基于机器学习方法的脑出血后脑积水分类预测。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2025-01-14 DOI:10.1007/s12021-024-09710-5
Enwen Zhu, Zhuojun Zou, Jianxian Li, Jipan Chen, Ao Chen, Naifei Zhao, Qiang Yuan, Caicai Liu, Xin Tang
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引用次数: 0

摘要

为了构建脑出血后脑积水的临床分类预测模型,指导临床治疗决策,本文回顾性分析2019年5月至2022年10月岳阳市人民医院收治的844例脑出血合并脑积水住院患者的临床资料,其中脑出血后发生脑积水95例,无脑积水749例。比较两组患者的以下指标:性别、年龄、格拉斯哥昏迷量表(GCS)评分、出血量是否大于30ml、是否进入心室、改良graaeb评分(MGS)、改良Rankin评分(MRS)、是否手术、红细胞、白细胞、血小板。变量筛选后,选取GCS评分、MGS评分、MRS评分、出血量是否大于30ml、是否进入脑室、是否手术等6个变量,采用机器学习中的logistic回归模型和支持向量机模型进行建模分析。结果表明:在相同条件下,支持向量机模型的准确率为0.89,F1为0.838,支持向量机模型的AUC值为0.888;logistic回归模型的准确率为0.902,F1为0.89,支持向量机模型的AUC值为0.903。与非脑积水组相比,脑积水组患者出血量大于30ml,出血进入脑室,并行脑内手术,差异有统计学意义(P < 0.001)。统计分析显示,GCS评分≤8.8、改良Graeb评分(MGS)≥10、MRS评分≥3是自发性脑室出血后脑积水发生的独立危险因素。因此,对于GCS评分较低、改良Graeb评分较高、MRS评分较高、出血量> ~ 30ml、脑室出血、有颅脑手术经历的患者,应及早手术切除脑室内血肿,以减少脑积水的发生。
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Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.

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. The results showed that under the same conditions, the accuracy of the support vector machine model was 0.89 and F1 was 0.838 ,the value of the AUC of the support vector machine model is 0.888; the accuracy of the logistic regression model was 0.902 and F1 was 0.89, the value of the AUC of the support vector machine model is 0.903. Compared with the group without hydrocephalus, patients in the group with hydrocephalus had bleeding volume greater than 30 ml, haemorrhage into the ventricles of the brain, and had undergone surgery in the brain, and the difference was statistically significant (P 0.001). Statistical analysis showed that GCS score ≤ 8.8, modified Graeb score (MGS) ≥ 10 and MRS score ≥ 3 were independent risk factors for the development of hydrocephalus after spontaneous ventricular haemorrhage. Therefore, patients with lower GCS score, higher modified Graeb score, higher MRS score, bleeding volume > 30 ml, haemorrhage into the ventricles of the brain, and experience of having undergone surgery in the brain should be operated on early to remove the intraventricular haematoma in order to reduce the incidence of hydrocephalus.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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