---
license: cc-by-nc-sa-4.0
language:
- en
tags:
- information retrieval
- patient similarity
- clinical decision support
size_categories:
- 100K<n<1M
---

# Dataset Card for PMC-Patients-ReCDS

## Dataset Description

- **Homepage:** https://github.com/pmc-patients/pmc-patients
- **Repository:** https://github.com/pmc-patients/pmc-patients
- **Paper:** https://arxiv.org/pdf/2202.13876.pdf
- **Leaderboard:** https://pmc-patients.github.io/
- **Point of Contact:** zhengyun21@mails.tsinghua.edu.cn

### Dataset Summary

**PMC-Patients** is a first-of-its-kind dataset consisting of 167k patient summaries extracted from case reports in PubMed Central (PMC), 3.1M patient-article relevance and 293k patient-patient similarity annotations defined by PubMed citation graph.

### Supported Tasks and Leaderboards

Based on PMC-Patients, we define two tasks to benchmark Retrieval-based Clinical Decision Support (ReCDS) systems: Patient-to-Article Retrieval (PAR) and Patient-to-Patient Retrieval (PPR). 
For details, please refer to [our paper](https://arxiv.org/pdf/2202.13876.pdf) and [leaderboard](https://pmc-patients.github.io/).

### Languages

English (en).

## Dataset Structure

The PMC-Patients ReCDS benchmark is presented as retrieval tasks and the data format is the same as [BEIR](https://github.com/beir-cellar/beir) benchmark. 
To be specific, there are queries, corpus, and qrels (annotations).

### Queries

ReCDS-PAR and ReCDS-PPR tasks share the same query patient set and dataset split.
For each split (train, dev, and test), queries are stored a `jsonl` file that contains a list of dictionaries, each with two fields: 
- `_id`: unique query identifier represented by patient_uid.
- `text`: query text represented by patient summary text.

### Corpus

Corpus is shared by different splits. For ReCDS-PAR, the corpus contains 11.7M PubMed articles, and for ReCDS-PPR, the corpus contains 155.2k reference patients from PMC-Patients. The corpus is also presented by a `jsonl` file that contains a list of dictionaries with three fields:
- `_id`:  unique document identifier represented by PMID of the PubMed article in ReCDS-PAR, and patient_uid of the candidate patient in ReCDS-PPR.
- `title`: : title of the article in ReCDS-PAR, and empty string in ReCDS-PPR.
- `text`: abstract of the article in ReCDS-PAR, and patient summary text in ReCDS-PPR.

### Qrels

Qrels are TREC-style retrieval annotation files in `tsv` format.
A qrels file contains three tab-separated columns, i.e. the query identifier, corpus identifier, and score in this order. The scores (2 or 1) indicate the relevance level in ReCDS-PAR or similarity level in ReCDS-PPR.

Note that the qrels may not be the same as `relevant_articles` and `similar_patients` in `PMC-Patients.json` due to dataset split (see our manuscript for details).

### Data Instances


**A sample of query**

{"_id": "8699387-1", "text": "A 60-year-old female patient with a medical history of hypertension came to our attention because of several neurological deficits that had developed over the last few years, significantly impairing her daily life. Four years earlier, she developed sudden weakness and hypoesthesia of the right hand. The symptoms resolved in a few days and no specific diagnostic tests were performed. Two months later, she developed hypoesthesia and weakness of the right lower limb. On neurological examination at the time, she had spastic gait, ataxia, slight pronation of the right upper limb and bilateral Babinski sign. Brain MRI showed extensive white matter hyperintensities (WMHs), so leukodystrophy was suspected. However, these WMHs were located bilaterally in the corona radiata, basal ganglia, the anterior part of the temporal lobes and the medium cerebellar peduncle (A–D), and were highly suggestive of CADASIL. Genetic testing was performed, showing heterozygous mutation of the NOTCH3 gene (c.994 C<T; exon 6). The diagnosis of CADASIL was confirmed and antiplatelet prevention therapy was started. Since then, her clinical conditions remained stable, and the lesion load was unchanged at follow-up brain MRIs for 4 years until November 2020, when the patient was diagnosed with COVID-19 after a PCR nasal swab. The patient developed only mild respiratory symptoms, not requiring hospitalization or any specific treatment. Fifteen days after the COVID-19 diagnosis, she suddenly developed aphasia, agraphia and worsened right upper limb motor deficit, but she did not seek medical attention. Some days later, she reported these symptoms to her family medical doctor, and a new brain MRI was performed, showing a subacute ischemic area in the left corona radiata (E,F). Therapy with acetylsalicylic acid was switched to clopidogrel as secondary prevention, while her symptoms improved in the next few weeks. The patient underwent a carotid doppler ultrasound and an echocardiogram, which did not reveal any pathological changes. The review of the blood pressure log, both in-hospital and the personal one the patient had kept, excluded uncontrolled hypertension."}

**A sample of qrels**

query-id	corpus-id	score

8647806-1	6437752-1	1

8647806-1	6946242-1	1


### Data Splits

Refer to our paper.

## Dataset Creation

If you are interested in the collection of PMC-Patients and reproducing our baselines, please refer to [this reporsitory](https://github.com/zhao-zy15/PMC-Patients).

### Citation Information

If you find PMC-Patients helpful in your research, please cite our work by:
```
@misc{zhao2023pmcpatients,
      title={PMC-Patients: A Large-scale Dataset of Patient Summaries and Relations for Benchmarking Retrieval-based Clinical Decision Support Systems}, 
      author={Zhengyun Zhao and Qiao Jin and Fangyuan Chen and Tuorui Peng and Sheng Yu},
      year={2023},
      eprint={2202.13876},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```