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OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project

Berg, Johanna LU ; Aasa, Carl ; Appelgren Thorell, Björn and Aits, Sonja LU orcid (2023)
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... (More)
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. (Less)
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author
; ; and
organization
publishing date
type
Working paper/Preprint
publication status
published
subject
keywords
electronic health records, natural language processing, artificial intelligence, citizen science
publisher
medRxiv
DOI
10.1101/2023.01.03.23284160
project
Lund University AI Research
Artificial intelligence-based text mining for COVID-19 and other areas of medicine
language
English
LU publication?
yes
id
6805cd9a-6ff0-4209-88b0-79b7c0699500
date added to LUP
2023-01-07 12:09:01
date last changed
2024-05-16 08:46:00
@misc{6805cd9a-6ff0-4209-88b0-79b7c0699500,
  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.}},
  author       = {{Berg, Johanna and Aasa, Carl and Appelgren Thorell, Björn and Aits, Sonja}},
  keywords     = {{electronic health records; natural language processing; artificial intelligence; citizen science}},
  language     = {{eng}},
  month        = {{01}},
  note         = {{Preprint}},
  publisher    = {{medRxiv}},
  title        = {{OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project}},
  url          = {{http://dx.doi.org/10.1101/2023.01.03.23284160}},
  doi          = {{10.1101/2023.01.03.23284160}},
  year         = {{2023}},
}