ArticleCNS neuroscience & therapeutics2021
Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.
Article in CNS neuroscience & therapeutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.
- Can Artificial Intelligence Be Applied to Diagnose Intracerebral Hemorrhage under the Background of the Fourth Industrial Revolution? A Novel Systemic Review and Meta-Analysis.International journal of clinical practice · 2022Pooled it
- Comparative associations of three nutritional indices with hematoma expansion after intracerebral hemorrhage.Frontiers in nutrition · 2026Article
- Predicting long-term outcomes in intracerebral hemorrhage: a comparative study of machine learning models highlights the prognostic value of hematoma clearance.Frontiers in neurology · 2026Article
- Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications.Journal of clinical medicine · 2025Review
- Interpretable machine learning model for early prediction of disseminated intravascular coagulation in critically ill children.Scientific reports · 2025Article
- Machine learning models predict coagulopathy in traumatic brain injury patients in ER.Frontiers in neurology · 2025Article
- Hematoma expansion prediction based on SMOTE and XGBoost algorithm.BMC medical informatics and decision making · 2024Article
- Review
- Must-have Qualities of Clinical Research on Artificial Intelligence and Machine LearningBalkan medical journal · 2023Review
- Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury.CNS neuroscience & therapeutics · 2023Article
- Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage.BMC medical informatics and decision making · 2022Article
- Machine learning models predict coagulopathy in spontaneous intracerebral hemorrhage patients in ER.CNS neuroscience & therapeutics · 2021Article
- Enhancing Robustness of Machine Learning Integration With Routine Laboratory Blood Tests to Predict Inpatient Mortality After Intracerebral Hemorrhage.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsCoagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms.
methodsA retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy-related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four-fold cross-validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score.
resultsAlbumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913-0.950), precision (92.4%, 95% CI: 0.897-0.951), F1 score (91.5%, 95% CI: 0.889-0.964), and recall score (93.6%, 95% CI: 0.909-0.964), and yielded higher area under the receiver operating characteristic curve (AU-ROC) (0.962, 95% CI: 0.942-0.982).
conclusionThe constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.
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