McDermott, Matthew

Publicly Available Clinical

Alsentzer, E., Murphy, J., Boag, W., Weng, W.-H., Jindi, D., Naumann, T., & McDermott, M. (2019) · Proceedings of the 2nd Clinical Natural Language Processing Workshop

Machine Learning in HealthcareTopic Modeling

Cited by 1,736

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 928

CheXclusion: Fairness gaps in deep chest X-ray classifiers

Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y., & Ghassemi, M. (2020) · Biocomputing 2021

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 275

Reproducibility in machine learning for health research: Still a ways to go

McDermott, M. B. A., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., & Ghassemi, M. (2021) · Science Translational Medicine

Scientific Computing and Data ManagementResearch Data Management Practices

Cited by 273

Hurtful words: quantifying biases in clinical contextual word embeddings

Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning

Machine Learning in HealthcareEthics and Social Impacts of AI

Cited by 122

A Closer Look at AUROC and AUPRC under Class Imbalance

McDermott, M. B. A., Zhang, H., Hansen, L. H., Angelotti, G., & Gallifant, J. (2024) · arXiv

Imbalanced Data Classification TechniquesMachine Learning and Data Classification

Cited by 65

Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings

Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · arXiv

Machine Learning in HealthcareEthics and Social Impacts of AI

Cited by 37

A comprehensive EHR timeseries pre-training benchmark

McDermott, M., Nestor, B., Kim, E., Zhang, W., Goldenberg, A., Szolovits, P., & Ghassemi, M. (2021) · Proceedings of the Conference on Health, Inference, and Learning

Machine Learning in HealthcareDomain Adaptation and Few-Shot Learning

Cited by 24

CheXpert++: Approximating the CheXpert labeler for Speed, Differentiability, and Probabilistic Output

McDermott, M. B. A., Hsu, T. M. H., Weng, W.-H., Ghassemi, M., & Szolovits, P. (2020) · arXiv

COVID-19 diagnosis using AIMultimodal Machine Learning Applications

Cited by 22