ArticleBMC bioinformatics2019
Computed tomography angiography-based analysis of high-risk intracerebral haemorrhage patients by employing a mathematical model.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis.BMC medical informatics and decision making · 2025Article
- Need for Transparency and Clinical Interpretability in Hemorrhagic Stroke Artificial Intelligence Research: Promoting Effective Clinical Application.Yonsei medical journal · 2024Article
- DMF-Net: a deep multi-level semantic fusion network for high-resolution chest CT and X-ray image de-noising.BMC medical imaging · 2023Article
- Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis.Frontiers in immunology · 2023Article
- Learn from antibody-drug conjugates: consideration in the future construction of peptide-drug conjugates for cancer therapy.Experimental hematology & oncology · 2022Review
- Geometric features of middle cerebral artery are associated with spontaneous basal ganglia intracerebral haemorrhage.Stroke and vascular neurology · 2022Observational
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- An integrated platform forFrontiers in genetics · 2022Article
- MCDB: A comprehensive curated mitotic catastrophe database for retrieval, protein sequence alignment, and target prediction.Acta pharmaceutica Sinica. B · 2021Article
- Revealing dynamic regulations and the related key proteins of myeloma-initiating cells by integrating experimental data into a systems biological model.Bioinformatics (Oxford, England) · 2021Article
- Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research.Frontiers in genetics · 2021Article
- Developing the novel bioinformatics algorithms to systematically investigate the connections among survival time, key genes and proteins for Glioblastoma multiforme.BMC bioinformatics · 2020Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
- Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis.Frontiers in genetics · 2020Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundHaemorrhagic stroke accounts for approximately 31.52% of all stroke cases, and the most common origin is hypertension. However, little is known about the method to identify high-risk populations of hypertensive intracerebral haemorrhage.
resultsThe results showed that the angle between the middle cerebral artery and the internal carotid artery (AMIC), the distance between the beginning of the median artery and superior trunk (DMS), and the density (CT value) of the lenticulostriate artery (CTL) were statistically significant enough to cause intracerebral haemorrhage. In addition, we chose these three potential features for the ensemble learning classification model. Our developed ensemble-learning method outperforms not only previous work but also three other classic classification methods based on accuracy measurements.
conclusionsThe developed mathematical model in the present study is efficient in predicting the probability of intracerebral haemorrhage.
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