Research graph
References from Predicting hypertension control using machine learning. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2021 · External reference
Fifty years of Framingham Study contributions to understanding hypertension
10.1038/sj.jhh.1000949 · 2000 · External reference
Unresolved reference
2017 · External reference
Unresolved reference
2017 · External reference
Incidence and risk factors of isolated systolic and diastolic hypertension: a 10 year follow-up of the Tehran Lipids and Glucose Study.
10.3109/08037051.2015.1116221 · 2016 · External reference
Prediction of hypertension based on the genetic analysis of longitudinal phenotypes: a comparison of different modeling approaches for the binary trait of hypertension
10.1186/1753-6561-8-s1-s78 · 2014 · External reference
A hypertension risk score for middle-aged and older adults
10.1111/j.1751-7176.2010.00343.x · 2010 · External reference
Incremental predictive value of adding past blood pressure measurements to the Framingham hypertension risk equation: the Whitehall II Study
10.1161/hypertensionaha.109.144220 · 2010 · External reference
Prediction of incident hypertension risk in women with currently normal blood pressure
10.1016/j.amjmed.2008.10.034 · 2009 · External reference
A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study
10.7326/0003-4819-148-2-200801150-00005 · 2008 · External reference
Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: A cross-sectional study of chronic diseases in central China.
10.1371/journal.pone.0233166 · 2020 · External reference
Prediction of hypertension using traditional regression and machine learning models: A systematic review and meta-analysis.
10.1371/journal.pone.0266334 · 2022 · External reference
Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project.
10.1371/journal.pone.0195344 · 2018 · External reference
Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest.
10.3390/app8081325 · 2018 · External reference
Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques
10.1111/jch.13759 · 2020 · External reference
Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning.
10.3389/fcvm.2021.778306 · 2021 · External reference
Effects of immediate versus delayed antihypertensive therapy on outcome in the Systolic Hypertension in Europe Trial
10.1097/00004872-200404000-00029 · 2004 · External reference
Benefits of early hypertension control on cardiovascular outcomes in patients with diabetes
10.2337/dc12-0284 · 2013 · External reference
The Study on COgnition and Prognosis in the Elderly (SCOPE)—major CV events and stroke in subgroups of patients.
10.1080/08037050510008823 · 2005 · External reference
Prevention of cardiovascular events with an antihypertensive regimen of amlodipine adding perindopril as required versus atenolol adding bendroflumethiazide as required, in the Anglo-Scandinavian Cardiac Outcomes Trial-Blood Pressure Lowering Arm (ASCOT-BPLA): a multicentre randomised controlled trial.
10.1016/s0140-6736(05)67185-1 · 2005 · External reference
Major outcomes in high-risk hypertensive patients randomized to angiotensin-converting enzyme inhibitor or calcium channel blocker vs diuretic: The Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).
10.1001/jama.288.23.2981 · 2002 · External reference
Outcomes in hypertensive patients at high cardiovascular risk treated with regimens based on valsartan or amlodipine: the VALUE randomised trial
10.1016/s0140-6736(04)16451-9 · 2004 · External reference
Clinical practice guidelines for the management of hypertension in the community a statement by the American Society of Hypertension and the International Society of Hypertension
10.1097/hjh.0000000000000065 · 2014 · External reference
Use of blood pressure lowering drugs in the prevention of cardiovascular disease: meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies
10.1136/bmj.b1665 · 2009 · External reference
Effects of blood pressure lowering on outcome incidence in hypertension: 4. Effects of various classes of antihypertensive drugs—overview and meta-analyses
10.1097/hjh.0000000000000447 · 2015 · External reference
A Randomized Trial of Intensive versus Standard Blood-Pressure Control
10.1056/nejmoa1511939 · 2015 · External reference
Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis
10.1016/s0140-6736(15)01225-8 · 2016 · External reference
Factors influencing the decline in stroke mortality: a statement from the American Heart Association/American Stroke Association
10.1161/01.str.0000437068.30550.cf · 2014 · External reference
Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke
10.1161/strokeaha.118.024293 · 2019 · External reference
Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis
10.1161/circresaha.117.311312 · 2017 · External reference
A data-driven approach to predicting diabetes and cardiovascular disease with machine learning
10.1186/s12911-019-0918-5 · 2019 · External reference
Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review
10.1016/j.jacc.2020.11.030 · 2021 · External reference
Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs
10.1016/s2589-7500(21)00043-1 · 2021 · External reference
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning.
10.1038/s41551-018-0195-0 · 2018 · External reference
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
10.1161/circulationaha.121.057480 · 2022 · External reference
Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review.
10.2196/32344 · 2022 · External reference
Using recurrent neural network models for early detection of heart failure onset
10.1093/jamia/ocw112 · 2017 · External reference
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.
2014 · External reference
Deep Visual-Semantic Alignments for Generating Image Descriptions
10.1109/tpami.2016.2598339 · 2017 · External reference
Machine Learning for Hypertension Prediction: a Systematic Review
10.1007/s11906-022-01212-6 · 2022 · External reference
Heterogeneity in Blood Pressure Response to 4 Antihypertensive Drugs: A Randomized Clinical Trial
10.1001/jama.2023.3322 · 2023 · External reference
