ArticleScientific reports2020
Deep Learning for Improved Risk Prediction in Surgical Outcomes.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- PORT: Generative EHR Pretraining for Pediatric Intraoperative Risk Prediction.Research square · 2026Article
- Which Method Best Predicts Postoperative Complications: Deep Learning, Machine Learning, or Conventional Logistic Regression?Annals of gastroenterological surgery · 2026Review
- Representation learning for the prediction of length of stay in infants with congenital heart diseases undergoing cardiac surgery.European heart journal. Digital health · 2026Article
- Comparative analysis of perceptions on artificial intelligence in surgery: a survey study among surgeons and medical students in Ireland.Irish journal of medical science · 2025Article
- Prediction of Postoperative Mortality After Fontan Procedure: A Clinical Prediction Model Study Using Deep Learning Artificial Intelligence Techniques.Journal of cardiovascular development and disease · 2025Article
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.Scientific reports · 2025Article
- Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.Frontiers in public health · 2025Article
- Comparative clinical outcomes and mortality risk in coronary artery bypass grafting, valve surgeries, and percutaneous interventions.American journal of cardiovascular disease · 2025Article
- The Efficacy of Artificial Intelligence in Predicting the Postoperative Mortality Rate in Patients with Congenital Heart Disease: A Systematic Review and Meta-Analysis.Research in heart yield and translational medicine · 2025Review
- Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery.Diagnostics (Basel, Switzerland) · 2024Article
- Maximizing Survival in Pediatric Congenital Cardiac Surgery Using Machine Learning, Explainability, and Simulation Techniques.Journal of clinical medicine · 2024Article
- Computational simulation of the potential improvement in clinical outcomes of cardiovascular diseases with the use of a personalized predictive medicine approach.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Artificial intelligence in surgery.Nature medicine · 2024Review
- Using machine learning to predict five-year transplant-free survival among infants with hypoplastic left heart syndrome.Scientific reports · 2024Article
- Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Uncovering the Genetic Basis of Congenital Heart Disease: Recent Advancements and Implications for Clinical Management.CJC pediatric and congenital heart disease · 2023Review
- Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes.Journal of clinical medicine · 2023Review
- Myopia prediction for children and adolescents via time-aware deep learning.Scientific reports · 2023Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this complex procedure carries a high mortality rate. In this study we address the need to provide an accurate patient specific risk prediction for one-year postoperative mortality or cardiac transplantation and prolonged length of hospital stay with the purpose of assisting clinicians and patients' families in the preoperative decision making process. Currently available risk prediction models either do not provide patient specific risk factors or only predict in-hospital mortality rates. We apply machine learning models to predict and calculate individual patient risk for mortality and prolonged length of stay using the Pediatric Heart Network Single Ventricle Reconstruction trial dataset. We applied a Markov Chain Monte-Carlo simulation method to impute missing data and then fed the selected variables to multiple machine learning models. The individual risk of mortality or cardiac transplantation calculation produced by our deep neural network model demonstrated 89 ± 4% accuracy and 0.95 ± 0.02 area under the receiver operating characteristic curve (AUROC). The C-statistics results for prediction of prolonged length of stay were 85 ± 3% accuracy and AUROC 0.94 ± 0.04. These predictive models and calculator may help to inform clinical and organizational decision making.
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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.