Abstract
Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (https://github.com/Aitslab/openchart-se). Additional batches of quality-controlled EHRs will be released periodically.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
This study was supported by a grant to Science for Life Laboratory from the Knut and Alice Wallenberg (KAW) Foundation (S.A. 2020.0182), which was distributed through the SciLifeLab and KAW National COVID-19 Research Program. The project is conducted in the AI Lund research environment at Lund University.
Author Declarations
I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.
Yes
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Yes
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I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.
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Data Availability
All data produced are contained in the manuscript or available online athttps://doi.org/ https://zenodo.org/record/7499831.
https://doi.org/ https://zenodo.org/record/7499831