xMICD: Explainable Representation of Multiple ICD Codes

2026-08-01

AI Summary

This work addresses the challenge of constructing interpretable patient representations from multiple ICD diagnosis codes while maintaining high predictive performance. The authors propose a similarity-based relative grouping mechanism that integrates clinical diagnostic groupings with semantic information from pretrained ICD embeddings, such as ICD2Vec. By applying similarity-weighted mapping, high-dimensional diagnosis codes are projected into a low-dimensional feature space where each dimension corresponds to a clinically meaningful category. This approach preserves strong representational capacity while enhancing model interpretability through explicit clinical semantics. Extensive experiments on multiple large-scale electronic health record datasets demonstrate that the proposed method achieves predictive performance comparable to purely embedding-based approaches, yet offers substantially improved clinical interpretability of the resulting features.

Abstract

Electronic Health Records (EHRs) are widely used for clinical risk prediction using machine learning. International Classification of Diseases (ICD) codes provide structured information about patient diagnoses, but representing them effectively remains challenging. Existing approaches often face a trade-off between predictive performance and interpretability: grouping-based representations are interpretable but may lose information, while embedding-based representations achieve strong predictive performance but are difficult to interpret. We propose Explainable Representation of Multiple ICD Codes (xMICD), a method for constructing low-dimensional patient representations from sets of ICD codes. xMICD combines clinically meaningful diagnostic groupings with similarity in a pre-trained ICD embedding space. Instead of using binary group membership, the method assigns codes to groups via similarity-based relative assignments, yielding features that reflect how closely a patient's diagnoses align with each clinical group. Experiments on large-scale EHR datasets demonstrate that xMICD achieves predictive performance comparable to embedding-based representations such as ICD2Vec across multiple clinical prediction tasks. At the same time, the resulting features remain clinically interpretable because each dimension corresponds to a recognizable diagnostic group. xMICD therefore provides a practical way to integrate embedding-based semantic relationships into interpretable clinical feature spaces for machine learning models.

Problem

• ICD codes
• interpretability
• predictive performance
• clinical representation
• electronic health records

Innovation

• xMICD
• ICD code representation
• interpretable machine learning
• clinical embeddings
• diagnostic grouping