ArticlePloS one2019
A machine learning approach for the prediction of pulmonary hypertension.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 3 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in pulmonary hypertension: a systematic review.European journal of medical research · 2025Pooled it
- Comparative accuracy of artificial intelligence versus manual interpretation in detecting pulmonary hypertension across chest imaging modalities: a diagnostic test accuracy meta-analysis.Frontiers in artificial intelligence · 2025Pooled it
- Assessing the precision of machine learning for diagnosing pulmonary arterial hypertension: a systematic review and meta-analysis of diagnostic accuracy studies.Frontiers in cardiovascular medicine · 2024Pooled it
- Hypertension Detection Using Explainable Stacked Ensemble Machine Learning From Clinical and Physiological Data: A Comprehensive Study.Health science reports · 2026Article
- Automated detection of neonatal pulmonary hypertension in echocardiograms with a deep learning model.Pediatric research · 2026Article
- Machine learning for prediction of key haemodynamic parameters in pulmonary arterial hypertension.European heart journal. Digital health · 2026Article
- Beyond the abdomen: an interpretable machine learning model for predicting postoperative ileus in non-abdominal surgery.Frontiers in physiology · 2026Article
- Artificial intelligence-based echocardiography assessment to detect pulmonary hypertension.ERJ open research · 2025Article
- A machine learning approach to predict hypertension using cross-sectional & two years follow up data from a health & demographic cohort of Assam, North East India.The Indian journal of medical research · 2025Article
- An ensemble learning model for detection of pulmonary hypertension using electrocardiogram, chest X-ray, and brain natriuretic peptide.European heart journal. Digital health · 2025Article
- Predicting hypertension and identifying most important factors among married women in Bangladesh using machine learning approach.PloS one · 2025Article
- Prediction of primary Hypertension in Primary Health Care Settings in Coastal Karnataka Using Artificial Neural Network.Current hypertension reviews · 2025Article
- Development and validation of a mortality predictive model for ICU patients with primary pulmonary hypertension.Scientific reports · 2024Article
- Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography.Journal of advanced research · 2024Article
- An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension.The European respiratory journal · 2024Article
- Resistin predicts disease severity and survival in patients with pulmonary arterial hypertension.Respiratory research · 2024Article
- Artificial Intelligence in Cutaneous Leishmaniasis Diagnosis: Current Developments and Future Perspectives.Diagnostics (Basel, Switzerland) · 2024Review
- The relationship between anthropometric indices and the presence of hypertension in an Iranian population sample using data mining algorithms.Journal of human hypertension · 2024Article
- Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms.International journal of computer vision · 2024Article
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Authors and funding
7 authors.
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Abstract
backgroundMachine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters.
methodsIn our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme.
resultsML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73-0.93), boosted classification trees (AUC 0.80; 95% CI 0.68-0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67-0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75-0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78-0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance.
conclusionsUsing ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.
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