Trial reportComputational and mathematical methods in medicine2022
Prediction Model between Serum Vitamin D and Neurological Deficit in Cerebral Infarction Patients Based on Machine Learning.
Trial report in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
- Vitamin D and hypoxia-inducible factors signaling interplay: A hypothesis-driven review of therapeutic strategies for acute cerebral infarction.Metabolic brain disease · 2026Review
- Supervised model based polycystic ovarian syndrome detection in relation to vitamin d deficiency by exploring different feature selection techniques.Scientific reports · 2025Article
- Lactylation and Central Nervous System Diseases.Brain sciences · 2025Review
- Construction of a machine learning-based prediction model for unfavorable discharge outcomes in patients with ischemic stroke.Heliyon · 2024Article
- Development of machine learning-based models for predicting risk factors in acute cerebral infarction patients: a clinical retrospective study.BMC neurology · 2024Article
- Vitamin D: An Essential Nutrient in the Dual Relationship between Autoimmune Thyroid Diseases and Celiac Disease-A Comprehensive Review.Nutrients · 2024Review
- Inhibition of the Glycolysis Prevents the Cerebral Infarction Progression Through Decreasing the Lactylation Levels of LCP1.Molecular biotechnology · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Vitamin D is associated with neurological deficits in patients with cerebral infarction. This study uses machine learning to evaluate the prediction model's efficacy of the correlation between vitamin D and neurological deficit in patients with cerebral infarction. Methods: A total of 200 patients with cerebral infarction admitted to the Department of Neurology of our hospital from July 2018 to June 2019 were selected. The patients were randomly divided into a training set ( Results: In the training set, the area under the curve (AUC) of the logistic regression model and XGBoost algorithm model was 0.727 (95% CI: 0.601~0.854) and 0.818 (95% CI: 0.734~0.934), respectively. While in the test set, the AUC of the logistic regression model and XGBoost algorithm model was 0.761 (95% CI: 0.640~0.882) and 0.786 (95% CI: 0.670~0.902), respectively. Conclusion: The prediction model of the correlation between vitamin D and neurological deficit in patients with cerebral infarction based on machine learning has a good prediction efficiency.
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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 Socratic graph.