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2020 – today
- 2024
[i18]Yining Hua, Fenglin Liu, Kailai Yang, Zehan Li, Yi-han Sheu, Peilin Zhou, Lauren V. Moran, Sophia Ananiadou, Andrew Beam:
Large Language Models in Mental Health Care: a Scoping Review. CoRR abs/2401.02984 (2024)- [i17]Joe B. Hakim, Jeffery L. Painter, Darmendra Ramcharran, Vijay Kara, Greg Powell, Paulina Sobczak, Chiho Sato, Andrew Bate, Andrew Beam:
The Need for Guardrails with Large Language Models in Medical Safety-Critical Settings: An Artificial Intelligence Application in the Pharmacovigilance Ecosystem. CoRR abs/2407.18322 (2024) - 2023
- [c9]Anil Palepu, Andrew Beam:
TIER: Text-Image Entropy Regularization for Medical CLIP-style models. MLHC 2023: 548-564 - [e1]Bobak J. Mortazavi, Tasmie Sarker, Andrew Beam, Joyce C. Ho:
Conference on Health, Inference, and Learning, CHIL 2023, Broad Institute of MIT and Harvard (Merkin Building), 415 Main Street, Cambridge, MA, USA. Proceedings of Machine Learning Research 209, PMLR 2023 [contents] - [i16]Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David R. Bellamy, Ramesh Raskar, Andrew Beam:
Conformal Prediction with Large Language Models for Multi-Choice Question Answering. CoRR abs/2305.18404 (2023) - [i15]David R. Bellamy, Bhawesh Kumar, Cindy Wang
, Andrew Beam:
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data. CoRR abs/2312.11502 (2023) - 2022
- [c8]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: 1301-1310 - [c7]Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew Beam:
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. NeurIPS 2022 - [i14]Benjamin Kompa, David R. Bellamy, Thomas Kolokotrones, James M. Robins, Andrew L. Beam:
Deep Learning Methods for Proximal Inference via Maximum Moment Restriction. CoRR abs/2205.09824 (2022) - [i13]Anil Palepu, Andrew L. Beam:
Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features. CoRR abs/2206.07155 (2022) - [i12]Bhawesh Kumar, Anil Palepu, Rudraksh Tuwani, Andrew Beam:
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction. CoRR abs/2210.15805 (2022) - [i11]Anil Palepu, Andrew L. Beam:
TIER: Text-Image Entropy Regularization for CLIP-style models. CoRR abs/2212.06710 (2022) - 2021
- [j7]Benjamin Kompa, Jasper Snoek, Andrew L. Beam:
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. Entropy 23(12): 1608 (2021) - [j6]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) - [j5]Benjamin Kompa, Jasper Snoek, Andrew L. Beam:
Second opinion needed: communicating uncertainty in medical machine learning. npj Digit. Medicine 4 (2021) - [i10]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 abs/2103.14339 (2021) - 2020
- [c6]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: 295-306 - [i9]Allen Schmaltz, Andrew Beam:
Exemplar Auditing for Multi-Label Biomedical Text Classification. CoRR abs/2004.03093 (2020) - [i8]David R. Bellamy, Leo A. Celi, Andrew L. Beam:
Evaluating Progress on Machine Learning for Longitudinal Electronic Healthcare Data. CoRR abs/2010.01149 (2020) - [i7]Benjamin Kompa, Jasper Snoek, Andrew Beam:
Empirical Frequentist Coverage of Deep Learning Uncertainty Quantification Procedures. CoRR abs/2010.03039 (2020) - [i6]Allen Schmaltz, Andrew Beam:
Coarse-to-Fine Memory Matching for Joint Retrieval and Classification. CoRR abs/2012.02287 (2020)
2010 – 2019
- 2019
- [j4]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:
Feature extraction for phenotyping from semantic and knowledge resources. J. Biomed. Informatics 91 (2019) - [j3]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:
Automated grouping of medical codes via multiview banded spectral clustering. J. Biomed. Informatics 100 (2019) - [c5]Hadi Amiri, Andrew Beam, Isaac S. Kohane:
Learning to Estimate Nutrition Facts from Food Descriptions. AMIA 2019 - [c4]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:
Machine Learning for Health ( ML4H ) 2019 : What Makes Machine Learning in Medicine Different? ML4H@NeurIPS 2019: 1-9 - [c3]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: 8-17 - 2018
- [i5]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 abs/1804.01486 (2018) - [i4]Samuel G. Finlayson, Isaac S. Kohane, Andrew L. Beam:
Adversarial Attacks Against Medical Deep Learning Systems. CoRR abs/1804.05296 (2018) - [i3]Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L. Beam, Rajesh Ranganath:
Opportunities in Machine Learning for Healthcare. CoRR abs/1806.00388 (2018) - [i2]Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam:
Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes. CoRR abs/1811.01294 (2018) - [i1]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 abs/1811.07216 (2018) - 2017
- [c2]Tianrun A. Cai, Andrew Beam, Stephanie Chan, Jacqueline Honerlaw, Yichi Zhang, David R. Gagnon, Kelly Cho, J. Michael Gaziano, Katherine P. Liao, Tianxi Cai:
Improving EHR Chart Review Efficiency via Semantic Similarity Assessment. AMIA 2017 - 2016
- [c1]Uri Kartoun, Andrew Beam, Jennifer Pai, Arnaub Chatterjee, Timothy P. Fitzgerald, Isaac S. Kohane, Stanley Y. Shaw:
The Spectrum of Insomnia-Associated Comorbidities in an Electronic Medical Records Cohort. AMIA 2016 - 2015
- [j2]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: 6 (2015) - 2014
- [j1]Andrew L. Beam, Alison A. Motsinger-Reif, Jon Doyle:
Bayesian neural networks for detecting epistasis in genetic association studies. BMC Bioinform. 15: 368 (2014)
Coauthor Index
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last updated on 2024-10-07 21:12 CEST by the dblp team
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