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

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.

[1]  Yahao Zhang,et al.  Performance of the heart failure risk scores in predicting 1 year mortality and short‐term readmission of patients , 2022, ESC heart failure.

[2]  G. Grassi,et al.  Accuracy of home blood pressure measurement: the ACCURAPRESS study – a proposal of Young Investigator Group of the Italian Hypertension Society (Società Italiana dell’Ipertensione Arteriosa) , 2022, Blood pressure.

[3]  Akshay S. Desai,et al.  Sustained Reduction in Pulmonary Artery Pressures and Hospitalizations During 2 Years of Ambulatory Monitoring. , 2022, Journal of cardiac failure.

[4]  Jorge A Cuadros,et al.  Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes , 2022, Journal of diabetes science and technology.

[5]  A. Hubbard,et al.  Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study , 2022, BMC Medicine.

[6]  K. Seetharam,et al.  Applications of Machine Learning in Cardiology , 2022, Cardiology and Therapy.

[7]  Rohan Kumar Yadav,et al.  Artificial intelligence in cardiology: The past, present and future , 2022, Indian heart journal.

[8]  F. Giallauria,et al.  Predictors of sacubitril/valsartan high dose tolerability in a real world population with HFrEF , 2022, ESC heart failure.

[9]  Spiros C. Denaxas,et al.  Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease , 2022, European heart journal.

[10]  A. Fiorentino,et al.  A Multistep Approach to Deal With Advanced Heart Failure: A Case Report on the Positive Effect of Cardiac Contractility Modulation Therapy on Pulmonary Pressure Measured by CardioMEMS , 2022, Frontiers in Cardiovascular Medicine.

[11]  Md. Martuza Ahamad,et al.  Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries , 2022, Frontiers in Cardiovascular Medicine.

[12]  Z. Krajcer.  Artificial Intelligence in Cardiovascular Medicine: Historical Overview, Current Status, and Future Directions. , 2022, Texas Heart Institute journal.

[13]  Qi-qi Ke,et al.  Mobilizing artificial intelligence to cardiac telerehabilitation. , 2022, Reviews in cardiovascular medicine.

[14]  G. Galasso,et al.  Post-COVID-19 Syndrome: Involvement and Interactions between Respiratory, Cardiovascular and Nervous Systems , 2022, Journal of clinical medicine.

[15]  G. de Simone,et al.  Carotid Atherosclerosis Predicts Blood Pressure Control in Patients With Hypertension: The Campania Salute Network Registry , 2022, Journal of the American Heart Association.

[16]  Sae Won Choi,et al.  Estimation of low-density lipoprotein cholesterol levels using machine learning. , 2022, International journal of cardiology.

[17]  Mildred K. Cho,et al.  Rising to the challenge of bias in health care AI , 2021, Nature Medicine.

[18]  G. Bottà,et al.  New prevention scenarios: polygenic risk , 2021, European heart journal supplements : journal of the European Society of Cardiology.

[19]  Hong Wang,et al.  Application of Artificial Intelligence in Acute Coronary Syndrome: A Brief Literature Review , 2021, Advances in Therapy.

[20]  I. V. Van Gelder,et al.  2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. , 2021, European heart journal.

[21]  Shih-Lun Chen,et al.  A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia , 2021, Sensors.

[22]  Yaru Yue,et al.  Automatic Detection of Short-Term Atrial Fibrillation Segments Based on Frequency Slice Wavelet Transform and Machine Learning Techniques , 2021, Sensors.

[23]  D. McManus,et al.  Atrial Fibrillation Prediction from Critically Ill Sepsis Patients , 2021, Biosensors.

[24]  G. Galasso,et al.  Cardiovascular Involvement in COVID-19: What Sequelae Should We Expect? , 2021, Cardiology and Therapy.

[25]  Ahmed Abba Haruna,et al.  Machine Learning Predictive Models for Coronary Artery Disease , 2021, SN Computer Science.

[26]  D. Zahger,et al.  Predicting 30-day mortality after ST elevation myocardial infarction: Machine learning- based random forest and its external validation using two independent nationwide datasets. , 2021, Journal of cardiology.

[27]  Syed Waseem Abbas Sherazi,et al.  A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome , 2021, PloS one.

[28]  D. McManus,et al.  Feasibility of atrial fibrillation detection from a novel wearable armband device , 2021, Cardiovascular digital health journal.

[29]  M. Clausel,et al.  An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection , 2021, Frontiers in Physiology.

