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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4728705PMC
http://dx.doi.org/10.11622/smedj.2016013DOI Listing

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Introduction Intraventricular hemorrhage is a severe condition caused by bleeding within the brain ventricles. It is often due to trauma, tumors, vascular malformation, aneurysm, oxygen deprivation, or idiopathic. A common complication associated with intraventricular hemorrhage is hydrocephalus, which is the accumulation of cerebrospinal fluid in the ventricles.

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Background: Appropriate management of spontaneous intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) requires rapid, accurate volume estimation. Viz.AI has developed an artificial intelligence (AI)-powered ICH calculation tool that may improve existing methods.

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Background: Numerous noncontrast computed tomography (NCCT) markers have been reported and validated as effective predictors of hematoma expansion (HE). Our objective was to develop and validate a score based on NCCT markers and clinical characteristics to predict risk of HE in acute intracerebral hemorrhage (ICH) patients.

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In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment decisions, this paper retrospectively analyses the clinical data of 844 cases of ICH and hydrocephalus inpatients admitted to Yueyang People's Hospital from May 2019 to October 2022, of which 95 cases of hydrocephalus occurred after ICH and no hydrocephalus in 749 cases. The following indicators were compared between the two groups of patients: gender, age, Glasgow Coma Scale(GCS)score, whether the amount of bleeding was greater than 30 ml, whether it broke into the ventricle or not, modified Graeb score(MGS), modified Rankin Scale (MRS) score, whether surgery was performed or not, red blood cells, white blood cells, and platelets. After variable screening, the following six variables were selected: GCS score, MGS, MRS score, whether the bleeding volume was greater than 30 ml, whether it broke into the ventricle or not, and whether surgery was performed or not were modelled and analysed using logistic regression model and support vector machine model in machine learning.

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