Big Data in Cardiology: State-of-Art and Future Prospects

被引:15
作者
Dai, Haijiang [1 ,2 ]
Younis, Arwa [3 ]
Kong, Jude Dzevela [2 ]
Puce, Luca [4 ]
Jabbour, Georges [5 ]
Yuan, Hong [1 ]
Bragazzi, Nicola Luigi [2 ,4 ,6 ,7 ]
机构
[1] Cent South Univ, Xiangya Hosp 3, Dept Cardiol, Changsha, Peoples R China
[2] York Univ, Dept Math & Stat, Lab Ind & Appl Math LIAM, Toronto, ON, Canada
[3] Univ Rochester, Clin Cardiovasc Res Ctr, Med Ctr, Rochester, NY USA
[4] Univ Genoa, Dept Neurosci Rehabil Ophthalmol Genet Maternal &, Genoa, Italy
[5] Qatar Univ, Coll Educ, Phys Educ Dept, Doha, Qatar
[6] Univ Genoa, Postgrad Sch Publ Hlth, Dept Hlth Sci, Genoa, Italy
[7] Univ Leeds, Chapel Allerton Hosp, Leeds Inst Mol Med, NIHR Leeds Musculoskeletal Biomed Res Unit,Sect Mu, Leeds, England
关键词
Big Data; epidemiological registries; high-throughput technologies; wearable technologies; non-conventional data streams; cardiology; ARTIFICIAL-INTELLIGENCE; RISK-FACTORS; HYPERTROPHIC CARDIOMYOPATHY; CARDIOVASCULAR-DISEASES; QUALITY IMPROVEMENT; PRECISION MEDICINE; NATIONAL HEART; GLOBAL BURDEN; GO RED; HEALTH;
D O I
10.3389/fcvm.2022.844296
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.
引用
收藏
页数:13
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共 89 条
[1]   CardioNet: a manually curated database for artificial intelligence-based research on cardiovascular diseases [J].
Ahn, Imjin ;
Na, Wonjun ;
Kwon, Osung ;
Yang, Dong Hyun ;
Park, Gyung-Min ;
Gwon, Hansle ;
Kang, Hee Jun ;
Jeong, Yeon Uk ;
Yoo, Jungsun ;
Kim, Yunha ;
Jun, Tae Joon ;
Kim, Young-Hak .
BMC MEDICAL INFORMATICS AND DECISION MAKING, 2021, 21 (01)
[2]   Digital Technology Interventions for Risk Factor Modification in Patients With Cardiovascular Disease: Systematic Review and Meta-analysis [J].
Akinosun, Adewale Samuel ;
Polson, Rob ;
Diaz-Skeete, Yohanca ;
De Kock, Johannes Hendrikus ;
Carragher, Lucia ;
Leslie, Stephen ;
Grindle, Mark ;
Gorely, Trish .
JMIR MHEALTH AND UHEALTH, 2021, 9 (03)
[3]   CardioGenBase: A Literature Based Multi-Omics Database for Major Cardiovascular Diseases [J].
Alexandar, V ;
Nayar, Pradeep G. ;
Murugesan, R. ;
Mary, Beaulah ;
Darshana, P. ;
Ahmed, Shiek S. S. J. .
PLOS ONE, 2015, 10 (12)
[4]   Sex Differences in Case Fatality Rate of COVID-19: Insights From a Multinational Registry [J].
Alkhouli, Mohamad ;
Nanjundappa, Aravinda ;
Annie, Frank ;
Bates, Mark C. ;
Bhatt, Deepak L. .
MAYO CLINIC PROCEEDINGS, 2020, 95 (08) :1613-1620
[5]   The importance of patient-reported outcomes: a call for their comprehensive integration in cardiovascular clinical trials [J].
Anker, Stefan D. ;
Agewall, Stefan ;
Borggrefe, Martin ;
Calvert, Melanie ;
Caro, J. Jaime ;
Cowie, Martin R. ;
Ford, Ian ;
Paty, Jean A. ;
Riley, Jillian P. ;
Swedberg, Karl ;
Tavazzi, Luigi ;
Wiklund, Ingela ;
Kirchhof, Paulus .
EUROPEAN HEART JOURNAL, 2014, 35 (30) :2001-+
[6]   Big Data, Big Research Implementing Population Health-Based Research Models and Integrating Care to Reduce Cost and Improve Outcomes [J].
Anoushiravani, Afshin A. ;
Patton, Jason ;
Sayeed, Zain ;
El-Othmani, Mouhanad M. ;
Saleh, Khaled J. .
ORTHOPEDIC CLINICS OF NORTH AMERICA, 2016, 47 (04) :717-+
[7]   Machine learning of native T1 mapping radiomics for classification of hypertrophic cardiomyopathy phenotypes [J].
Antonopoulos, Alexios S. ;
Boutsikou, Maria ;
Simantiris, Spyridon ;
Angelopoulos, Andreas ;
Lazaros, George ;
Panagiotopoulos, Ioannis ;
Oikonomou, Evangelos ;
Kanoupaki, Mikela ;
Tousoulis, Dimitris ;
Mohiaddin, Raad H. ;
Tsioufis, Konstantinos ;
Vlachopoulos, Charalambos .
SCIENTIFIC REPORTS, 2021, 11 (01)
[8]   Cardiovascular risk stratification by coronary computed tomography angiography imaging: current state-of-the-art [J].
Antonopoulos, Alexios S. ;
Angelopoulos, Andreas ;
Tsioufis, Konstantinos ;
Antoniades, Charalambos ;
Tousoulis, Dimitris .
EUROPEAN JOURNAL OF PREVENTIVE CARDIOLOGY, 2022, 29 (04) :608-624
[9]   Predictive, preventive, personalized and participatory medicine: back to the future [J].
Auffray, Charles ;
Charron, Dominique ;
Hood, Leroy .
GENOME MEDICINE, 2010, 2
[10]   Incomplete Revascularization in Patients Treated With Percutaneous Coronary Intervention When Enough Is Enough [J].
Ayalon, Nir ;
Jacobs, Alice K. .
JACC-CARDIOVASCULAR INTERVENTIONS, 2016, 9 (03) :216-218