File size: 2,383 Bytes
536e8ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | ---
pretty_name: MedCase-Bench
size_categories:
- n<1K
tags:
- medical-literature
- case-reports
- clinical-atoms
configs:
- config_name: metadata
default: true
data_files:
- split: evaluation
path: metadata.json
---
# MedCase-Bench
[Project page](https://huggingface.co/spaces/PediaMedAI/MedCaseAgent)
· MedCase-150K
· [Agent code](https://github.com/PediaMedAI/MedCaseAgent)
MedCase-Bench contains **584 cases** for medical case-report generation, with
one clinical-atom JSON file per case and source bibliography. Clinical atoms
are patient-specific facts extracted from published case reports, covering
history, presentation, examinations, treatment and outcome.
Each atom file contains five lists of strings:
| Field | Contents |
| --- | --- |
| `history` | Patient background and relevant medical history. |
| `presentation` | Symptoms and findings at presentation. |
| `diagnostics` | Examinations, laboratory results and diagnostic observations. |
| `management` | Treatments, procedures and clinical decisions. |
| `outcome` | Clinical course, response and follow-up. |
## Files
| File | Contents |
| --- | --- |
| `atoms/<PMCID>_atoms.json` | 584 clinical-atom files. |
| `metadata.json` | 584 corresponding source records, including article identifiers, publication dates, licenses and source download links. |
This package contains textual inputs and source metadata. Full articles and
medical images can be obtained through the source links in `metadata.json`.
Source publications retain the licenses recorded in their metadata.
## Use
```python
import json
from pathlib import Path
files = sorted(Path("atoms").glob("*_atoms.json"))
assert len(files) == 584
atoms = json.loads(files[0].read_text(encoding="utf-8"))
print(atoms["history"])
```
After installing [MedCaseAgent](https://github.com/PediaMedAI/MedCaseAgent) and
configuring a compatible model API:
```bash
medcase-agent validate atoms/<PMCID>_atoms.json
medcase-agent generate atoms/<PMCID>_atoms.json \
--exclude-ids path/to/your-exclusions.json --output runs
```
For generation with images, prepare each case's source images following the
[preprocessing guide](https://github.com/PediaMedAI/MedCaseAgent/blob/main/docs/preprocessing.md).
For retrieval-based evaluation, prepare your own PMCID, PMID and DOI exclusion
list for the benchmark sources and any related publications.
|