SynthesisCurrent hypertension reports2022
Machine Learning for Hypertension Prediction: a Systematic Review.
Synthesis in Current hypertension reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
102 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Smoking cessation in pregnancy: a systematic review and meta-analysis of vulnerability approaches and expectant father involvement.Public health reviews · 2026Pooled it
- Artificial intelligence in ophthalmology: a bibliometric analysis of the 5-year trends in literature.Frontiers in medicine · 2025Pooled it
- Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review.European journal of hospital pharmacy : science and practice · 2024Pooled it
- Data-driven prognostic factors analysis and personalized follow-up strategies for post-progression survival in locally advanced esophageal squamous cell carcinoma after definitive chemoradiotherapy.Annals of medicine · 2026Article
- Evaluation of a National Health Service Machine-Learning Model for Hypertension Case-Finding: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- Age, Sex, and Waist-to-Height Ratio Approach the Discrimination of Fifty-Four Model Inputs for Prevalent Hypertension: An Explainable Machine Learning Analysis of the Chilean National Health Survey.Diagnostics (Basel, Switzerland) · 2026Article
- Machine Learning Approaches to Identify Influential Factors of the Comorbid Psychiatric Symptoms and Hypertension Among Rural Adults in Bangladesh: A Cross-Sectional Study.Health science reports · 2026Article
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- An interpretable combinatorial data-mining framework for predicting new-onset hypertension in the general population.Hypertension research : official journal of the Japanese Society of Hypertension · 2026Article
- Interpretable machine learning model based on non-contrast computed tomography within 24 hours of admission for early prediction of the risk of severe deterioration of acute pancreatitis.Quantitative imaging in medicine and surgery · 2026Article
- Single-cell and spatial transcriptomics identify CAPG as a key driver of cisplatin resistance in bladder cancer.Functional & integrative genomics · 2026Article
- Artificial Intelligence and Healthcare Policy: A Bibliometric Analysis of Global Research Trends.Healthcare (Basel, Switzerland) · 2026Article
- Article
- Development and evaluation of an explainable machine learning selection pipeline for predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.Journal of translational medicine · 2026Article
- A machine learning-based framework for predicting hypertension using serum hematological factors.Scientific reports · 2026Article
- Precision biomarker discovery in hypertension through explainable AI and proteomics.Journal of human hypertension · 2026Article
- Development and internal validation of a prognostic model for esophageal cancer liver metastases using the Surveillance, Epidemiology, and End Results database.Journal of thoracic disease · 2026Article
- Machine learning approaches in identifying factors associated with hypertension and undiagnosed hypertension in adults in rural areas of Bangladesh.Archives of public health = Archives belges de sante publique · 2026Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewTo provide an overview of the literature regarding the use of machine learning algorithms to predict hypertension. A systematic review was performed to select recent articles on the subject. RECENT
findingsThe screening of the articles was conducted using a machine learning algorithm (ASReview). A total of 21 articles published between January 2018 and May 2021 were identified and compared according to variable selection, train-test split, data balancing, outcome definition, final algorithm, and performance metrics. Overall, the articles achieved an area under the ROC curve (AUROC) between 0.766 and 1.00. The algorithms most frequently identified as having the best performance were support vector machines (SVM), extreme gradient boosting (XGBoost), and random forest. Machine learning algorithms are a promising tool to improve preventive clinical decisions and targeted public health policies for hypertension. However, technical factors such as outcome definition, availability of the final code, predictive performance, explainability, and data leakage need to be consistently and critically evaluated.
Indexed as
Identifiers
35731335What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.