[30]  J. Hartikainen,et al.  Wrist Band Photoplethysmography Autocorrelation Analysis Enables Detection of Atrial Fibrillation Without Pulse Detection , 2021, Frontiers in Physiology.

[31]  Thomas M. Deserno,et al.  Automatic Detection of Atrial Fibrillation in ECG Using Co-Occurrence Patterns of Dynamic Symbol Assignment and Machine Learning , 2021, Sensors.

[32]  Stéphane Delliaux,et al.  A filter approach for feature selection in classification: application to automatic atrial fibrillation detection in electrocardiogram recordings , 2021, BMC Medical Informatics and Decision Making.

[33]  A. Albadvi,et al.  Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming , 2021, Journal of healthcare engineering.

[34]  Timothy D. Imler,et al.  Development, validation, and proof-of-concept implementation of a two-year risk prediction model for undiagnosed atrial fibrillation using common electronic health data (UNAFIED) , 2021, BMC Medical Informatics and Decision Making.

[35]  Martin Glavin,et al.  Prediction of paroxysmal atrial fibrillation using new heart rate variability features , 2021, Comput. Biol. Medicine.

[36]  P. Noseworthy,et al.  Smart Wearables for Cardiac Monitoring—Real-World Use beyond Atrial Fibrillation , 2021, Sensors.

[37]  N. Ayache,et al.  Applications of artificial intelligence in cardiovascular imaging , 2021, Nature Reviews Cardiology.

[38]  Sheng Wang,et al.  Atrial fibrillation detection based on multi-feature extraction and convolutional neural network for processing ECG signals , 2021, Comput. Methods Programs Biomed..

[39]  Kipp W. Johnson,et al.  Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation–Related Stroke , 2021, Circulation.

[40]  M. Bourbon,et al.  Machine learning modelling of blood lipid biomarkers in familial hypercholesterolaemia versus polygenic/environmental dyslipidaemia , 2021, Scientific Reports.

[41]  F. D’Ascenzo,et al.  Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets , 2021, The Lancet.

[42]  G. De Luca,et al.  Cardiovascular risk factors and mortality in hospitalized patients with COVID-19: systematic review and meta-analysis of 45 studies and 18,300 patients , 2021, BMC Cardiovascular Disorders.

[43]  Harman S. Suri,et al.  Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence. , 2020, Reviews in cardiovascular medicine.

[44]  M. Ciccarelli,et al.  Artificial Intelligence as a Business Partner in Cardiovascular Precision Medicine: an Emerging Approach for Disease Detection and Treatment Optimization. , 2020, Current medicinal chemistry.

[45]  Jinliang Wang,et al.  Treatment effect prediction with adversarial deep learning using electronic health records , 2020, BMC Medical Informatics and Decision Making.

[46]  Young-Hoon Cho,et al.  Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram. , 2020, International journal of cardiology.

[47]  D. Dey,et al.  Machine learning integration of circulating and imaging biomarkers for explainable patient-specific prediction of cardiac events: A prospective study. , 2020, Atherosclerosis.

[48]  T. Uejima,et al.  Prediction of current and new development of atrial fibrillation on electrocardiogram with sinus rhythm in patients without structural heart disease. , 2020, International journal of cardiology.

[49]  Michael D Abràmoff,et al.  Identifying Ethical Considerations for Machine Learning Healthcare Applications , 2020, The American journal of bioethics : AJOB.

[50]  G. Pontone,et al.  Role of multimodality imaging in evaluation of cardiovascular involvement in COVID-19 , 2020, Trends in Cardiovascular Medicine.

[51]  A. Sposito,et al.  Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes. , 2020, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[52]  Durgadevi Velusamy,et al.  Ensemble of heterogeneous classifiers for diagnosis and prediction of coronary artery disease with reduced feature subset , 2020, Comput. Methods Programs Biomed..

[53]  H. Zeng,et al.  Machine learning-aided risk stratification system for the prediction of coronary artery disease. , 2020, International journal of cardiology.

[54]  Markus Kollmann,et al.  Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions , 2020, Sensors.

[55]  Jing Li,et al.  Develop and Evaluate a New and Effective Approach for Predicting Dyslipidemia in Steel Workers , 2020, Frontiers in Bioengineering and Biotechnology.

[56]  Jeroen J. Bax,et al.  2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association of Cardio-Thoracic Surgery (EACTS). , 2020, European heart journal.

