ArticleScientific reports2023
Predicting unplanned readmission due to cardiovascular disease in hospitalized patients with cancer: a machine learning approach.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 13 citations in OpenAlex.
- From data to decision: a clinical pipeline for wearable AI in early cardiotoxicity detection.Cardio-oncology (London, England) · 2026Review
- Leveraging environmental information for enhanced prediction of cardiac readmissions.iScience · 2025Article
- A Predictive Model of Cardiovascular Aging by Clinical and Immunological Markers Using Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- AI and Smart Devices in Cardio-Oncology: Advancements in Cardiotoxicity Prediction and Cardiovascular Monitoring.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.Current cardiology reports · 2025Review
- The role of continuous monitoring in acute-care settings for predicting all-cause 30-day hospital readmission: A pilot study.Heliyon · 2025Article
- Lipoproteins predicting coronary lesion complexity in premature coronary artery disease: a supervised machine learning approach.Frontiers in cardiovascular medicine · 2025Article
- Investigating the impact of extreme weather events and related indicators on cardiometabolic multimorbidity.Archives of public health = Archives belges de sante publique · 2024Article
- Predicting the Risk of Severity and Readmission in Patients with Heart Failure in Indonesia: A Machine Learning Approach.Healthcare informatics research · 2024Article
- Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review.Frontiers in cardiovascular medicine · 2023Review
- Machine Learning-Based Prediction of Unplanned Readmission Due to Major Adverse Cardiac Events Among Hospitalized Patients with Blood Cancers.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular disease (CVD) in cancer patients can affect the risk of unplanned readmissions, which have been reported to be costly and associated with worse mortality and prognosis. We aimed to demonstrate the feasibility of using machine learning techniques in predicting the risk of unplanned 180-day readmission attributable to CVD among hospitalized cancer patients using the 2017-2018 Nationwide Readmissions Database. We included hospitalized cancer patients, and the outcome was unplanned hospital readmission due to any CVD within 180 days after discharge. CVD included atrial fibrillation, coronary artery disease, heart failure, stroke, peripheral artery disease, cardiomegaly, and cardiomyopathy. Decision tree (DT), random forest, extreme gradient boost (XGBoost), and AdaBoost were implemented. Accuracy, precision, recall, F2 score, and receiver operating characteristic curve (AUC) were used to assess the model's performance. Among 358,629 hospitalized patients with cancer, 5.86% (n = 21,021) experienced unplanned readmission due to any CVD. The three ensemble algorithms outperformed the DT, with the XGBoost displaying the best performance. We found length of stay, age, and cancer surgery were important predictors of CVD-related unplanned hospitalization in cancer patients. Machine learning models can predict the risk of unplanned readmission due to CVD among hospitalized cancer patients.
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Registered trials
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