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
Record details
Published: 2 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment · Healthcare · Transparency
Retrieved: 4 August 2026
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ethics.ai (2 August 2026), “xMICD: Explainable Representation of Multiple ICD Codes,” evidence record 16160, https://ethics.ai/record/16160 (originally published by arXiv cs.LG).
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