CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases
Abstract Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However...
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2021-01-01
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Series: | BMC Medical Informatics and Decision Making |
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Online Access: | https://doi.org/10.1186/s12911-021-01392-2 |
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record_format |
Article |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Imjin Ahn Wonjun Na Osung Kwon Dong Hyun Yang Gyung-Min Park Hansle Gwon Hee Jun Kang Yeon Uk Jeong Jungsun Yoo Yunha Kim Tae Joon Jun Young-Hak Kim |
spellingShingle |
Imjin Ahn Wonjun Na Osung Kwon Dong Hyun Yang Gyung-Min Park Hansle Gwon Hee Jun Kang Yeon Uk Jeong Jungsun Yoo Yunha Kim Tae Joon Jun Young-Hak Kim CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases BMC Medical Informatics and Decision Making Cardiovascular diseases Database Artificial intelligence Electronic health records |
author_facet |
Imjin Ahn Wonjun Na Osung Kwon Dong Hyun Yang Gyung-Min Park Hansle Gwon Hee Jun Kang Yeon Uk Jeong Jungsun Yoo Yunha Kim Tae Joon Jun Young-Hak Kim |
author_sort |
Imjin Ahn |
title |
CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases |
title_short |
CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases |
title_full |
CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases |
title_fullStr |
CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases |
title_full_unstemmed |
CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases |
title_sort |
cardionet: a manually curated database for artificial intelligence-based research on cardiovascular diseases |
publisher |
BMC |
series |
BMC Medical Informatics and Decision Making |
issn |
1472-6947 |
publishDate |
2021-01-01 |
description |
Abstract Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects. |
topic |
Cardiovascular diseases Database Artificial intelligence Electronic health records |
url |
https://doi.org/10.1186/s12911-021-01392-2 |
work_keys_str_mv |
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doaj-cf1ca6249cc043c8bafa339f7468d7a92021-01-31T16:33:31ZengBMCBMC Medical Informatics and Decision Making1472-69472021-01-0121111510.1186/s12911-021-01392-2CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseasesImjin Ahn0Wonjun Na1Osung Kwon2Dong Hyun Yang3Gyung-Min Park4Hansle Gwon5Hee Jun Kang6Yeon Uk Jeong7Jungsun Yoo8Yunha Kim9Tae Joon Jun10Young-Hak Kim11Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDivision of Cardiology, Department of Internal Medicine, Eunpyeong St. Mary’s Hospital, The Catholic University of KoreaDepartment of Radiology, Asan Medical Center, University of Ulsan College of MedicineDepartment of Cardiology, Ulsan University Hospital, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of MedicineDepartment of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of MedicineBig Data Research Center, Asan Institute for Life Sciences, Asan Medical CenterDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of MedicineAbstract Background Cardiovascular diseases (CVDs) are difficult to diagnose early and have risk factors that are easy to overlook. Early prediction and personalization of treatment through the use of artificial intelligence (AI) may help clinicians and patients manage CVDs more effectively. However, to apply AI approaches to CVDs data, it is necessary to establish and curate a specialized database based on electronic health records (EHRs) and include pre-processed unstructured data. Methods To build a suitable database (CardioNet) for CVDs that can utilize AI technology, contributing to the overall care of patients with CVDs. First, we collected the anonymized records of 748,474 patients who had visited the Asan Medical Center (AMC) or Ulsan University Hospital (UUH) because of CVDs. Second, we set clinically plausible criteria to remove errors and duplication. Third, we integrated unstructured data such as readings of medical examinations with structured data sourced from EHRs to create the CardioNet. We subsequently performed natural language processing to structuralize the significant variables associated with CVDs because most results of the principal CVD-related medical examinations are free-text readings. Additionally, to ensure interoperability for convergent multi-center research, we standardized the data using several codes that correspond to the common data model. Finally, we created the descriptive table (i.e., dictionary of the CardioNet) to simplify access and utilization of data for clinicians and engineers and continuously validated the data to ensure reliability. Results CardioNet is a comprehensive database that can serve as a training set for AI models and assist in all aspects of clinical management of CVDs. It comprises information extracted from EHRs and results of readings of CVD-related digital tests. It consists of 27 tables, a code-master table, and a descriptive table. Conclusions CardioNet database specialized in CVDs was established, with continuing data collection. We are actively supporting multi-center research, which may require further data processing, depending on the subject of the study. CardioNet will serve as the fundamental database for future CVD-related research projects.https://doi.org/10.1186/s12911-021-01392-2Cardiovascular diseasesDatabaseArtificial intelligenceElectronic health records |