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CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases

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Abstract
BackgroundCardiovascular 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.MethodsTo 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.ResultsCardioNet 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.ConclusionsCardioNet 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.
Author(s)
강희준권오성권한슬김영학김윤하나원준박경민안임진양동현유정선전태준정연욱
Issued Date
2021
Type
Article
Keyword
Artificial IntelligenceCardiovascular diseasesCardiovascular Diseases - diagnosisCardiovascular Diseases -epidemiologyDatabaseFactualElectronic health recordsHumans Natural Language ProcessingReproducibility of Results
DOI
 10.1186/s12911-021-01392-2
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7786
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_cf1ca6249cc043c8bafa339f7468d7a9&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,CardioNet:%20a%20manually%20curated%20database%20for%20artificial%20intelligence-based%20research%20on%20cardiovascular%20diseases&offset=0&pcAvailability=true
Publisher
BMC Medical Informatics and Decision Making
Location
영국
Language
영어
ISSN
1472-6947
Citation Volume
21
Citation Number
1
Citation Start Page
29
Citation End Page
29
Appears in Collections:
Medicine > Medicine
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