Andrew L. Beam

Publication Activity (10 Years)

Years Active: 2014-2022
Publications (10 Years): 16

Top Topics

Acute Coronary Syndrome

Machine Learning

Hierarchical Structure

Top Venues

npj Digit. Medicine

Publications

Anil Palepu, Andrew L. Beam
TIER: Text-Image Entropy Regularization for CLIP-style models. CoRR (2022)
Anil Palepu, Andrew L. Beam
Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features. CoRR (2022)
Damir Vrabac, Akshay Smit, Yujie He, Andrew Y. Ng, Andrew L. Beam, Pranav Rajpurkar
MedSelect: Selective Labeling for Medical Image Classification Using Meta-Learning. MIDL (2022)
Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew L. Beam
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. CoRR (2022)
Benjamin Kompa, Jasper Snoek, Andrew L. Beam
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. Entropy 23 (12) (2021)
Akshay Smit, Damir Vrabac, Yujie He, Andrew Y. Ng, Andrew L. Beam, Pranav Rajpurkar
MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning. CoRR (2021)
Benjamin Kompa, Jasper Snoek, Andrew L. Beam
Second opinion needed: communicating uncertainty in medical machine learning. npj Digit. Medicine 4 (2021)
Brett K. Beaulieu-Jones, William Yuan, Gabriel A. Brat, Andrew L. Beam, Griffin M. Weber, Marshall Ruffin, Isaac S. Kohane
Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians? npj Digit. Medicine 4 (2021)
Andrew L. Beam, Benjamin Kompa, Allen Schmaltz, Inbar Fried, Griffin M. Weber, Nathan P. Palmer, Xu Shi, Tianxi Cai, Isaac S. Kohane
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data. PSB (2020)
David Bellamy, Leo Celi, Andrew L. Beam
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data. CoRR (2020)
Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam
Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes. PSB (2019)
Andrew L. Beam, Benjamin Kompa, Inbar Fried, Nathan P. Palmer, Xu Shi, Tianxi Cai, Isaac S. Kohane
Clinical Concept Embeddings Learned from Massive Sources of Medical Data. CoRR (2018)
Samuel G. Finlayson, Isaac S. Kohane, Andrew L. Beam
Adversarial Attacks Against Medical Deep Learning Systems. CoRR (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
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. CoRR (2018)
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath
Opportunities in Machine Learning for Healthcare. CoRR (2018)
Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam
Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes. CoRR (2018)
Andrew L. Beam, Alison A. Motsinger-Reif, Jon Doyle
An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics. BioData Min. 8 (2015)
Andrew L. Beam, Alison A. Motsinger-Reif, Jon Doyle
Bayesian neural networks for detecting epistasis in genetic association studies. BMC Bioinform. 15 (2014)