33 citations · 122 across the 12 of their papers we have counts for
7 papers · 1 filter
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
Scott L. Fleming, Alejandro Lozano, William J. Haberkorn +27
The ability of large language models (LLMs) to follow natural language instructions with human-level fluency suggests many opportunities in healthcare to reduce administrative burd…
EHRSHOT: An EHR Benchmark for Few-Shot Evaluation of Foundation Models
Michael Wornow, Rahul Thapa, Ethan Steinberg +2
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…
All models are local: time to replace external validation with recurrent local validation
Alex Youssef, Michael Pencina, Anshul Thakur +3
External validation is often recommended to ensure the generalizability of ML models. However, it neither guarantees generalizability nor equates to a model's clinical usefulness (…
Evaluation of GPT-3.5 and GPT-4 for supporting real-world information needs in healthcare delivery
Debadutta Dash, Rahul Thapa, Juan M. Banda +15
Despite growing interest in using large language models (LLMs) in healthcare, current explorations do not assess the real-world utility and safety of LLMs in clinical settings. Our…
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs
Michael Wornow, Yizhe Xu, Rahul Thapa +6
The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patie…
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record
Conor K. Corbin, Rob Maclay, Aakash Acharya +6
Machine learning (ML) applications in healthcare are extensively researched, but successful translations to the bedside are scant. Healthcare institutions are establishing framewor…
MOTOR: A Time-To-Event Foundation Model For Structured Medical Records
Ethan Steinberg, Jason Fries, Yizhe Xu +1
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…