Update README.md

README.md

@@ -23,3 +23,72 @@ task_ids:
   - 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}
+}
+
+
+
+```
+