ArticleScientific reports2021
Machine learning prediction models for prognosis of critically ill patients after open-heart surgery.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Big Data in cardiac surgery: real world and perspectives.Journal of cardiothoracic surgery · 2022Pooled it
- Development and validation of a prediction model for in-hospital mortality of patients with severe thrombocytopenia.Scientific reports · 2022Trial
- Application of supervised machine learning algorithms to predict in-hospital death risk in patients receiving intra-aortic balloon pump therapy during the perioperative period of cardiac surgery.Journal of cardiothoracic surgery · 2026Article
- Machine learning time-to-event algorithms for predicting the duration of ventilation after cardiac surgery.JTCVS open · 2026Article
- Development and validation of a machine learning-based prediction model for thrombocytopenia following cardiopulmonary bypass.Scientific reports · 2026Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Application of artificial intelligence in predicting the results of open-heart surgery: a scoping review.BMC medical informatics and decision making · 2025Article
- Integrative Machine Learning and Bayesian Analysis Reveals Atrial Fibrillation as a Key Predictor of Severe COVID-19 Outcomes.Clinical and translational science · 2025Article
- Observational
- A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future.Clinics and practice · 2025Review
- Risk factor identification and prediction of pleural effusion following coronary artery bypass grafting.American journal of translational research · 2025Article
- Harnessing multi-output machine learning approach and dynamical observables from network structure to optimize COVID-19 intervention strategies.Biology methods & protocols · 2025Article
- Cardiac patients' surgery outcome and associated factors in Ethiopia: application of machine learning.BMC pediatrics · 2024Article
- Post-surgery survival and associated factors for cardiac patients in Ethiopia: applications of machine learning, semi-parametric and parametric modelling.BMC medical informatics and decision making · 2024Article
- Article
- Artificial Intelligence-enabled Decision Support in Surgery: State-of-the-art and Future Directions.Annals of surgery · 2023Article
- Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.Scientific reports · 2023Article
- Machine learning-based prediction of 1-year mortality in hypertensive patients undergoing coronary revascularization surgery.Clinical cardiology · 2023Article
- Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to build up multiple machine learning models to predict 30-days mortality, and 3 complications including septic shock, thrombocytopenia, and liver dysfunction after open-heart surgery. Patients who underwent coronary artery bypass surgery, aortic valve replacement, or other heart-related surgeries between 2001 and 2012 were extracted from MIMIC-III databases. Extreme gradient boosting, random forest, artificial neural network, and logistic regression were employed to build models by utilizing fivefold cross-validation and grid search. Receiver operating characteristic curve, area under curve (AUC), decision curve analysis, test accuracy, F1 score, precision, and recall were applied to access the performance. Among 6844 patients enrolled in this study, 215 patients (3.1%) died within 30 days after surgery, part of patients appeared liver dysfunction (248; 3.6%), septic shock (32; 0.5%), and thrombocytopenia (202; 2.9%). XGBoost, selected to be our final model, achieved the best performance with highest AUC and F1 score. AUC and F1 score of XGBoost for 4 outcomes: 0.88 and 0.58 for 30-days mortality, 0.98 and 0.70 for septic shock, 0.88 and 0.55 for thrombocytopenia, 0.89 and 0.40 for liver dysfunction. We developed a promising model, presented as software, to realize monitoring for patients in ICU and to improve prognosis.
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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.