Geoffrey Young — accepted AI-conference papers

2024

Towards Reducing Diagnostic Errors with Interpretable Risk Prediction

Denis McInerney, William Dickinson, Lucy Flynn, Andrea Young, Geoffrey Young, Jan-Willem van de Meent +1

NAACL 2024long

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 to identify pieces of evidence in patient EHR data that indicate increased or decreased risk of specific diagnoses; our…

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2023

CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models

Denis Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron C Wallace

EMNLP 2023long findings

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 generate interpretable features from health records. The resulting noisy labels are then used to train a simple linear class…

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2022

That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data

Jered McInerney, Geoffrey Young, Jan-Willem van de Meent, Byron Wallace

EMNLP 2022main

Pretraining multimodal models on Electronic Health Records (EHRs) provides a means of learning representations that can transfer to downstream tasks with minimal supervision. Recent multimodal models induce soft local alignments between image regions and sentences. This is of particular interest in…

Cited by 3 Source ↗PDF ↗code ↗Scholar ↗