The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ValueError
Message: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/zhengyun21/PMC-Patients@28d8836518f86d4f1e6358ea8ec09977023e5766/PMC-Patients-V2.json.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 233, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
return _examples_to_batch(list(self.take(n)))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
for key, pa_table in self.generate_tables_fn(**gen_kwags):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 172, in _generate_tables
raise ValueError(
ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/zhengyun21/PMC-Patients@28d8836518f86d4f1e6358ea8ec09977023e5766/PMC-Patients-V2.json.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for PMC-Patients
News
We released PMC-Patients-V2 (in JSON format with the same keys), which is based on 2024 PMC baseline and contains 250,294 patients. The data collection pipeline remains the same except for using more PMC articles.
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
This is purely the patient summary dataset with relational annotations. For ReCDS benchmark, refer to this dataset
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 and leaderboard.
Languages
English (en).
Dataset Structure
PMC-Paitents.csv
This file contains all information about patients summaries in PMC-Patients, with the following columns:
patient_id: string. A continuous id of patients, starting from 0.patient_uid: string. Unique ID for each patient, with format PMID-x, where PMID is the PubMed Identifier of the source article of the patient and x denotes index of the patient in source article.PMID: string. PMID for source article.file_path: string. File path of xml file of source article.title: string. Source article title.patient: string. Patient summary.age: list of tuples. Each entry is in format(value, unit)where value is a float number and unit is in 'year', 'month', 'week', 'day' and 'hour' indicating age unit. For example,[[1.0, 'year'], [2.0, 'month']]indicating the patient is a one-year- and two-month-old infant.gender: 'M' or 'F'. Male or Female.relevant_articles: dict. The key is PMID of the relevant articles and the corresponding value is its relevance score (2 or 1 as defined in the ``Methods'' section).similar_patients: dict. The key is patient_uid of the similar patients and the corresponding value is its similarity score (2 or 1 as defined in the ``Methods'' section).
Dataset Creation
If you are interested in the collection of PMC-Patients and reproducing our baselines, please refer to this reporsitory.
Citation Information
If you find PMC-Patients helpful in your research, please cite our work by:
@article{zhao2023large,
title={A large-scale dataset of patient summaries for retrieval-based clinical decision support systems},
author={Zhao, Zhengyun and Jin, Qiao and Chen, Fangyuan and Peng, Tuorui and Yu, Sheng},
journal={Scientific Data},
volume={10},
number={1},
pages={909},
year={2023},
publisher={Nature Publishing Group UK London}
}
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