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Matthew B. A. McDermott
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2020 – today
- 2024
- [i26]Matthew B. A. McDermott, Lasse Hyldig Hansen, Haoran Zhang, Giovanni Angelotti, Jack Gallifant:
A Closer Look at AUROC and AUPRC under Class Imbalance. CoRR abs/2401.06091 (2024) - [i25]Hyewon Jeong, Sarah Jabbour, Yuzhe Yang, Rahul Thapa, Hussein Mozannar, William Jongwon Han, Nikita Mehandru, Michael Wornow, Vladislav Lialin, Xin Liu, Alejandro Lozano, Jiacheng Zhu, Rafal Dariusz Kocielnik, Keith Harrigian, Haoran Zhang, Edward Lee, Milos Vukadinovic, Aparna Balagopalan, Vincent Jeanselme, Katherine Matton, Ilker Demirel, Jason A. Fries, Parisa Rashidi, Brett K. Beaulieu-Jones, Xuhai Orson Xu, Matthew B. A. McDermott, Tristan Naumann, Monica Agrawal, Marinka Zitnik, Berk Ustun, Edward Choi, Kristen Yeom, Gamze Gürsoy, Marzyeh Ghassemi, Emma Pierson, George H. Chen, Sanjat Kanjilal, Michael Oberst, Linying Zhang
, Harvineet Singh, Tom Hartvigsen, Helen Zhou, Chinasa T. Okolo
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium. CoRR abs/2403.01628 (2024) - [i24]Justin Xu, Jack Gallifant, Alistair E. W. Johnson, Matthew B. A. McDermott:
ACES: Automatic Cohort Extraction System for Event-Stream Datasets. CoRR abs/2406.19653 (2024) - [i23]Ethan Steinberg, Michael Wornow, Suhana Bedi, Jason Alan Fries, Matthew B. A. McDermott, Nigam H. Shah:
meds_reader: A fast and efficient EHR processing library. CoRR abs/2409.09095 (2024) - 2023
- [j2]Matthew B. A. McDermott
Structure-inducing pre-training. Nat. Mac. Intell. 5(6): 612-621 (2023) - [c19]Matthew B. A. McDermott, Bret Nestor, Peniel N. Argaw, Isaac S. Kohane:
Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events. NeurIPS 2023 - [i22]Matthew B. A. McDermott, Bret Nestor, Peniel N. Argaw, Isaac S. Kohane:
Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events. CoRR abs/2306.11547 (2023) - [i21]Hyewon Jeong, Nassim Oufattole, Aparna Balagopalan, Matthew B. A. McDermott, Payal Chandak, Marzyeh Ghassemi, Collin M. Stultz:
Event-Based Contrastive Learning for Medical Time Series. CoRR abs/2312.10308 (2023) - 2022
- [b1]Matthew B. A. McDermott:
Leveraging Structure and Knowledge in Clinical and Biomedical Representation Learning. MIT, USA, 2022 - 2021
- [c18]Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi:
A comprehensive EHR timeseries pre-training benchmark. CHIL 2021: 257-278 - [c17]Subhrajit Roy, Stephen Pfohl, Girmaw Abebe Tadesse, Luis Oala, Fabian Falck, Yuyin Zhou, Liyue Shen, Ghada Zamzmi, Purity Mugambi, Ayah Zirikly, Matthew B. A. McDermott, Emily Alsentzer:
Machine Learning for Health (ML4H) 2021. ML4H@NeurIPS 2021: 1-12 - [c16]Samuel G. Finlayson, Matthew B. A. McDermott, Alex V. Pickering, Scott L. Lipnick, Isaac S. Kohane:
Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles. PSB 2021 - [c15]Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew B. A. McDermott, Irene Y. Chen, Marzyeh Ghassemi:
CheXclusion: Fairness gaps in deep chest X-ray classifiers. PSB 2021 - [e3]Subhrajit Roy, Stephen Pfohl, Emma Rocheteau, Girmaw Abebe Tadesse, Luis Oala, Fabian Falck, Yuyin Zhou, Liyue Shen, Ghada Zamzmi, Purity Mugambi, Ayah Zirikly, Matthew B. A. McDermott, Emily Alsentzer:
Machine Learning for Health, ML4H@NeurIPS 2021, 04 December 2021, Virtual Event. Proceedings of Machine Learning Research 158, PMLR 2021 [contents] - [i20]Matthew B. A. McDermott, Brendan Yap, Tzu-Ming Harry Hsu, Di Jin, Peter Szolovits:
Adversarial Contrastive Pre-training for Protein Sequences. CoRR abs/2102.00466 (2021) - [i19]Matthew B. A. McDermott, Brendan Yap, Peter Szolovits, Marinka Zitnik:
Rethinking Relational Encoding in Language Model: Pre-Training for General Sequences. CoRR abs/2103.10334 (2021) - [i18]Fabian Falck, Yuyin Zhou, Emma Rocheteau, Liyue Shen, Luis Oala, Girmaw Abebe, Subhrajit Roy, Stephen Pfohl, Emily Alsentzer, Matthew B. A. McDermott:
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021. CoRR abs/2112.00179 (2021) - 2020
- [j1]Matthew B. A. McDermott
Deep Learning Benchmarks on L1000 Gene Expression Data. IEEE ACM Trans. Comput. Biol. Bioinform. 17(6): 1846-1857 (2020) - [c14]Haoran Zhang
Hurtful words: quantifying biases in clinical contextual word embeddings. CHIL 2020: 110-120 - [c13]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, Tristan Naumann:
MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. CHIL 2020: 222-235 - [c12]Matthew B. A. McDermott, Tzu-Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits:
CheXpert++: Approximating the CheXpert Labeler for Speed, Differentiability, and Probabilistic Output. MLHC 2020: 913-927 - [c11]Suproteem K. Sarkar, Subhrajit Roy, Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Ioana Bica, Griffin Adams, Stephen Pfohl, Stephanie L. Hyland:
Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All. ML4H@NeurIPS 2020: 1-11 - [e2]Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Suproteem K. Sarkar, Subhrajit Roy, Stephanie L. Hyland:
