awinml/pubmed_case_reports

PubMed Case Reports

A collection of 13,989 full-text case reports from the PubMed Central (PMC) Open Access subset, spanning 2005–2025. Each article includes structured metadata, abstract, full body text, and section-level annotations. This dataset is designed for medical NLP, clinical reasoning, and biomedical text mining.

Dataset Description

Summary

This dataset comprises case reports published in peer-reviewed medical journals, sourced from the PMC Open Access Subset. Case reports are detailed accounts of individual patient encounters — including presentation, diagnosis, treatment, and outcome — making them a rich resource for clinical reasoning research.

Each row corresponds to one article with:

• Bibliographic metadata (title, journal, publication date, PMCID, license)
• Structured abstract and full body text
• Section-level breakdown of the body text with normalised section types (background, case presentation, investigations, management, outcome, discussion, etc.)

Supported Tasks

Task	Description
Clinical text generation	Pre-train or fine-tune language models on medical case report text
Abstractive QA / summarisation	Generate summaries of clinical encounters from full text
Section classification	Classify paragraphs by clinical section type
Information extraction	Extract medical entities, relations, and treatment-outcome pairs
Clinical reasoning evaluation	Use as a knowledge source for retrieval-augmented generation (RAG) pipelines

Languages

All articles are in English.

Dataset Structure

Data Fields

Field	Type	Description
pmcid	string	PubMed Central identifier (e.g., PMC1234567)
title	string	Article title
journal	string	Journal name
publication_date	string	Publication date in YYYY-MM-DD format
article_link	string	URL to the article on PubMed Central
license	string	License text or URL
abstract	string	Article abstract (may be empty for some entries)
body_text	string	Full body text, concatenated from all sections
sections	list[dict]	Section-level breakdown (see below)
background	string	Concatenated text of all background‑type sections
case_presentation	string	Concatenated text of all case_presentation‑type sections
discussion	string	Concatenated text of all discussion‑type sections
conclusion	string	Concatenated text of all conclusion‑type sections
investigations	string	Concatenated text of all investigations‑type sections
management	string	Concatenated text of all management‑type sections
outcome	string	Concatenated text of all outcome‑type sections
other	string	Concatenated text of all other‑type sections (declarations, ethics, supplementary, etc.)
references	string	Concatenated text of all references‑type sections

Each element in the sections list is a dictionary with:

Sub-field	Type	Description
section_type	string	Normalised section type (one of: abstract, background, case_presentation, investigations, management, outcome, discussion, conclusion, references, other)
heading	string	Original section heading (e.g., "Case Presentation", "Discussion")
text	string	Section body text

In addition to the nested sections list, each section type is also available as a top-level string column (e.g. background, case_presentation, discussion) containing the concatenated text of all sections of that type, making it easy to access specific clinical sections without iterating over lists.

Data Splits

The dataset contains a single split:

Split	Size
train	13,989

Data Instance Example

{
  "pmcid": "PMC10000501",
  "title": "Multisystem Inflammatory Syndrome in Adults Associated with Recent Infection with COVID-19",
  "journal": "Diagnostics",
  "publication_date": "2023-03-04",
  "article_link": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10000501/",
  "license": "© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license",
  "abstract": "Multisystem inflammatory syndrome in adults (MIS-A) is a rare but severe complication of coronavirus disease 2019 (COVID-19)...",
  "body_text": "Background\\n\\nMultisystem inflammatory syndrome in adults (MIS-A) is a rare but severe complication...",
  "sections": [
    {"section_type": "background", "heading": "1. Introduction", "text": "Multisystem inflammatory syndrome in adults (MIS-A) is a rare..."},
    {"section_type": "case_presentation", "heading": "2. Case Report", "text": "A 34-year-old male with no significant past medical history..."},
    {"section_type": "discussion", "heading": "3. Discussion", "text": "MIS-A is a newly recognized entity that presents with..."},
    {"section_type": "conclusion", "heading": "4. Conclusions", "text": "This case highlights the importance of considering MIS-A..."}
  ],
  "background": "Multisystem inflammatory syndrome in adults (MIS-A) is a rare...",
  "case_presentation": "A 34-year-old male with no significant past medical history...",
  "discussion": "MIS-A is a newly recognized entity that presents with...",
  "conclusion": "This case highlights the importance of considering MIS-A...",
  "investigations": "",
  "management": "",
  "outcome": "",
  "other": "",
  "references": ""
}

