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Matthew B. A. McDermott
Matthew B. A. McDermott
Postdoctoral Researcher, Harvard Medical School, Department of Biomedical Informatics
Vahvistettu sähköpostiosoite verkkotunnuksessa hms.harvard.edu
Nimike
Viittaukset
Viittaukset
Vuosi
Publicly available clinical BERT embeddings
E Alsentzer, JR Murphy, W Boag, WH Weng, D Jin, T Naumann, ...
Proceedings of the 2nd Clinical Natural Language Processing Workshop 2 (W19 …, 2019
25452019
Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
L Seyyed-Kalantari, H Zhang, MBA McDermott, IY Chen, M Ghassemi
Nature medicine 27 (12), 2176-2182, 2021
5672021
CheXclusion: Fairness gaps in deep chest X-ray classifiers
L Seyyed-Kalantari, G Liu, M McDermott, IY Chen, M Ghassemi
BIOCOMPUTING 2021: proceedings of the Pacific symposium, 232-243, 2020
3342020
Clinically accurate chest x-ray report generation
G Liu, TMH Hsu, M McDermott, W Boag, WH Weng, P Szolovits, ...
Machine Learning for Healthcare Conference, 249-269, 2019
3142019
Reproducibility in machine learning for health research: Still a ways to go
MBA McDermott, S Wang, N Marinsek, R Ranganath, L Foschini, ...
Science Translational Medicine 13 (586), eabb1655, 2021
282*2021
Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii
S Wang, MBA McDermott, G Chauhan, M Ghassemi, MC Hughes, ...
Proceedings of the ACM conference on health, inference, and learning, 222-235, 2020
2662020
Hurtful words: quantifying biases in clinical contextual word embeddings
H Zhang, AX Lu, M Abdalla, M McDermott, M Ghassemi
proceedings of the ACM Conference on Health, Inference, and Learning, 110-120, 2020
2112020
Rethinking clinical prediction: why machine learning must consider year of care and feature aggregation
B Nestor, M McDermott, G Chauhan, T Naumann, MC Hughes, ...
Proceedings of the 4th Machine Learning for Healthcare Conference 106, 381-405, 2019
201*2019
Baselines for chest x-ray report generation
W Boag, TMH Hsu, M McDermott, G Berner, E Alesentzer, P Szolovits
Machine learning for health workshop, 126-140, 2020
722020
Trends and focus of machine learning applications for health research
B Beaulieu-Jones, SG Finlayson, C Chivers, I Chen, M McDermott, ...
JAMA network open 2 (10), e1914051-e1914051, 2019
692019
A comprehensive EHR timeseries pre-training benchmark
M McDermott, B Nestor, E Kim, W Zhang, A Goldenberg, P Szolovits, ...
Proceedings of the Conference on Health, Inference, and Learning, 257-278, 2021
68*2021
Unsupervised multimodal representation learning across medical images and reports
TMH Hsu, WH Weng, W Boag, M McDermott, P Szolovits
arXiv preprint arXiv:1811.08615, 2018
492018
Meta-learning to improve pre-training
A Raghu, J Lorraine, S Kornblith, M McDermott, DK Duvenaud
Advances in Neural Information Processing Systems 34, 23231-23244, 2021
392021
Chexpert++: Approximating the chexpert labeler for speed, differentiability, and probabilistic output
MBA McDermott, TMH Hsu, WH Weng, M Ghassemi, P Szolovits
Machine Learning for Healthcare Conference, 913-927, 2020
382020
Semi-supervised biomedical translation with cycle wasserstein regression GANs
M McDermott, T Yan, T Naumann, N Hunt, HS Suresh, P Szolovits, ...
AAAI Conference on Artificial Intelligence 32, 2363-2370, 2018
352018
APPRAISE-AI tool for quantitative evaluation of AI studies for clinical decision support
JCC Kwong, A Khondker, K Lajkosz, MBA McDermott, XB Frigola, ...
JAMA Network Open 6 (9), e2335377-e2335377, 2023
332023
A closer look at auroc and auprc under class imbalance
M McDermott, H Zhang, L Hansen, G Angelotti, J Gallifant
Advances in Neural Information Processing Systems 37, 44102-44163, 2025
292025
Event Stream GPT: a data pre-processing and modeling library for generative, pre-trained transformers over continuous-time sequences of complex events
M McDermott, B Nestor, P Argaw, IS Kohane
Advances in Neural Information Processing Systems 36, 24322-24334, 2023
252023
Modeling the role of negative cooperativity in metabolic regulation and homeostasis
EC Bush, AE Clark, CM DeBoever, LE Haynes, S Hussain, S Ma, ...
PLoS One 7 (11), e48920, 2012
252012
Deep learning benchmarks on L1000 gene expression data
MBA McDermott, J Wang, WN Zhao, SD Sheridan, P Szolovits, I Kohane, ...
IEEE/ACM transactions on computational biology and bioinformatics 17 (6 …, 2019
202019
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Artikkelit 1–20