18 citations · 27 across the 9 of their papers we have counts for
9 papers
Open (Clinical) LLMs are Sensitive to Instruction Phrasings
Alberto Mario Ceballos Arroyo, Monica Munnangi, Jiuding Sun +4
Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are p…
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction
Denis Jered McInerney, William Dickinson, Lucy C. Flynn +4
Many diagnostic errors occur because clinicians cannot easily access relevant information in patient Electronic Health Records (EHRs). In this work we propose a method to use LLMs…
Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Gongbo Zhang, Qiao Jin, Denis Jered McInerney +11
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical ev…
Retrieving Evidence from EHRs with LLMs: Possibilities and Challenges
Hiba Ahsan, Denis Jered McInerney, Jisoo Kim +4
Unstructured data in Electronic Health Records (EHRs) often contains critical information -- complementary to imaging -- that could inform radiologists' diagnoses. But the large vo…
Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges
Sanjana Ramprasad, Denis Jered McInerney, Iain J. Marshal +1
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. Building on…
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent +1
We propose CHiLL (Crafting High-Level Latents), an approach for natural-language specification of features for linear models. CHiLL prompts LLMs with expert-crafted queries to gene…
That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data
Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent +1
Pretraining multimodal models on Electronic Health Records (EHRs) provides a means of learning representations that can transfer to downstream tasks with minimal supervision. Recen…
Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking
Denis Jered McInerney, Luyang Kong, Kristjan Arumae +2
Multi-task learning is useful in NLP because it is often practically desirable to have a single model that works across a range of tasks. In the medical domain, sequential training…
Query-Focused EHR Summarization to Aid Imaging Diagnosis
Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret +3
Electronic Health Records (EHRs) provide vital contextual information to radiologists and other physicians when making a diagnosis. Unfortunately, because a given patient's record…