Papers by Geoffrey Young
Towards Reducing Diagnostic Errors with Interpretable Risk Prediction (2024.naacl-long)
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Denis McInerney, William Dickinson, Lucy Flynn, Andrea Young, Geoffrey Young, Jan-Willem van de Meent, Byron Wallace
| Challenge: | Diagnostic errors occur because clinicians cannot easily access relevant information in EHRs. |
| Approach: | They propose a method to use LLMs to identify pieces of evidence that indicate increased or decreased risk of specific diagnoses in patient EHRs. |
| Outcome: | The proposed method reduces diagnostic errors by identifying evidence in patient EHRs . it uses a Neural Additive Model to make predictions backed by evidence at time-points where clinicians are uncertain . |
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models (2023.findings-emnlp)
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| Challenge: | a recent study suggests that linear models with interpretable features are more reliable than opaque models. |
| Approach: | They propose an approach for natural-language specification of features for linear models . they prompt LLMs with expert-crafted queries to generate interpretable features from health records . |
| Outcome: | The proposed approach can be used to craft features clinically meaningful for downstream tasks . it is based on a risk prediction task and standard predictive tasks based upon this data . |
That’s the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data (2022.emnlp-main)
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| Challenge: | Recent multimodal models induce soft local alignments between image regions and sentences. |
| Approach: | They compare alignments from a state-of-the-art multimodal model for EHR with human annotations that link image regions to sentences. |
| Outcome: | The proposed models induce soft local alignments between image regions and sentences . the text has an often weak or unintuitive influence on attention, the authors found . |