Datasets:
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---
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
```python
from datasets import load_dataset
ds = load_dataset("cxyzhang/caseReportBench_ClinicalDenseExtraction_Benchmark")
print(ds["train"][0])
```
## Citation
```bibtex
@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}
}
```
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