README.md

---
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:
- information-extraction
- text-classification
- question-answering
task_ids:
- entity-extraction
- multi-label-classification
- open-domain-qa
---


# CaseReportBench: Clinical Dense Extraction Benchmark

**CaseReportBench** is a curated benchmark dataset designed to evaluate the ability of large language models to perform **dense information extraction** from **clinical case reports**, particularly in the context of **rare disease diagnosis**.

This dataset supports fine-grained, system-wise phenotype extraction and structured diagnostic reasoning evaluation.

---

## Key Features

- Expert-annotated dense labels simulating comprehensive head-to-toe clinical assessments, capturing multi-system findings as encountered in real-world diagnostic reasoning
- Domain: Clinical Case Reports (PubmedCentral indexed)
- Use case: Medical IE, LLM evaluation, Rare disease diagnosis
- Data type: JSON with structured system-wise output
- Evaluation metrics: Token Selection Rate, Levenshtein Similarity, Exact Match

---

## Dataset Structure

Each record includes:

- `id`: Unique document identifier
- `text`: Raw case report
- `extracted_labels`: Dense structured annotations by system (e.g., nervous system, metabolic)
- `diagnosis`: Gold standard diagnosis
- `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={Conference on Health, Inference, and Learning (CHIL)},
  year={2025}
}
```