[57]  Amy L. Seybert,et al.  Efficacy assessment of ticagrelor versus clopidogrel in Chinese patients with acute coronary syndrome undergoing percutaneous coronary intervention by data mining and machine‐learning decision tree approaches , 2020, Journal of clinical pharmacy and therapeutics.

[58]  M. Ciccarelli,et al.  It is easy to see, but it is better to foresee: a case report on the favourable alliance between CardioMEMS and levosimendan. , 2020, European heart journal. Case reports.

[59]  Avishek Choudhury,et al.  Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians , 2019, Journal of medical Internet research.

[60]  Laura Burattini,et al.  Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices , 2020, Sensors.

[61]  Rik Vullings,et al.  Detecting Atrial Fibrillation and Atrial Flutter in Daily Life Using Photoplethysmography Data , 2020, IEEE Journal of Biomedical and Health Informatics.

[62]  Jun Cai,et al.  An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records , 2020, JMIR medical informatics.

[63]  D. Dong,et al.  The Role of Imaging in the Detection and Management of COVID-19: A Review , 2020, IEEE Reviews in Biomedical Engineering.

[64]  M. Kozáková,et al.  Imaging subclinical atherosclerosis in cardiovascular risk stratification. , 2020, European journal of preventive cardiology.

[65]  Bor-Jiunn Hwang,et al.  Detection of Atrial Fibrillation Using 1D Convolutional Neural Network , 2020, Sensors.

[66]  Soonil Kwon,et al.  Detection of Atrial Fibrillation Using a Ring-Type Wearable Device (CardioTracker) and Deep Learning Analysis of Photoplethysmography Signals: Prospective Observational Proof-of-Concept Study , 2020, Journal of medical Internet research.

[67]  Anita Deswal,et al.  Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization , 2020, Circulation. Heart failure.

[68]  L. Tarassenko,et al.  Identification of patients with atrial fibrillation: a big data exploratory analysis of the UK Biobank , 2020, Physiological measurement.

[69]  George Lewith,et al.  Machine learning detection of Atrial Fibrillation using wearable technology , 2020, PloS one.

[70]  Minggang Shao,et al.  A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection , 2020, Sensors.

[71]  Mei-Ling Huang,et al.  Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network , 2020, Biomedical engineering letters.

[72]  Xiao Hu,et al.  Photoplethysmography based atrial fibrillation detection: a review , 2020, npj Digital Medicine.

[73]  K. Kario,et al.  Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques , 2019, Journal of clinical hypertension.

[74]  R. Suzuki,et al.  Potential of machine learning methods to identify patients with nonvalvular atrial fibrillation. , 2019, Future cardiology.

[75]  Chengjin Qin,et al.  An incremental learning system for atrial fibrillation detection based on transfer learning and active learning , 2019, Comput. Methods Programs Biomed..

[76]  M. Olsen,et al.  Circulating biomarkers for long-term cardiovascular risk stratification in apparently healthy individuals from the MONICA 10 cohort , 2019, European journal of preventive cardiology.

[77]  Sonia Allan,et al.  A governance model for the application of AI in health care , 2019, J. Am. Medical Informatics Assoc..

[78]  D. Clifton,et al.  Predicting atrial fibrillation in primary care using machine learning , 2019, PloS one.

[79]  L. Trinquart,et al.  Development and Validation of a Prediction Model for Atrial Fibrillation Using Electronic Health Records. , 2019, JACC. Clinical electrophysiology.

[80]  Jesús Sampedro-Gómez,et al.  Applications of artificial intelligence in cardiology. The future is already here. , 2019, Revista espanola de cardiologia.

[81]  J. Magnani,et al.  New technologies, new disparities: The intersection of electronic health and digital health literacy. , 2019, International journal of cardiology.

[82]  Rickey E Carter,et al.  An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction , 2019, The Lancet.

[83]  S. Farzanefar,et al.  Improved diagnostic accuracy for myocardial perfusion imaging using artificial neural networks on different input variables including clinical and quantification data , 2019, Revista Española de Medicina Nuclear e Imagen Molecular (English Edition).

[84]  A. Khera,et al.  2019 ACC/AHA Guideline on the Primary Prevention of Cardiovascular Disease: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. , 2019, Journal of the American College of Cardiology.

[85]  S. Iliceto,et al.  Comparison of Machine Learning Techniques for Prediction of Hospitalization in Heart Failure Patients , 2019, Journal of clinical medicine.

[86]  Yang Yan,et al.  The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine? , 2019, Journal of geriatric cardiology : JGC.

