Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models
This work addresses the challenge of improving interpretability and accuracy in diagnosing ARDS for clinical applications, representing an incremental advance in domain-specific AI methods.
The paper tackled the problem of limited interpretability and performance in AI tools for clinical datasets by incorporating contextual information from clinical notes into Concept Bottleneck Models (CBMs) for ARDS diagnosis, resulting in a 10% performance gain over existing methods.
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.