ArticleCardiovascular revascularization medicine : including molecular interventions2021
Machine-Learning-Based In-Hospital Mortality Prediction for Transcatheter Mitral Valve Repair in the United States.
Article in Cardiovascular revascularization medicine : including molecular interventions, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Personalized Diagnoses for Those Born with Congenitally Malformed Hearts.Journal of personalized medicine · 2025Review
- Artificial Intelligence in Cardiovascular Imaging and Interventional Cardiology: Emerging Trends and Clinical Implications.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Article
- Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.BMC public health · 2024Article
- Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.BMC infectious diseases · 2024Observational
- Predicting intubation risk among COVID-19 hospitalized patients using artificial neural networks.Journal of education and health promotion · 2023Article
- Comparing artificial neural network training algorithms to predict length of stay in hospitalized patients with COVID-19.BMC infectious diseases · 2022Article
- Comparing machine learning algorithms for predicting COVID-19 mortality.BMC medical informatics and decision making · 2022Article
- Predicting the Need for Intubation among COVID-19 Patients Using Machine Learning Algorithms: A Single-Center Study.Medical journal of the Islamic Republic of Iran · 2022Article
- Design of an artificial neural network to predict mortality among COVID-19 patients.Informatics in medicine unlocked · 2022Article
- [Artificial intelligence in cardiology : Relevance, current applications, and future developments].Herzschrittmachertherapie & Elektrophysiologie · 2021Review
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Authors and funding
19 authors.
Funding
Abstract
backgroundTranscatheter mitral valve repair (TMVR) utilization has increased significantly in the United States over the last years. Yet, a risk-prediction tool for adverse events has not been developed. We aimed to generate a machine-learning-based algorithm to predict in-hospital mortality after TMVR.
methodsPatients who underwent TMVR from 2012 through 2015 were identified using the National Inpatient Sample database. The study population was randomly divided into a training set (n = 636) and a testing set (n = 213). Prediction models for in-hospital mortality were obtained using five supervised machine-learning classifiers.
resultsA total of 849 TMVRs were analyzed in our study. The overall in-hospital mortality was 3.1%. A naïve Bayes (NB) model had the best discrimination for fifteen variables, with an area under the receiver-operating curve (AUC) of 0.83 (95% CI, 0.80-0.87), compared to 0.77 for logistic regression (95% CI, 0.58-0.95), 0.73 for an artificial neural network (95% CI, 0.55-0.91), and 0.67 for both a random forest and a support-vector machine (95% CI, 0.47-0.87). History of coronary artery disease, of chronic kidney disease, and smoking were the three most significant predictors of in-hospital mortality.
conclusionsWe developed a robust machine-learning-derived model to predict in-hospital mortality in patients undergoing TMVR. This model is promising for decision-making and deserves further clinical validation.
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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.