[87]  Hyun-Jai Cho,et al.  Artificial intelligence algorithm for predicting mortality of patients with acute heart failure , 2019, PloS one.

[88]  Zuo-guang Wang,et al.  A Prediction Model of Essential Hypertension Based on Genetic and Environmental Risk Factors in Northern Han Chinese , 2019, International journal of medical sciences.

[89]  Alexander Valys,et al.  Smartwatch Performance for the Detection and Quantification of Atrial Fibrillation. , 2019, Circulation. Arrhythmia and electrophysiology.

[90]  Chad J Zack,et al.  Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention. , 2019, JACC. Cardiovascular interventions.

[91]  J. H. Rudd,et al.  Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants , 2019, PloS one.

[92]  Manoel Horta Ribeiro,et al.  Automatic diagnosis of the 12-lead ECG using a deep neural network , 2019, Nature Communications.

[93]  M. Henein,et al.  Cardiac calcification as a marker of subclinical atherosclerosis and predictor of cardiovascular events: A review of the evidence , 2019, European journal of preventive cardiology.

[94]  G. Galasso,et al.  Big Health Data and Cardiovascular Diseases: A Challenge for Research, an Opportunity for Clinical Care , 2019, Front. Med..

[95]  Pashupati P. Mishra,et al.  Extensive phenotype data and machine learning in prediction of mortality in acute coronary syndrome – the MADDEC study , 2019, Annals of medicine.

[96]  A. Ng,et al.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.

[97]  Ioannis A. Kakadiaris,et al.  Machine Learning Outperforms ACC/AHA CVD Risk Calculator in MESA , 2018, Journal of the American Heart Association.

[98]  E. Vayena,et al.  Machine learning in medicine: Addressing ethical challenges , 2018, PLoS medicine.

[99]  D. Dey,et al.  Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. , 2018, JACC. Cardiovascular imaging.

[100]  Piotr J. Slomka,et al.  Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study , 2018, The Journal of Nuclear Medicine.

[101]  Minggang Shao,et al.  Detection of atrial fibrillation from ECG recordings using decision tree ensemble with multi-level features , 2018, Physiological measurement.

[102]  Hsien W. Herbert Chan,et al.  Machine learning: applications of artificial intelligence to imaging and diagnosis , 2018, Biophysical Reviews.

[103]  Henggui Zhang,et al.  Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks , 2018, Front. Physiol..

[104]  Jonathan Rubin,et al.  Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation , 2018, Physiological measurement.

[105]  S. C. Olhede,et al.  The growing ubiquity of algorithms in society: implications, impacts and innovations , 2018, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[106]  Amir Lerman,et al.  Voice Signal Characteristics Are Independently Associated With Coronary Artery Disease , 2018, Mayo Clinic proceedings.

[107]  Asghar Tabatabaei Balaei,et al.  A low-complexity algorithm for detection of atrial fibrillation using an ECG , 2018, Physiological measurement.

[108]  Kipp W. Johnson,et al.  Artificial Intelligence in Cardiology. , 2018, Journal of the American College of Cardiology.

[109]  M. Illario,et al.  Difficult-to-control hypertension: identification of clinical predictors and use of ICT-based integrated care to facilitate blood pressure control , 2018, Journal of Human Hypertension.

[110]  Kira Radinsky,et al.  Machine learning of big data in gaining insight into successful treatment of hypertension , 2018, Pharmacology research & perspectives.

[111]  Becky McCall,et al.  What does the GDPR mean for the medical community? , 2018, The Lancet.

[112]  A. Mokdad,et al.  Selective screening for atrial fibrillation using multivariable risk models , 2018, Heart.

[113]  N. Shah,et al.  Implementing Machine Learning in Health Care - Addressing Ethical Challenges. , 2018, The New England journal of medicine.

[114]  J. Michaelson,et al.  Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study , 2018, PloS one.

[115]  Steven Shea,et al.  Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis , 2017, Circulation research.

[116]  P. Bundhun,et al.  Application of the SYNTAX score in interventional cardiology , 2017, Medicine.

[117]  Y. Kokubo,et al.  Development of a Basic Risk Score for Incident Atrial Fibrillation in a Japanese General Population　- The Suita Study. , 2017, Circulation journal : official journal of the Japanese Circulation Society.

[118]  Yi Zhang,et al.  A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices: Results From the MultiSENSE Study. , 2017, JACC. Heart failure.

