ArticleJournal of personalized medicine2020
Using Machine Learning to Predict 30-Day Hospital Readmissions in Patients with Atrial Fibrillation Undergoing Catheter Ablation.
Article in Journal of personalized medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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Who cites it
12 citing papers in PubMed, 25 citations in OpenAlex.
- Predicting 28-day all-cause unplanned hospital re-admission of patients with alcohol use disorders: a machine learning approach.Alcohol and alcoholism (Oxford, Oxfordshire) · 2025Article
- An Explainable AI Application (AF'fective) to Support Monitoring of Patients With Atrial Fibrillation After Catheter Ablation: Qualitative Focus Group, Design Session, and Interview Study.JMIR human factors · 2025Article
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
- The Use of Artificial Intelligence for Detecting and Predicting Atrial Arrhythmias Post Catheter Ablation.Reviews in cardiovascular medicine · 2023Review
- Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database.BMC medical informatics and decision making · 2022Article
- Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2022Article
- Prediction of Atrial Fibrillation in Hospitalized Elderly Patients With Coronary Heart Disease and Type 2 Diabetes Mellitus Using Machine Learning: A Multicenter Retrospective Study.Frontiers in public health · 2022Article
- Machine Learning-Predicted Progression to Permanent Atrial Fibrillation After Catheter Ablation.Frontiers in cardiovascular medicine · 2022Article
- How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management.Cardiovascular research · 2021Review
- Patient Flows, Patient Distribution Computations and Medicines Accounting in the Pharmacoeconomic Models Through Procurement Perspective.ClinicoEconomics and outcomes research : CEOR · 2021Article
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Corrections and comments
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Authors and funding
10 authors at 5 institutions in 1 country.
Funding
Abstract
Atrial fibrillation (AF) cases are expected to increase over the next several decades, due to the rise in the elderly population. One promising treatment option for AF is catheter ablation, which is increasing in use. We investigated the hospital readmissions data for AF patients undergoing catheter ablation, and used machine learning models to explore the risk factors behind these readmissions. We analyzed data from the 2013 Nationwide Readmissions Database on cases with AF, and determined the relative importance of factors in predicting 30-day readmissions for AF with catheter ablation. Various machine learning methods, such as
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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.