metadata

annotations_creators:
  - expert-generated
language:
  - en
license: cc-by-nc-4.0
multilinguality: monolingual
pretty_name: 'CaseReportBench: Clinical Dense Extraction Benchmark'
tags:
  - clinical-nlp
  - dense-information-extraction
  - medical
  - case-reports
  - rare-diseases
  - benchmarking
  - information-extraction
task_categories:
  - token-classification
task_ids:
  - named-entity-recognition

CaseReportBench: Clinical Dense Extraction Benchmark

CaseReportBench is a curated benchmark dataset designed to evaluate how well large language models (LLMs) can perform dense information extraction from clinical case reports, with a focus on rare disease diagnosis.

It supports fine-grained, system-level phenotype extraction and structured diagnostic reasoning — enabling model evaluation in real-world medical decision-making contexts.

🔔 Note

This dataset accompanies our upcoming publication:

Zhang et al. CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports.
To appear in the Proceedings of the Conference on Health, Inference, and Learning (CHIL 2025), PMLR.

The official PMLR citation and link will be added upon publication.

Key Features

Expert-annotated, system-wise phenotypic labels mimicking clinical assessments
Based on real-world PubMed Central-indexed clinical case reports
Format: JSON with structured head-to-toe organ system outputs
Designed for: Biomedical NLP, IE, rare disease reasoning, and LLM benchmarking
Metrics include: Token Selection Rate, Levenshtein Similarity, Exact Match

Dataset Structure

Each record includes:

id: Unique document ID
text: Full raw case report
extracted_labels: System-organized dense annotations (e.g., neuro, heme, derm, etc.)
diagnosis: Final confirmed diagnosis (Inborn Error of Metabolism)
source: PubMed ID or citation

Usage

from datasets import load_dataset

ds = load_dataset("cxyzhang/caseReportBench_ClinicalDenseExtraction_Benchmark")
print(ds["train"][0])

Citation

@inproceedings{zhang2025casereportbench,
  title     = {CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports},
  author    = {Zhang, Cindy and Others},
  booktitle = {Proceedings of the Conference on Health, Inference, and Learning (CHIL)},
  series    = {Proceedings of Machine Learning Research},
  volume    = {vX},  % Update when available
  year      = {2025},
  publisher = {PMLR},
  note      = {To appear}
}