Artificial intelligence in cardiovascular prevention: new ways will open new doors

被引：0
作者：
Ciccarelli, Michele [1 ,9 ]
Giallauria, Francesco [2 ]
Carrizzo, Albino [1 ,3 ]
Visco, Valeria [1 ]
Silverio, Angelo [1 ]
Cesaro, Arturo [4 ]
Calabro, Paolo [4 ]
De Luca, Nicola [5 ]
Mancusi, Costantino [5 ]
Masarone, Daniele [6 ]
Pacileo, Giuseppe [6 ]
Tourkmani, Nidal [7 ,8 ]
Vigorito, Carlo [2 ]
Vecchione, Carmine [1 ,3 ]
机构：
[1] Univ Salerno, Dept Med Surg & Dent, Baronissi, Italy
[2] Univ Naples Federico II, Dept Translat Med Sci, Naples, Italy
[3] IRCCS Neuromed, Vasc Physiopathol Unit, Pozzilli, Italy
[4] Univ Campania Luigi Vanvitelli, Dept Translat Med Sci, Naples, Italy
[5] Univ Naples Federico II, Dept Adv Biomed Sci, Naples, Italy
[6] AORN Colli Monaldi Hosp Naples, Dept Cardiol, Heart Failure Unit, Naples, Italy
[7] Mons Giosue Calaciura Clin, Cardiol & Cardiac Rehabil Unit, Catania, Italy
[8] ABL, Guangzhou, Peoples R China
[9] Univ Salerno, Dept Med Surg & Dent, Via Salvador Allende, I-84081 Salerno, Italy
来源：
JOURNAL OF CARDIOVASCULAR MEDICINE | 2023年 / 24卷
关键词：
artificial intelligence; atrial fibrillation; cardiovascular prevention; coronary artery disease; heart failure; hypertension; machine learning; personalized medicine; risk stratification; CORONARY-ARTERY-DISEASE; INCIDENT ATRIAL-FIBRILLATION; FAILURE RISK SCORE; HEART-FAILURE; PREDICTION MODEL; HEALTH-CARE; VALIDATION; CARDIOLOGY; EVENTS; PERFORMANCE;
D O I：
暂无
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.
引用
收藏
页码：E106 / E115
页数：10
相关论文
共 142 条
  • [41] Artificial intelligence in cardiology: The past, present and future
    Gupta, Mohit D.
    Kunal, Shekhar
    Girish, M. P.
    Gupta, Anubha
    Yadav, Rakesh
    [J]. INDIAN HEART JOURNAL, 2022, 74 (04) : 265 - 269
  • [42] Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets
    Hadanny, Amir
    Shouval, Roni
    Wu, Jianhua
    Shlomo, Nir
    Unger, Ron
    Zahger, Doron
    Matetzky, Shlomi
    Goldenberg, Ilan
    Beigel, Roy
    Gale, Chris
    Iakobishvili, Zaza
    [J]. JOURNAL OF CARDIOLOGY, 2021, 78 (05) : 439 - 446
  • [43] Simple risk model and score for predicting of incident atrial fibrillation in Japanese
    Hamada, Richiro
    Muto, Shigeki
    [J]. JOURNAL OF CARDIOLOGY, 2019, 73 (1-2) : 65 - 72
  • [44] Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
    Hannun, Awni Y.
    Rajpurkar, Pranav
    Haghpanahi, Masoumeh
    Tison, Geoffrey H.
    Bourn, Codie
    Turakhia, Mintu P.
    Ng, Andrew Y.
    [J]. NATURE MEDICINE, 2019, 25 (01) : 65 - +
  • [45] Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks
    He, Runnan
    Wang, Kuanquan
    Zhao, Na
    Liu, Yang
    Yuan, Yongfeng
    Li, Qince
    Zhang, Henggui
    [J]. FRONTIERS IN PHYSIOLOGY, 2018, 9
  • [46] Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome - the MADDEC study
    Hernesniemi, Jussi A.
    Mahdiani, Shadi
    Tynkkynen, Juho A.
    Lyytikainen, Leo-Pekka
    Mishra, Pashupati P.
    Lehtimaki, Terho
    Eskola, Markku
    Nikus, Kjell
    Antila, Kari
    Oksala, Niku
    [J]. ANNALS OF MEDICINE, 2019, 51 (02) : 156 - 163
  • [47] Heywood JT., 2022, J Card Fail
  • [48] Predicting atrial fibrillation in primary care using machine learning
    Hill, Nathan R.
    Ayoubkhani, Daniel
    McEwan, Phil
    Sugrue, Daniel M.
    Farooqui, Usman
    Lister, Steven
    Lumley, Matthew
    Bakhai, Ameet
    Cohen, Alexander T.
    O'Neill, Mark
    Clifton, David
    Gordon, Jason
    [J]. PLOS ONE, 2019, 14 (11):
  • [49] 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)
    Hindricks, Gerhard
    Potpara, Tatjana
    Dagres, Nikolaos
    Arbelo, Elena
    Bax, Jeroen J.
    Blomstroem-Lundqvist, Carina
    Boriani, Giuseppe
    Castella, Manuel
    Dan, Gheorghe-Andrei
    Dilaveris, Polychronis E.
    Fauchier, Laurent
    Filippatos, Gerasimos
    Kalman, Jonathan M.
    La Meir, Mark
    Lane, Deirdre A.
    Lebeau, Jean-Pierre
    Lettino, Maddalena
    Lip, Gregory Y. H.
    Pinto, Fausto J.
    Thomas, G. Neil
    Valgimigli, Marco
    Van Gelder, Isabelle C.
    Van Putte, Bart P.
    Watkins, Caroline L.
    [J]. EUROPEAN HEART JOURNAL, 2021, 42 (05) : 373 - 498
  • [50] Prediction of current and new development of atrial fi brillation on electrocardiogram with sinus rhythm in patients without structural heart disease
    Hirota, Naomi
    Suzuki, Shinya
    Arita, Takuto
    Yagi, Naoharu
    Otsuka, Takayuki
    Kishi, Mikio
    Semba, Hiroaki
    Kano, Hiroto
    Matsuno, Shunsuke
    Kato, Yuko
    Uejima, Tokuhisa
    Oikawa, Yuji
    Matsuhama, Minoru
    Inoue, Tatsuya
    Yajima, Junji
    Yamashita, Takeshi
    [J]. INTERNATIONAL JOURNAL OF CARDIOLOGY, 2021, 327 : 93 - 99
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