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--- |
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pretty_name: MultiCaRe Cases |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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- text-retrieval |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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# MultiCaRe: Open-Source Clinical Case Dataset |
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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. |
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- 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). |
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- Scale: 85k+ articles with 110k+ patient mentions and 160k+ images (v2.0). |
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- Tasks enabled: narrative classification, retrieval, summarization; multimodal modeling with image joins; VQA/doc-QA with figure references. |
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- Citation: Paper — https://www.mdpi.com/2306-5729/10/8/123; Zenodo — https://zenodo.org/records/13936721. |
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This repository: per-case dataset |
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Per-case clinical narratives and demographics extracted from case reports. |
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Schema |
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- case_id: case identifier (joins to images.patient_id) |
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- article_id: PMCID (joins to articles.article_id) |
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- case_text: clinical case narrative |
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- age: age in years (0 if <1 y.o.) |
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- gender: Female, Male, Transgender, Unknown |
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Quick start |
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```python |
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from datasets import load_dataset |
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cases = load_dataset("openmed-community/multicare-cases", split="train") |
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print(cases[0]["case_text"][:600]) |
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``` |
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Join with images |
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```python |
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from datasets import load_dataset |
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cases = load_dataset("openmed-community/multicare-cases", split="train") |
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imgs = load_dataset("openmed-community/multicare-images", split="train") |
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cid = cases[0]["case_id"] |
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imgs_for_case = imgs.filter(lambda e: e["patient_id"] == cid) |
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imgs_for_case[0]["image"].show() |
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``` |
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Notes |
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- No official splits; recommend patient/article-level splitting to avoid leakage. |
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- Per-item OA licenses are provided at the image level and via articles. |
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