Search Results for author: Jason A. Fries

Found 6 papers, 4 papers with code

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

1 code implementation NeurIPS 2023 Michael Wornow, Rahul Thapa, Ethan Steinberg, Jason A. Fries, Nigam H. Shah

The success of foundation models creates new challenges for healthcare ML by requiring access to shared pretrained models to validate performance benefits.

Language Models in the Loop: Incorporating Prompting into Weak Supervision

no code implementations4 May 2022 Ryan Smith, Jason A. Fries, Braden Hancock, Stephen H. Bach

Our experimental evaluation shows that prompting large language models within a weak supervision framework can provide significant gains in accuracy.

Ontology-driven weak supervision for clinical entity classification in electronic health records

1 code implementation5 Aug 2020 Jason A. Fries, Ethan Steinberg, Saelig Khattar, Scott L. Fleming, Jose Posada, Alison Callahan, Nigam H. Shah

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.

General Classification Named Entity Recognition (NER) +3

Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data

2 code implementations6 Jan 2020 Ethan Steinberg, Ken Jung, Jason A. Fries, Conor K. Corbin, Stephen R. Pfohl, Nigam H. Shah

Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.

Representation Learning

Medical device surveillance with electronic health records

1 code implementation3 Apr 2019 Alison Callahan, Jason A. Fries, Christopher Ré, James I Huddleston III, Nicholas J Giori, Scott Delp, Nigam H. Shah

Using hip replacements as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96. 3% precision, 98. 5% recall, and 97. 4% F1, improved classification performance by 12. 7- 53. 0% over rule-based methods, and detected over 6 times as many complication events compared to using structured data alone.

Reading Comprehension

Cannot find the paper you are looking for? You can Submit a new open access paper.