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Precision Health Database


Precision Health Database|Search|PHGKB

Heart, Lung, Blood and Sleep Disorders

Last data update: May 31, 2023. (Total: 60044 Documents since 2012)
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Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification.
Zeynep Ozpolat et al. Diagnostics (Basel, Switzerland) 2023 13(6)
Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts.
Yikuan Li et al. European heart journal. Digital health 2023 3(4) 535-547
Predictive value of CAC score combined with clinical features for obstructive coronary heart disease on coronary computed tomography angiography: a machine learning method.
Ren Yongkui et al. BMC cardiovascular disorders 2022 22(1) 569
Development and evaluation of a radiomics model of resting N-ammonia positron emission tomography myocardial perfusion imaging to predict coronary artery stenosis in patients with suspected coronary heart disease.
Zhang Xiaochun et al. Annals of translational medicine 2022 10(21) 1167
Recurrence risk prediction of acute coronary syndrome per patient as a personalized ACS recurrence risk: a retrospective study.
Kong Vungsovanreach et al. PeerJ 2022 10e14348
A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease.
Yan Jingjing et al. Computers in biology and medicine 2022 151(Pt A) 106300
Study on the risk of coronary heart disease in middle-aged and young people based on machine learning methods: a retrospective cohort study.
Cao Jiaoyu et al. PeerJ 2022 10e14078
The Impact of Time Horizon on Classification Accuracy: Application of Machine Learning to Prediction of Incident Coronary Heart Disease.
Simon Steven et al. JMIR cardio 2022 6(2) e38040
A Machine Learning Model to Predict Cardiovascular Events during Exercise Evaluation in Patients with Coronary Heart Disease.
Shen Tao et al. Journal of clinical medicine 2022 11(20)
Development and Validation of Prediction Models of Adverse Kidney Outcomes in the Population With and Without Diabetes Mellitus.
Grams Morgan E et al. Diabetes care 2022
Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders.
Qu Yimin et al. Journal of clinical medicine 2022 11(10)
Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study.
Wang Jinwan et al. JMIR medical informatics 2022 10(4) e33395
Nonlaboratory-based risk assessment model for coronary heart disease screening: Model development and validation.
Zhang Liying et al. International journal of medical informatics 2022 162104746
Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study.
Guo Haihong et al. BMC medical informatics and decision making 2022 22(1) 39
Deep Phenotyping and Prediction of Long-Term Cardiovascular Disease: Optimized by Machine Learning.
Zhuang Xiao-Dong et al. The Canadian journal of cardiology 2022
Predicting Coupled Herbs for the Treatment of Hypertension Complicated with Coronary Heart Disease in Real-World Data Based on a Complex Network and Machine Learning.
Huan Jia-Ming et al. Evidence-based complementary and alternative medicine : eCAM 2022 20228285111
Identifying Coronary Artery Lesions by Feature Analysis of Radial Pulse Wave: A Case-Control Study.
Zhang Chun-Ke et al. BioMed research international 2022 20215047501
ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure.
Akbilgic Oguz et al. European heart journal. Digital health 2022 2(4) 626-634
A Prediction Model of Incident Cardiovascular Disease in Patients with Sleep-Disordered Breathing.
Park Jong-Uk et al. Diagnostics (Basel, Switzerland) 2021 11(12)
Interpretable prediction of 3-year all-cause mortality in patients with heart failure caused by coronary heart disease based on machine learning and SHAP.
Wang Ke et al. Computers in biology and medicine 2021 137104813
Prediction of Coronary Calcification and Stenosis: Role of Radiomics From Low-Dose CT.
Homayounieh Fatemeh et al. Academic radiology 2021 28(7) 972-979
Electronic Medical Record Risk Modeling of Cardiovascular Outcomes Among Patients with Type 2 Diabetes.
Hong Dongzhe et al. Diabetes therapy : research, treatment and education of diabetes and related disorders 2021
Improving Medication Adherence Through Adaptive Digital Interventions (iMedA) in Patients With Hypertension: Protocol for an Interrupted Time Series Study.
Etminani Kobra et al. JMIR research protocols 2021 10(5) e24494
Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes.
Gevaert Andreas B et al. ESC heart failure 2021
Estimation of low-density lipoprotein cholesterol by machine learning methods.
Christina Tsigalou et al. Clinica chimica acta; international journal of clinical chemistry 2021
Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models.
Andy Anietie U et al. JMIR cardio 2021 Feb 5(1) e24473
Predicting Postoperative Length of Stay for Isolated Coronary Artery Bypass Graft Patients Using Machine Learning.
Alshakhs Fatima et al. International journal of general medicine 2020 13751-762
Accurate Prediction of Coronary Heart Disease for Patients With Hypertension From Electronic Health Records With Big Data and Machine-Learning Methods: Model Development and Performance Evaluation.
Du Zhenzhen et al. JMIR medical informatics 2020 Jul 8(7) e17257
Applying Artificial Intelligence to Predict Self-Reported Poor Health Among Black and Hispanic Caregivers with Mild Cognitive Impairment.
Yoon Sunmoo et al. Studies in health technology and informatics 2020 Jun 272433-436
Artificial Intelligence in Intracoronary Imaging.
Fedewa Russell et al. Current cardiology reports 2020 May 22(7) 46
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