ArticleJournal of thoracic disease2020
A study of aortic dissection screening method based on multiple machine learning models.
Article in Journal of thoracic disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- An LLM-enhanced AI agent for rapid diagnosis of acute aortic dissection in multi-center settings.iScience · 2026Article
- Deep learning in the early diagnosis of acute aortic dissection.Frontiers in cardiovascular medicine · 2026Review
- Early auxiliary diagnosis model for chest pain triad based on artificial intelligence multimodal fusion.JAMIA open · 2025Article
- Evaluating the use of body mass index change as a proxy for anorexia nervosa recovery: a machine learning perspective.Journal of eating disorders · 2025Article
- Artificial intelligence-driven diagnosis of acute thoracic aortic dissection: integrating imaging, biomarkers, and clinical workflows-a narrative review.Annals of translational medicine · 2025Review
- Establishment of a nomogram model based on immune-related genes using machine learning for aortic dissection diagnosis and immunomodulation assessment.International journal of medical sciences · 2025Article
- Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network.International journal of cardiology. Heart & vasculature · 2024Article
- A generalizable and easy-to-use COVID-19 stratification model for the next pandemic via immune-phenotyping and machine learning.Frontiers in immunology · 2024Article
- Application of the Unbalanced Ensemble Algorithm for Prognostic Prediction Outcomes of All-Cause Mortality in Coronary Heart Disease Patients Comorbid with Hypertension.Risk management and healthcare policy · 2024Article
- Machine learning is an effective method to predict the 90-day prognosis of patients with transient ischemic attack and minor stroke.BMC medical research methodology · 2022Article
- Cluster-Based Ensemble Learning Model for Aortic Dissection Screening.International journal of environmental research and public health · 2022Article
- Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection.BMC medical informatics and decision making · 2022Article
- Current state and future directions of genomic medicine in aortic dissection: A path to prevention and personalized care.Seminars in vascular surgery · 2022Review
- Temporal Trends and Outcomes of Elective Thoracic Aortic Repair and Acute Aortic Syndromes in Bicuspid Aortic Valves: Insights from a National Database.Cardiology and therapy · 2021Article
- An early aortic dissection screening model and applied research based on ensemble learning.Annals of translational medicine · 2020Article
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe main purpose of the study was to develop an early screening method for aortic dissection (AD) based on machine learning. Due to the rarity of AD and the complexity of symptoms, many doctors have no clinical experience with it. Many patients are not suspected of having AD, which lead to a high rate of misdiagnosis. Here, we report the preliminary study and feasibility of rapid and accurate screening method of AD with machine learning methods.
methodsThe dataset analyzed was composed by examination data provided by the Xiangya Hospital Central South University of China which include a total of 60,000 samples, including aortic patients and non-aortic ones. Each sample has 76 features which is consist of routine examinations and other easily accessible information. Since the proportion of people who are affected is usually imbalanced compared to non-diseased people, multiple machine learning models were used, include AdaBoost, SmoteBagging, EasyEnsemble and CalibratedAdaMEC. They used different methods such as ensemble learning, undersampling, oversampling, and cost-sensitivity to solve data imbalance problems.
resultsAdaBoost performed poorly with an average recall of 16.1% and a specificity of 99.8%. SmoteBagging achieved a statistically significant better performance for this problem with an average recall of 78.1% and a specificity of 79.2%. EasyEnsemble reached the values of 77.8% and 79.3% for recall and specificity respectively. CalibratedAdaMEC's recall and specificity are 75.8% and 76%.
conclusionsIt was found that the screening performance of the models evaluated in this paper had a misdiagnosis rate lower than 25% except AdaBoost. The data used in these methods are only routine inspection data. This means that machine learning methods can help us build a fast, cheap, worthwhile and effective early screening approach for AD.
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