Dataset Statistics

Overview

Metric	Value
Total articles	13,989
Unique PMCIDs	13,989
Unique journals	811
Year range	2005–2025
Total characters (abstract + body)	~171 M
Total words (abstract + body)	~25 M

Per-Column Statistics

Column	Non-empty	Mean chars	Mean words	Min chars	Max chars
title	13,989	92	13	7	305
abstract	13,527	1,038	148	0	4,171
body_text	13,988	11,174	1,635	0	51,830
sections	13,988	6.0 sec/row	—	0	42
background	13,215 (94.5%)	1,248	184	0	18,594
case_presentation	11,557 (82.6%)	3,082	452	0	20,614
discussion	12,940 (92.5%)	4,350	637	0	23,891
conclusion	8,227 (58.8%)	356	52	0	14,062
investigations	468 (3.3%)	64	9	0	9,375
management	918 (6.6%)	75	11	0	8,123
outcome	161 (1.2%)	7	1	0	5,412
other	9,311 (66.6%)	1,982	289	0	49,658
references	0 (0.0%)	0	0	0	0

Note: Section-type columns (background, case_presentation, etc.) contain empty strings when no section of that type is present in an article. The "Non-empty" column shows how many rows have content for each field.

Publication Years

Year Range	Articles
2005–2010	418
2011–2015	2,498
2016–2020	4,167
2021–2025	7,205

The collection skews toward recent publications, with the majority (51%) from 2021–2025.

Top Journals

Articles	Journal
1,136	Journal of Medical Case Reports
1,057	Clinical Case Reports
959	International Journal of Surgery Case Reports
886	Radiology Case Reports
403	Journal of Surgical Case Reports
345	JAAD Case Reports
299	SAGE Open Medical Case Reports
294	Journal of Orthopaedic Case Reports
263	Case Reports in Medicine
228	European Heart Journal: Case Reports
204	American Journal of Ophthalmology Case Reports
190	Case Reports in Dentistry

License Distribution (Top 10)

Count	License
2,412	CC BY-NC-ND 4.0
2,227	Creative Commons Attribution License
1,321	Terms of CC Attribution license
907	CC BY 4.0
774	CC BY-NC 4.0
703	CC BY 3.0
609	CC BY-NC-SA 4.0
567	CC BY-NC-ND 3.0
420	CC BY-SA 4.0
386	CC BY 2.0

Section Types

Sections are normalised into the following categories:

Section Type	Coverage	Description
background	94.5%	Introduction, background
case_presentation	82.6%	Case report, clinical history, patient presentation
discussion	92.5%	Discussion, differential diagnosis
conclusion	58.8%	Conclusion
other	66.6%	Declarations, ethics, supplementary, etc.
management	6.6%	Treatment, therapeutic intervention
investigations	3.3%	Diagnostic workup, lab findings, imaging
outcome	1.2%	Follow-up, outcome
references	<0.1%	References
abstract¹	—	Article abstract (stored as top-level abstract column)

¹ abstract section types from the body are not duplicated as a separate column since the article's structured abstract is already available as the top-level abstract field.

Dataset Creation

Source Data

The raw XML files were downloaded from the PMC Open Access Subset via the PMC Cloud Service S3 bucket (pmc-oa-opendata).

Curation Process

1. Discovery: Case report PMCIDs were discovered by querying PubMed with the Case Reports[pt] filter, segmented by year to stay within the 10K-result cap.
2. Download: Full-text XML was downloaded in parallel using async HTTP (aiohttp) from the PMC Cloud Service S3 bucket.
3. Parsing: Each XML file was parsed to extract:
   • Article metadata (title, journal, publication date, license, abstract)
   • Section-aware body text with normalised section type labels
   • Text cleaning (citation markers, figure references, whitespace normalisation)
4. Format: The parsed data was saved as a Parquet file.

