README.md

metadata

pretty_name: MultiCaRe Cases
license: cc-by-4.0
task_categories:
  - text-classification
  - text-retrieval
language:
  - en
size_categories:
  - 100K<n<1M

MultiCaRe: Open-Source Clinical Case Dataset

MultiCaRe is an open-source, multimodal clinical case dataset derived from PubMed Central’s Open Access (OA) Case Report articles. It links de-identified case narratives to figure images/captions and article-level metadata, enabling cross-modal supervision and retrieval.

• Source and process: OA case reports from PMC; parsed metadata and abstracts; extracted case narratives; downloaded and processed figures; aligned captions; curated image taxonomy (>140 classes).
• Scale: 85k+ articles with 110k+ patient mentions and 160k+ images (v2.0).
• Tasks enabled: narrative classification, retrieval, summarization; multimodal modeling with image joins; VQA/doc-QA with figure references.
• Citation: Paper — https://www.mdpi.com/2306-5729/10/8/123; Zenodo — https://zenodo.org/records/13936721.

This repository: per-case dataset Per-case clinical narratives and demographics extracted from case reports.

Schema

• case_id: case identifier (joins to images.patient_id)
• article_id: PMCID (joins to articles.article_id)
• case_text: clinical case narrative
• age: age in years (0 if <1 y.o.)
• gender: Female, Male, Transgender, Unknown

Quick start

from datasets import load_dataset
cases = load_dataset("openmed-community/multicare-cases", split="train")
print(cases[0]["case_text"][:600])

Join with images

from datasets import load_dataset
cases = load_dataset("openmed-community/multicare-cases", split="train")
imgs  = load_dataset("openmed-community/multicare-images", split="train")

cid = cases[0]["case_id"]
imgs_for_case = imgs.filter(lambda e: e["patient_id"] == cid)
imgs_for_case[0]["image"].show()

Notes

• No official splits; recommend patient/article-level splitting to avoid leakage.
• Per-item OA licenses are provided at the image level and via articles.