Machine Learning for Health Workshop, ML4H@NeurIPS 2020, Virtual Event, 11 December 2020. Proceedings of Machine Learning Research 136, PMLR 2020 [contents] - [i17]Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca:
ML4H Abstract Track 2019. CoRR abs/2002.01584 (2020) - [i16]Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew B. A. McDermott, Marzyeh Ghassemi:
CheXclusion: Fairness gaps in deep chest X-ray classifiers. CoRR abs/2003.00827 (2020) - [i15]Haoran Zhang, Amy X. Lu, Mohamed Abdalla, Matthew B. A. McDermott, Marzyeh Ghassemi:
Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings. CoRR abs/2003.11515 (2020) - [i14]Matthew B. A. McDermott, Tzu-Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits:
CheXpert++: Approximating the CheXpert labeler for Speed, Differentiability, and Probabilistic Output. CoRR abs/2006.15229 (2020) - [i13]Matthew B. A. McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, Marzyeh Ghassemi:
A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Data. CoRR abs/2007.10185 (2020) - [i12]Emily Alsentzer, Matthew B. A. McDermott, Fabian Falck, Suproteem K. Sarkar, Subhrajit Roy, Stephanie L. Hyland:
ML4H Abstract Track 2020. CoRR abs/2011.11554 (2020)
2010 – 2019
- 2019
- [c10]Geeticka Chauhan, Matthew B. A. McDermott, Peter Szolovits:
A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains. WNLP@ACL 2019: 18-20 - [c9]Geeticka Chauhan
REflex: Flexible Framework for Relation Extraction in Multiple Domains. BioNLP@ACL 2019: 30-47 - [c8]Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini:
Reproducibility in Machine Learning for Health. RML@ICLR 2019 - [c7]Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew B. A. McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi:
Clinically Accurate Chest X-Ray Report Generation. MLHC 2019: 249-269 - [c6]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. MLHC 2019: 381-405 - [c5]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 - [c4]William Boag, Tzu-Ming Harry Hsu, Matthew B. A. McDermott, Gabriela Berner, Emily Alsentzer, Peter Szolovits:
Baselines for Chest X-Ray Report Generation. ML4H@NeurIPS 2019: 126-140 - [c3]Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi:
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. ML4H@NeurIPS 2019: 202-219 - [e1]Adrian V. Dalca, Matthew B. A. McDermott, Emily Alsentzer, Samuel G. Finlayson, Michael Oberst, Fabian Falck, Brett K. Beaulieu-Jones:
Machine Learning for Health Workshop, ML4H@NeurIPS 2019, Vancouver, BC, Canada, 13 December 2019. Proceedings of Machine Learning Research 116, PMLR 2019 [contents] - [i11]Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew B. A. McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits, Marzyeh Ghassemi:
Clinically Accurate Chest X-Ray Report Generation. CoRR abs/1904.02633 (2019) - [i10]Emily Alsentzer, John R. Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, Matthew B. A. McDermott:
Publicly Available Clinical BERT Embeddings. CoRR abs/1904.03323 (2019) - [i9]Geeticka Chauhan, Matthew B. A. McDermott, Peter Szolovits:
REflex: Flexible Framework for Relation Extraction in Multiple Domains. CoRR abs/1906.08318 (2019) - [i8]Matthew B. A. McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Marzyeh Ghassemi, Luca Foschini:
Reproducibility in Machine Learning for Health. CoRR abs/1907.01463 (2019) - [i7]Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Michael C. Hughes, Tristan Naumann, Marzyeh Ghassemi:
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III. CoRR abs/1907.08322 (2019) - [i6]Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks. CoRR abs/1908.00690 (2019) - [i5]Samuel G. Finlayson, Matthew B. A. McDermott, Alex V. Pickering, Scott L. Lipnick, William Yuan, Isaac S. Kohane:
Approaching Small Molecule Prioritization as a Cross-Modal Information Retrieval Task through Coordinated Representation Learning. CoRR abs/1911.10241 (2019) - [i4]Aparna Balagopalan, Jekaterina Novikova, Matthew B. A. McDermott, Bret Nestor, Tristan Naumann, Marzyeh Ghassemi:
Cross-Language Aphasia Detection using Optimal Transport Domain Adaptation. CoRR abs/1912.04370 (2019) - 2018
- [c2]Matthew B. A. McDermott, Tom Yan, Tristan Naumann, Nathan Hunt, Harini Suresh, Peter Szolovits, Marzyeh Ghassemi:
Semi-Supervised Biomedical Translation With Cycle Wasserstein Regression GANs. AAAI 2018: 2363-2370 - [c1]Di Jin, Franck Dernoncourt, Elena Sergeeva, Matthew B. A. McDermott, Geeticka Chauhan:
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network. SemEval@NAACL-HLT 2018: 798-804 - [i3]Natalia Antropova, Andrew L. Beam, Brett K. Beaulieu-Jones, Irene Chen, Corey Chivers
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018. CoRR abs/1811.07216 (2018) - [i2]Tzu-Ming Harry Hsu, Wei-Hung Weng, Willie Boag, Matthew B. A. McDermott, Peter Szolovits:
Unsupervised Multimodal Representation Learning across Medical Images and Reports. CoRR abs/1811.08615 (2018) - [i1]Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, Marzyeh Ghassemi:
Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation. CoRR abs/1811.12583 (2018)
Coauthor Index
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