ArticleJournal of clinical medicine2020
Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Cross-algorithm machine learning and consensus features for early disseminated intravascular coagulation risk prediction in sepsis-induced coagulopathy.Journal of translational medicine · 2026Article
- Metabolic dysfunction-associated fatty liver disease exacerbates hematoma expansion in intracerebral hemorrhage: an explainable machine learning approach.Frontiers in endocrinology · 2026Article
- Artificial intelligence in clinical thrombosis and hemostasis: A review.Research and practice in thrombosis and haemostasis · 2025Review
- Prediction of Postnatal Growth Failure in Very Low Birth Weight Infants Using a Machine Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Special Issue on "Disseminated Intravascular Coagulation: Current Understanding and Future Perspectives".Journal of clinical medicine · 2022Article
- Research frontiers and trends in the application of artificial intelligence to sepsis: A bibliometric analysis.Frontiers in medicine · 2022Article
- Interpretable Machine Learning to Optimize Early In-Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study.Computational and mathematical methods in medicine · 2022Article
- An Automated System for ECG Arrhythmia Detection Using Machine Learning Techniques.Journal of clinical medicine · 2021Article
- Artificial Intelligence for Clinical Decision Support in Sepsis.Frontiers in medicine · 2021Review
- An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning.Frontiers in medicine · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Sepsis-induced coagulopathy has poor prognosis; however, there is no established tool for predicting it. We aimed to create predictive models for coagulopathy progression using machine-learning techniques to evaluate predictive accuracies of machine-learning and conventional techniques. A post-hoc subgroup analysis was conducted based on the Japan Septic Disseminated Intravascular Coagulation retrospective study. We used the International Society on Thrombosis and Haemostasis disseminated intravascular coagulation (DIC) score to calculate the ΔDIC score as ((DIC score on Day 3) - (DIC score on Day 1)). The primary outcome was to determine whether the predictive accuracy of ΔDIC was more than 0. The secondary outcome was the actual predictive accuracy of ΔDIC (predicted ΔDIC-real ΔDIC). We used the machine-learning methods, such as random forests (RF), support vector machines (SVM), and neural networks (NN); their predictive accuracies were compared with those of conventional methods. In total, 1017 patients were included. Regarding DIC progression, predictive accuracy of the multiple linear regression, RF, SVM, and NN models was 63.7%, 67.0%, 64.4%, and 59.8%, respectively. The difference between predicted ΔDIC and real ΔDIC was 2.05, 1.54, 2.24, and 1.77 for the multiple linear regression, RF, SVM, and NN models, respectively. RF had the highest predictive accuracy.
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