دورية أكاديمية
أعرض في EDS

The Use of Machine Learning for the Care of Hypertension and Heart Failure.

التفاصيل البيبلوغرافية
العنوان: The Use of Machine Learning for the Care of Hypertension and Heart Failure.
المؤلفون: Cai A; Department of Cardiology, Guangdong Cardiovascular Institute, Hypertension Research Laboratory, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China., Zhu Y; Department of Cardiology, Guangdong Cardiovascular Institute, Hypertension Research Laboratory, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China., Clarkson SA; Division of Cardiovascular Disease, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA., Feng Y; Department of Cardiology, Guangdong Cardiovascular Institute, Hypertension Research Laboratory, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
المصدر: JACC. Asia [JACC Asia] 2021 Sep 21; Vol. 1 (2), pp. 162-172. Date of Electronic Publication: 2021 Sep 21 (Print Publication: 2021).
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: Elsevier on behalf of the American College of Cardiology Foundation Country of Publication: United States NLM ID: 9918452380106676 Publication Model: eCollection Cited Medium: Internet ISSN: 2772-3747 (Electronic) Linking ISSN: 27723747 NLM ISO Abbreviation: JACC Asia Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: [New York City] : Elsevier on behalf of the American College of Cardiology Foundation, [2021]-
مستخلص: Machine learning (ML) is a branch of artificial intelligence that combines computer science, statistics, and decision theory to learn complex patterns from voluminous data. In the last decade, accumulating evidence has shown the utility of ML for prediction, diagnosis, and classification of hypertension and heart failure (HF). In addition, ML-enabled image analysis has potential value in assessing cardiac structure and function in an accurate, scalable, and efficient way. Considering the high burden of hypertension and HF in China and worldwide, ML may help address these challenges from different aspects. Indeed, prior studies have shown that ML can enhance each stage of patient care, from research and development, to daily clinical practice and population health. Through reviewing the published literature, the aims of the current systemic review are to summarize the utilities of ML for the care of those with hypertension and HF.
Competing Interests: Dr Feng has received funding from the National Key Research and Development Program of China (No.2017YFC1307603), the Natural Science Foundation of Guangdong Province (No.2020A1515010738), Science and Technology Plan Program of Guangzhou (No.201803040012), the Key Area R&D Program of Guangdong Province (No.2019B020227005), Guangdong Provincial People's Hospital Clinical Research Fund (Y012018085), and Climbing Plan of Guangdong Provincial People's Hospital (DFJH2020022). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
(© 2021 The Authors.)
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فهرسة مساهمة: Keywords: ANN, artificial neural network; AUC, area under the curve; CNN, convolutional neural network; HFpEF, heart failure with preserved ejection fraction; LRM, linear or logistic regression model; LVDD, left ventricular diastolic dysfunction; LVH, left ventricular hypertrophy; ML, machine learning; RF, random forest; SVM, support vector machine; algorithms; heart failure; hypertension machine learning
تواريخ الأحداث: Date Created: 20221107 Latest Revision: 20221108
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9627876
DOI: 10.1016/j.jacasi.2021.07.005
PMID: 36338169
قاعدة البيانات: MEDLINE
الوصف
تدمد:2772-3747
DOI:10.1016/j.jacasi.2021.07.005