Curation Rationale

Clinical case reports are a uniquely valuable genre of medical literature. Unlike randomised trials or systematic reviews, case reports provide fine-grained, narrative descriptions of individual patient journeys — from initial presentation through diagnosis, treatment, and follow-up. This makes them especially suited for:

• Evaluating clinical reasoning capabilities of language models
• Building retrieval-augmented generation systems for rare conditions
• Training models to understand the structure of clinical narratives

Usage

Loading the Dataset

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("awinml/pubmed_case_reports", split="train")

# Access a single example
example = ds[0]
print(example["title"])
print(example["abstract"])

# Iterate in streaming mode (low memory)
ds_stream = load_dataset("awinml/pubmed_case_reports", split="train", streaming=True)
for i, row in enumerate(ds_stream):
    if i >= 10:
        break
    print(row["pmcid"], row["title"][:80])

Working with Sections

The dataset provides two ways to access section text:

1. Via the sections list (full detail with original headings):

from datasets import load_dataset

ds = load_dataset("awinml/pubmed_case_reports", split="train")

for row in ds:
    sections = row["sections"]
    presentation = [s for s in sections if s["section_type"] == "case_presentation"]
    if presentation:
        print(f"PMCID: {row['pmcid']}")
        print(f"Heading: {presentation[0]['heading']}")
        print(f"Text: {presentation[0]['text'][:200]}...")
        break

2. Via direct section columns (simpler, concatenated across all sections of that type):

from datasets import load_dataset

ds = load_dataset("awinml/pubmed_case_reports", split="train")

for row in ds:
    if row["case_presentation"]:
        print(f"PMCID: {row['pmcid']}")
        print(f"Case presentation: {row['case_presentation'][:200]}...")
        break

3. Section length analysis with pandas:

from datasets import load_dataset
import pandas as pd

ds = load_dataset("awinml/pubmed_case_reports", split="train")
df = ds.to_pandas()

# Which sections are most common?
for col in ["background", "case_presentation", "discussion", "conclusion"]:
    pct = (df[col].str.len() > 0).mean() * 100
    print(f"{col}: {pct:.1f}% of articles have this section")

# Average discussion length
df[df["discussion"] != ""]["discussion"].str.len().describe()

Converting to Pandas

import pandas as pd
from datasets import load_dataset

ds = load_dataset("awinml/pubmed_case_reports", split="train")
df = ds.to_pandas()

# Journal distribution
print(df["journal"].value_counts().head(10))

# Average body text length by year
df["year"] = pd.to_datetime(df["publication_date"]).dt.year
print(df.groupby("year")["body_text"].apply(lambda x: x.str.len().mean()))

Retrieval-Augmented Generation

from datasets import load_dataset
from sentence_transformers import SentenceTransformer

# Load and chunk for RAG
ds = load_dataset("awinml/pubmed_case_reports", split="train", streaming=True)

# Build a simple in-memory index from body texts
corpus = []
pmcids = []
for i, row in enumerate(ds):
    if i >= 1000:
        break
    corpus.append(row["body_text"][:2000])  # first 2000 chars
    pmcids.append(row["pmcid"])

model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = model.encode(corpus, show_progress_bar=True)

Limitations & Considerations

• Case reports are inherently anecdotal: They describe individual patient experiences and should not be treated as population-level evidence.
• Publication bias: Journals are more likely to publish rare or novel cases, so the dataset may overrepresent unusual presentations.
• Temporal skew: The collection is weighted toward recent publications (51% from 2021–2025).
• No structured outcome labels: The dataset does not include standardised diagnostic or treatment outcome labels — this is a raw text corpus.
• License variability: Articles carry different Creative Commons licenses. Users should verify license compatibility for their specific use case (see the license field per row).
• No PHI redaction guarantee: While the source articles are published in open-access journals, individual case reports may contain identifiable patient information. Users should exercise appropriate caution.

Citation

If you use this dataset, please cite it as:

@misc{awinml_pubmed_case_reports_2025,
  author       = {Ashwin Mathur},
  title        = {PubMed Case Reports: A Dataset of Full-Text Clinical Case Reports from PMC},
  year         = {2025},
  publisher    = {Hugging Face},
  journal      = {Hugging Face Datasets},
  howpublished = {\\url{https://huggingface.co/datasets/awinml/pubmed_case_reports}}
}

License

The dataset itself is released under CC BY 4.0. Individual articles carry their own licenses as specified in the license field and may have additional restrictions.

Contact

For questions or feedback, open an issue on the dataset repository.