关键词:
神经网络
最小二乘回归
线性回归
决策树
摘要:
出血性脑卒中是脑卒中的一种常见且致命的类型,其起病急、进展快、预后差,死亡率高。因此,研究血肿扩张、水肿发展及其预后预测具有重要临床意义。本文基于160位患者的个人史、疾病史、治疗方案及检查数据,采用多种机器学习模型(包括神经网络、最小二乘回归、XGBoost和LightGBM等)进行分析,构建了预测患者血肿扩展、水肿进展及mRS评分的数学模型。针对血肿扩张问题,本文首先利用患者首次检查与随访检查的数据,构建了血肿扩张的判定标准,并通过神经网络模型预测了所有患者发生血肿扩展的概率。结果表明,所建模型能够准确预测血肿扩展的发生,且具有较高的可靠性。对于水肿进展分析,本文结合最小二乘回归和决策树模型,深入探讨了不同治疗方法对水肿体积变化的影响,并通过Spearman’s rank correlation系数分析了血肿、水肿与治疗方法之间的关系。研究发现,不同治疗方法显著影响水肿体积的变化,且血肿与水肿的关系呈现一定的相关性。本研究通过多种模型的结合,提出了全面的血肿扩张与水肿进展预测框架,能够为临床提供精准的预警。创新性地将不同机器学习方法应用于出血性脑卒中的预后预测,并探讨了治疗方法与疾病进展的关系,为后续治疗优化提供依据。本文的模型和分析方法为出血性脑卒中患者的个性化治疗和预后评估提供了新的视角和技术支持。Hemorrhagic stroke is a common and fatal type of stroke, with acute onset, rapid progression, poor prognosis and high mortality. Therefore, it is of great clinical significance to study hematoma expansion, edema development and prognosis prediction. Based on the personal history, medical history, treatment plan and examination data of 160 patients, this paper uses a variety of machine learning models (including neural network, least squares regression, XGBoost and LightGBM, etc.) for analysis, and constructs a mathematical model to predict the patient’s hematoma expansion, edema progression and mRS score. For the problem of hematoma expansion, this paper first uses the data of the patient’s first examination and follow-up examination to construct the judgment criteria for hematoma expansion, and predicts the probability of hematoma expansion in all patients through the neural network model. The results show that the constructed model can accurately predict the occurrence of hematoma expansion and has high reliability. For the analysis of edema progression, this paper combines least squares regression and decision tree models to deeply explore the effects of different treatments on edema volume changes, and analyzes the relationship between hematoma, edema and treatment methods through Spearman’s rank correlation coefficient. The study found that different treatments significantly affect the changes in edema volume,