MIMIC-SR-ICD11: A Dataset for Narrative-Based Diagnosis
By: Yuexin Wu, Shiqi Wang, Vasile Rus
Potential Business Impact:
Helps doctors find diseases from patient notes.
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.
Similar Papers
LTR-ICD: A Learning-to-Rank Approach for Automatic ICD Coding
Machine Learning (CS)
Helps doctors sort patient sickness codes faster.
Synthetic Clinical Notes for Rare ICD Codes: A Data-Centric Framework for Long-Tail Medical Coding
Computation and Language
Teaches computers to find rare diseases in notes.
Knowledge Graph Augmented Large Language Models for Next-Visit Disease Prediction
Artificial Intelligence
Helps doctors understand patient health risks better.