan, Jacqueline Honerlaw, Yichi Zhang, David R. Gagnon, Kelly Cho, J. Michael Gaziano, Katherine P. Liao, Tianxi Cai

Proceedings of the AMIA 2017, 2017

2016

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

Proceedings of the AMIA 2016, 2016

2015

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

2014

Bayesian neural networks for detecting epistasis in genetic association studies.

Andrew L. Beam, Alison A. Motsinger-Reif, Jon Doyle

BMC Bioinform., 2014