33 citations · 122 across the 12 of their papers we have counts for
12 papers
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…
Instability in clinical risk stratification models using deep learning
Daniel Lopez-Martinez, Alex Yakubovich, Martin Seneviratne +9
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. We study th…
Clinical Utility Gains from Incorporating Comorbidity and Geographic Location Information into Risk Estimation Equations for Atherosclerotic Cardiovascular Disease
Yizhe Xu, Agata Foryciarz, Ethan Steinberg +1
Objective: There are several efforts to re-learn the 2013 ACC/AHA pooled cohort equations (PCE) for patients with specific comorbidities and geographic locations. With over 363 cus…
Treatment Heterogeneity for Survival Outcomes
Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup +3
Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearne…
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare
Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz +3
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of h…
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries +8
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning arch…