Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR

Area of Science:

• Digital Medicine
• Medical Informatics
• Machine Learning

Background:

• Electronic Health Record (EHR) modeling is vital for digital medicine.
• Current EHR models often overlook higher-order interactions and causal relationships among medical codes.
• This limitation hinders accurate downstream clinical predictions.

Purpose of the Study:

• To propose a novel framework, CACHE, for effective and insightful clinical predictions.
• To address the limitations of existing EHR models in capturing complex code interactions.
• To leverage hypergraph representation learning and counterfactual/factual reasoning.

Main Methods:

• Developed the CACHE framework utilizing hypergraph representation learning.
• Integrated counterfactual and factual reasoning techniques for enhanced analysis.
• Validated the framework on two real-world EHR datasets.

Main Results:

• CACHE demonstrated superior performance compared to existing methods on EHR datasets.
• Experiments confirmed the framework's effectiveness in clinical prediction tasks.
• Case studies highlighted the model's ability to provide clinically meaningful interpretations.

Conclusions:

• CACHE offers an effective and insightful approach to clinical prediction using EHR data.
• The framework successfully captures higher-order interactions and causal relations.
• CACHE shows promise for advancing digital medicine through improved EHR modeling.