xMICD: Explainable Representation of Multiple ICD Codes
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 ...
| Authors | Pat Vatiwutipong, Kumkup Keeratisiwakul, Albert Phuoc Kien Van Truong et al. |
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| Primary category | cs.LG |
| Published | 2 August 2026 |
| Last updated at source | 2 August 2026 |
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