20 citations · 26 across the 7 of their papers we have counts for
7 papers
BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP
Thomas Sounack, Joshua Davis, Brigitte Durieux +7
Encoder-based transformer models are central to biomedical and clinical Natural Language Processing (NLP), as their bidirectional self-attention makes them well-suited for efficien…
MEDS-Tab: Automated tabularization and baseline methods for MEDS datasets
Nassim Oufattole, Teya Bergamaschi, Aleksia Kolo +4
Effective, reliable, and scalable development of machine learning (ML) solutions for structured electronic health record (EHR) data requires the ability to reliably generate high-q…
ACES: Automatic Cohort Extraction System for Event-Stream Datasets
Justin Xu, Jack Gallifant, Alistair E. W. Johnson +1
Reproducibility remains a significant challenge in machine learning (ML) for healthcare. Datasets, model pipelines, and even task or cohort definitions are often private in this fi…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
A Closer Look at AUROC and AUPRC under Class Imbalance
Matthew B. A. McDermott, Haoran Zhang, Lasse Hyldig Hansen +2
In machine learning (ML), a widespread claim is that the area under the precision-recall curve (AUPRC) is a superior metric for model comparison to the area under the receiver oper…
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Event-Based Contrastive Learning for Medical Time Series
Hyewon Jeong, Nassim Oufattole, Matthew Mcdermott +4
In clinical practice, one often needs to identify whether a patient is at high risk of adverse outcomes after some key medical event. For example, quantifying the risk of adverse o…