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 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}
}



```