MIMIC-SR-ICD11: A Dataset for Narrative-Based Diagnosis

• URL: http://arxiv.org/abs/2511.05485v1
• Date: Fri, 07 Nov 2025 18:55:22 GMT
• Title: MIMIC-SR-ICD11: A Dataset for Narrative-Based Diagnosis
• Authors: Yuexin Wu, Shiqi Wang, Vasile Rus,
• Abstract summary: We introduce MIMIC-SR-ICD11, a large English diagnostic dataset built from EHR discharge notes and aligned to WHO ICD-11 terminology.<n>We present LL-Rank, a likelihood-based re-ranking framework that computes a length-normalized joint likelihood of each label given the clinical report context.
• Score: 14.505360834752866
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Disease diagnosis is a central pillar of modern healthcare, enabling early detection and timely intervention for acute conditions while guiding lifestyle adjustments and medication regimens to prevent or slow chronic disease. Self-reports preserve clinically salient signals that templated electronic health record (EHR) documentation often attenuates or omits, especially subtle but consequential details. To operationalize this shift, we introduce MIMIC-SR-ICD11, a large English diagnostic dataset built from EHR discharge notes and natively aligned to WHO ICD-11 terminology. We further present LL-Rank, a likelihood-based re-ranking framework that computes a length-normalized joint likelihood of each label given the clinical report context and subtracts the corresponding report-free prior likelihood for that label. Across seven model backbones, LL-Rank consistently outperforms a strong generation-plus-mapping baseline (GenMap). Ablation experiments show that LL-Rank's gains primarily stem from its PMI-based scoring, which isolates semantic compatibility from label frequency bias.

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