Andrew L. Beam

Orcid: 0000-0002-6657-2787

According to our database1, Andrew L. Beam authored at least 30 papers between 2014 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


Online presence:



Large Language Models in Mental Health Care: a Scoping Review.
CoRR, 2024

Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data.
CoRR, 2023

Conformal Prediction with Large Language Models for Multi-Choice Question Answering.
CoRR, 2023

TIER: Text-Image Entropy Regularization for Medical CLIP-style models.
Proceedings of the Machine Learning for Healthcare Conference, 2023

TIER: Text-Image Entropy Regularization for CLIP-style models.
CoRR, 2022

Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction.
CoRR, 2022

Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features.
CoRR, 2022

Deep Learning Methods for Proximal Inference via Maximum Moment Restriction.
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.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

Second opinion needed: communicating uncertainty in medical machine learning.
npj Digit. Medicine, 2021

Machine learning for patient risk stratification: standing on, or looking over, the shoulders of clinicians?
npj Digit. Medicine, 2021

Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures.
Entropy, 2021

MedSelect: Selective Labeling for Medical Image Classification Combining Meta-Learning with Deep Reinforcement Learning.
CoRR, 2021

Coarse-to-Fine Memory Matching for Joint Retrieval and Classification.
CoRR, 2020

Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data.
CoRR, 2020

Exemplar Auditing for Multi-Label Biomedical Text Classification.
CoRR, 2020

Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data.
Proceedings of the Pacific Symposium on Biocomputing 2020, 2020

Automated grouping of medical codes via multiview banded spectral clustering.
J. Biomed. Informatics, 2019

Feature extraction for phenotyping from semantic and knowledge resources.
J. Biomed. Informatics, 2019

Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes.
Proceedings of the Biocomputing 2019: Proceedings of the Pacific Symposium, 2019

Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different?
Proceedings of the Machine Learning for Health Workshop, 2019

Learning to Estimate Nutrition Facts from Food Descriptions.
Proceedings of the AMIA 2019, 2019

Machine Learning for Health (ML4H) Workshop at NeurIPS 2018.
CoRR, 2018

Opportunities in Machine Learning for Healthcare.
CoRR, 2018

Adversarial Attacks Against Medical Deep Learning Systems.
CoRR, 2018

Clinical Concept Embeddings Learned from Massive Sources of Medical Data.
CoRR, 2018

Improving EHR Chart Review Efficiency via Semantic Similarity Assessment.
Proceedings of the AMIA 2017, 2017

The Spectrum of Insomnia-Associated Comorbidities in an Electronic Medical Records Cohort.
Proceedings of the AMIA 2016, 2016

An investigation of gene-gene interactions in dose-response studies with Bayesian nonparametrics.
BioData Min., 2015

Bayesian neural networks for detecting epistasis in genetic association studies.
BMC Bioinform., 2014