[119]  M. Ciccarelli,et al.  Larger Blood Pressure Reduction by Fixed-Dose Compared to Free Dose Combination Therapy of ACE Inhibitor and Calcium Antagonist in Hypertensive Patients , 2017, Translational medicine @ UniSa.

[120]  Steffen E. Petersen,et al.  2021 ESC Guidelines on cardiovascular disease prevention in clinical practice. , 2016, European journal of preventive cardiology.

[121]  Richard A. Kronmal,et al.  Risk score overestimation: the impact of individual cardiovascular risk factors and preventive therapies on the performance of the American Heart Association-American College of Cardiology-Atherosclerotic Cardiovascular Disease risk score in a modern multi-ethnic cohort , 2016, European heart journal.

[122]  R. Luben,et al.  Performance of the CHARGE-AF risk model for incident atrial fibrillation in the EPIC Norfolk cohort , 2015, European journal of preventive cardiology.

[123]  P. Fergus,et al.  Predicting the likelihood of heart failure with a multi level risk assessment using decision tree , 2015, 2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE).

[124]  Michael A. Burke,et al.  Phenomapping for Novel Classification of Heart Failure With Preserved Ejection Fraction , 2015, Circulation.

[125]  O. Franco,et al.  Comparison of application of the ACC/AHA guidelines, Adult Treatment Panel III guidelines, and European Society of Cardiology guidelines for cardiovascular disease prevention in a European cohort. , 2014, JAMA.

[126]  Mary Cushman,et al.  Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. , 2014, JAMA.

[127]  L. Lund,et al.  Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51 043 patients from the Swedish Heart Failure Registry , 2014, European journal of heart failure.

[128]  David D. McManus,et al.  Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE‐AF Consortium , 2013, Journal of the American Heart Association.

[129]  Damini Dey,et al.  Improved Accuracy of Myocardial Perfusion SPECT for the Detection of Coronary Artery Disease Using a Support Vector Machine Algorithm , 2013, The Journal of Nuclear Medicine.

[130]  Niranjan Chakravarthy,et al.  Design and performance of a multisensor heart failure monitoring algorithm: results from the multisensor monitoring in congestive heart failure (MUSIC) study. , 2012, Journal of cardiac failure.

[131]  G. de Simone,et al.  Classes of antihypertensive medications and blood pressure control in relation to metabolic risk factors , 2012, Journal of hypertension.

[132]  A. Çengel,et al.  An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT , 2010, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[133]  Robby Nieuwlaat,et al.  Progression from paroxysmal to persistent atrial fibrillation clinical correlates and prognosis. , 2010, Journal of the American College of Cardiology.

[134]  D. Levy,et al.  Development of a risk score for atrial fibrillation (Framingham Heart Study): a community-based cohort study , 2009, The Lancet.

[135]  C. Lau,et al.  Intrathoracic Impedance Monitoring in Patients With Heart Failure: Correlation With Fluid Status and Feasibility of Early Warning Preceding Hospitalization , 2005, Circulation.

[136]  W. Kannel,et al.  Some lessons in cardiovascular epidemiology from Framingham. , 1976, The American journal of cardiology.

[137]  T. Taniguchi,et al.  Explainable Artificial Intelligence Model for Diagnosis of Atrial Fibrillation Using Holter Electrocardiogram Waveforms. , 2021, International heart journal.

[138]  Shigeki Muto,et al.  Simple risk model and score for predicting of incident atrial fibrillation in Japanese. , 2019, Journal of cardiology.

[139]  白石 泰之.  Validation of the Get With The Guideline-Heart Failure risk score in Japanese patients and the potential improvement of its discrimination ability by the inclusion of B-type natriuretic peptide level(審査報告) , 2016 .

[140]  K. Fukuda,et al.  Validation of the Get With The Guideline-Heart Failure risk score in Japanese patients and the potential improvement of its discrimination ability by the inclusion of B-type natriuretic peptide level. , 2016, American heart journal.

[141]  L. Di Biase,et al.  Validation of the Framingham Heart Study and CHARGE-AF Risk Scores for Atrial Fibrillation in Hispanics, African-Americans, and Non-Hispanic Whites. , 2016, The American journal of cardiology.

[142]  Elsayed Z Soliman,et al.  A clinical risk score for atrial fibrillation in a biracial prospective cohort (from the Atherosclerosis Risk in Communities [ARIC] study). , 2011, The American journal of cardiology.

[143]  Peter Herbison,et al.  Global Registry of Acute Coronary Events (GRACE) hospital discharge risk score accurately predicts long-term mortality post acute coronary syndrome. , 2007, American heart journal.