Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models

Happy to share our latest work presented at #MLHC2025!

Context-Aware Concept Bottleneck Models for ARDS

🧠 Improving ARDS Diagnosis Through Context-Aware Concept Bottleneck Models

Large, publicly available clinical datasets have emerged as a novel resource for understanding disease heterogeneity and exploring personalization of therapy. Our work leverages these datasets to improve diagnosis of Acute Respiratory Distress Syndrome (ARDS).

Key Innovation

✨ Combines multiple data modalities:

• Structured ICU data (vital signs, lab values)
• LLM-extracted concepts from clinical notes
• Information from medical imaging

Results

📈 Achieves 8-10% accuracy improvement without compromising interpretability

By using concept bottleneck models, we maintain the ability to understand why the model makes its predictions, which is crucial for clinical adoption and trust.

Why This Matters

ARDS is a critical condition that requires timely and accurate diagnosis. Our approach demonstrates that:

• Combining structured and unstructured clinical data improves predictions
• Interpretable AI models can achieve high performance
• LLMs can extract meaningful concepts from clinical text

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📄 Read the full paper: arXiv:2508.09719

Conference: Machine Learning for Healthcare (MLHC) 2025