MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

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.

2k Instruction Following +2

Instability in clinical risk stratification models using deep learning

no code implementations • 20 Nov 2022 • Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne, Adam D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen

While it has been well known in the ML community that deep learning models suffer from instability, the consequences for healthcare deployments are under characterised.