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Jason Alan Fries

7 accepted papers

2025

Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHR Data

Michael Wornow, Suhana Bedi, Miguel Angel Fuentes Hernandez, Ethan Steinberg, Jason Alan Fries, Christopher Re +2
ICLR 2025poster

Foundation Models (FMs) trained on Electronic Health Records (EHRs) have achieved state-of-the-art results on numerous clinical prediction tasks. However, prior EHR FMs typically have context windows of $<$1k tokens, which prevents them from modeling full patient EHRs which can exceed 10k's of event…

Cited by 1Source ↗PDF ↗Scholar ↗
2025

Time-to-Event Pretraining for 3D Medical Imaging

Zepeng Frazier Huo, Jason Alan Fries, Alejandro Lozano, Jeya Maria Jose Valanarasu, Ethan Steinberg, Louis Blankemeier +3
ICLR 2025poster

With the rise of medical foundation models and the growing availability of imaging data, scalable pretraining techniques offer a promising way to identify imaging biomarkers predictive of future disease risk. While current self-supervised methods for 3D medical imaging models capture local structura…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2024

MOTOR: A Time-to-Event Foundation Model For Structured Medical Records

Ethan Steinberg, Jason Alan Fries, Yizhe Xu, Nigam Shah
ICLR 2024spotlight

We present a self-supervised, time-to-event (TTE) foundation model called MOTOR (Many Outcome Time Oriented Representations) which is pretrained on timestamped sequences of events in electronic health records (EHR) and health insurance claims. TTE models are used for estimating the probability distr…

Cited by 17Source ↗PDF ↗Scholar ↗
2023

EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models

Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason Alan Fries, Nigam Shah
NeurIPS 2023spotlight

While the general machine learning (ML) community has benefited from public datasets, tasks, and models, the progress of ML in healthcare has been hampered by a lack of such shared assets. The success of foundation models creates new challenges for healthcare ML by requiring access to shared pretrai…

Cited by 36Source ↗PDF ↗code ↗Scholar ↗
2023

INSPECT: A Multimodal Dataset for Pulmonary Embolism Diagnosis and Prognosis

Shih-Cheng Huang, Zepeng Huo, Ethan Steinberg, Chia-Chun Chiang, Matthew P. Lungren, Curtis Langlotz +3
NeurIPS 2023poster

Synthesizing information from various data sources plays a crucial role in the practice of modern medicine. Current applications of artificial intelligence in medicine often focus on single-modality data due to a lack of publicly available, multimodal medical datasets. To address this limitation, we…

Cited by 11Source ↗PDF ↗Scholar ↗
2022

BigBio: A Framework for Data-Centric Biomedical Natural Language Processing

Jason Alan Fries, Leon Weber, Natasha Seelam, Gabriel Altay, Debajyoti Datta, Samuele Garda +37
NeurIPS 2022accept

Training and evaluating language models increasingly requires the construction of meta-datasets -- diverse collections of curated data with clear provenance. Natural language prompting has recently lead to improved zero-shot generalization by transforming existing, supervised datasets into a variety…

Cited by 58Source ↗PDF ↗code ↗Scholar ↗
2022

Multitask Prompted Training Enables Zero-Shot Task Generalization

Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai +34
ICLR 2022spotlight

Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a consequence of implicit multitask learning in language models’ pretraining (Radford et al., 2019). Can zero-shot genera…

Cited by 1949Source ↗PDF ↗code ↗Scholar ↗