Search Results for author:
Found 16 papers, 8 papers with code
TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records
Large language models (LLMs) have emerged as promising tools for assisting in medical tasks, yet processing Electronic Health Records (EHRs) presents unique challenges due to their longitudinal nature.
VeriFact: Verifying Facts in LLM-Generated Clinical Text with Electronic Health Records
Methods to ensure factual accuracy of text generated by large language models (LLM) in clinical medicine are lacking.
Assessing the Limitations of Large Language Models in Clinical Fact Decomposition
Verifying factual claims is critical for using large language models (LLMs) in healthcare.
A Proposed S.C.O.R.E. Evaluation Framework for Large Language Models : Safety, Consensus, Objectivity, Reproducibility and Explainability
A comprehensive qualitative evaluation framework for large language models (LLM) in healthcare that expands beyond traditional accuracy and quantitative metrics needed.
A Multi-Center Study on the Adaptability of a Shared Foundation Model for Electronic Health Records
With continued pretraining on local data, label efficiency substantially improved, such that $FM_{SM}$ required fewer than 1% of training examples to match the fully trained GBM's performance.
Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature
The quickly-expanding nature of published medical literature makes it challenging for clinicians and researchers to keep up with and summarize recent, relevant findings in a timely manner.
MOTOR: A Time-To-Event Foundation Model For Structured Medical Records
MOTOR is the first foundation model for medical TTE predictions and we release a 143M parameter pretrained model for research use at [redacted URL].
RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHR
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 architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
We propose rank-weighted average treatment effect (RATE) metrics as a simple and general family of metrics for comparing and testing the quality of treatment prioritization rules.
A new paradigm for accelerating clinical data science at Stanford Medicine
The ecosystem is designed to bring the modern data science community to highly sensitive clinical data in a secure and collaborative big data analytics environment with a goal to enable bigger, better and faster science.
The accuracy vs. coverage trade-off in patient-facing diagnosis models
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process.
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in Healthcare
There is an emerging trend in the reinforcement learning for healthcare literature.
A comparison of methods for model selection when estimating individual treatment effects
Instead of relying on a single method, multiple models fit by a diverse set of algorithms should be evaluated against each other using an objective function learned from the validation set.
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
Using simulations, we show that using synth-validation to select a causal inference method for each study lowers the expected estimation error relative to consistently using any single method.
Effective Representations of Clinical Notes
We used the learned representations, along with commonly used bag of words and topic model representations, as features for predictive models of clinical events.
