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Found 8 papers, 0 papers with code
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction
In this work we propose a method to use LLMs to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses; our ultimate aim is to increase access to evidence and reduce diagnostic errors.
Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence.
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
Our method entails tasking an LLM to infer whether a patient has, or is at risk of, a particular condition on the basis of associated notes; if so, we ask the model to summarize the supporting evidence.
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query.
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
We propose CHiLL (Crafting High-Level Latents), an approach for natural-language specification of features for linear models.
That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data
We compare alignments from a state-of-the-art multimodal (image and text) model for EHR with human annotations that link image regions to sentences.
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Elastic Weight Consolidation is a recently proposed method to address this issue, but scaling this approach to the modern large models used in practice requires making strong independence assumptions about model parameters, limiting its effectiveness.
Query-Focused EHR Summarization to Aid Imaging Diagnosis
We propose and evaluate models that extract relevant text snippets from patient records to provide a rough case summary intended to aid physicians considering one or more diagnoses.
