no code implementations • 27 Aug 2023 • Scott L. Fleming, Alejandro Lozano, William J. Haberkorn, Jenelle A. Jindal, Eduardo P. Reis, Rahul Thapa, Louis Blankemeier, Julian Z. Genkins, Ethan Steinberg, Ashwin Nayak, Birju S. Patel, Chia-Chun Chiang, Alison Callahan, Zepeng Huo, Sergios Gatidis, Scott J. Adams, Oluseyi Fayanju, Shreya J. Shah, Thomas Savage, Ethan Goh, Akshay S. Chaudhari, Nima Aghaeepour, Christopher Sharp, Michael A. Pfeffer, Percy Liang, Jonathan H. Chen, Keith E. Morse, Emma P. Brunskill, Jason A. Fries, Nigam H. Shah
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burden and improve quality of care.
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.
no code implementations • 4 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.
1 code implementation • 5 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.
2 code implementations • 6 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.
1 code implementation • 3 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.