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

被引：0
作者：
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
机构：
[1] University of Ulsan College of Medicine,Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center
[2] The Catholic University of Korea,Division of Cardiology, Department of Internal Medicine, Eunpyeong St. Mary’s Hospital
[3] University of Ulsan College of Medicine,Department of Radiology, Asan Medical Center
[4] University of Ulsan College of Medicine,Department of Cardiology, Ulsan University Hospital
[5] Asan Medical Center,Big Data Research Center, Asan Institute for Life Sciences
[6] University of Ulsan College of Medicine,Division of Cardiology, Department of Internal Medicine, Asan Medical Center
来源：
BMC Medical Informatics and Decision Making | / 21卷
关键词：
Cardiovascular diseases; Database; Artificial intelligence; Electronic health records;
D O I：
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[1]  
McKinney SM(2020)International evaluation of an ai system for breast cancer screening Nature 577 89-94
[2]  
Sieniek M(2018)Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning Nat Biomed Eng 2 158-961
[3]  
Godbole V(2019)End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography Nat Med 25 954-2024
[4]  
Godwin J(2014)The multimodal brain tumor image segmentation benchmark (brats) IEEE Trans Med Imaging 34 1993-867
[5]  
Antropova N(2019)An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction The Lancet 394 861-58
[6]  
Ashrafian H(2020)Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks Nat Med 26 52-119
[7]  
Back T(2019)A clinically applicable approach to continuous prediction of future acute kidney injury Nature 572 116-162
[8]  
Chesus M(1954)Distributional structure Word 10 146-633
[9]  
Corrado GC(2015)Observational health data sciences and informatics (OHDSI): opportunities for observational researchers Stud Health Technol Inform 216 574-23
[10]  
Darzi A(2006)Snomed-ct: the advanced terminology and coding system for ehealth Stud Health Technol Inform 121 279-855
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