Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models

TL;DR

This paper enhances concept bottleneck models for ARDS diagnosis by integrating clinical note context via a Large Language Model, leading to improved interpretability and a 10% performance boost.

Contribution

It introduces a context-aware approach that incorporates clinical notes into CBMs using LLMs, significantly improving ARDS classification performance and concept comprehensiveness.

Findings

10% performance improvement over existing methods

Enhanced concept learning reduces reliance on shortcuts

Better characterization of ARDS through contextual information

Abstract

Large, publicly available clinical datasets have emerged as a novel resource for understanding disease heterogeneity and to explore personalization of therapy. These datasets are derived from data not originally collected for research purposes and, as a result, are often incomplete and lack critical labels. Many AI tools have been developed to retrospectively label these datasets, such as by performing disease classification; however, they often suffer from limited interpretability. Previous work has attempted to explain predictions using Concept Bottleneck Models (CBMs), which learn interpretable concepts that map to higher-level clinical ideas, facilitating human evaluation. However, these models often experience performance limitations when the concepts fail to adequately explain or characterize the task. We use the identification of Acute Respiratory Distress Syndrome (ARDS) as a challenging test case to demonstrate the value of incorporating contextual information from clinical notes to improve CBM performance. Our approach leverages a Large Language Model (LLM) to process clinical notes and generate additional concepts, resulting in a 10% performance gain over existing methods. Additionally, it facilitates the learning of more comprehensive concepts, thereby reducing the risk of information leakage and reliance on spurious shortcuts, thus improving the characterization of ARDS.