出血性脑卒中智能诊疗建模

Intelligent Diagnosis and Treatment Modeling of Hemorrhagic Stroke

本文针对血肿周围水肿的发生及进展进行建模,并深入探讨治疗干预和水肿进展的关联关系。首先基于100名患者的水肿体积与检查时间点数据,运用二次多项式、三次多项式以及一维高斯函数模型进行数据拟合,以找出最佳拟合模型。结果表明,一维高斯函数模型在拟合效果上表现出色,与实际数据高度吻合。因此,由此建立全体患者水肿体积随时间进展的曲线。接下来,通过K均值聚类将患者分为四个亚组,并分别构建了水肿体积随时间的曲线模型,分析患者群体之间的水肿体积变化趋势,为个体化治疗提供了重要线索。结果显示,不同亚组之间存在显著差异,这可能对治疗策略的制定和优化产生积极影响。最后,通过对患者的治疗数据进行多因素方差分析,来探究不同治疗方法对血肿体积、水肿体积的影响。结果表明脑室引流和降颅压治疗对血肿和水肿体积具有显著影响。这为医生在治疗选择上提供了有力的依据。同时,本文还通过Spearman相关系数计算发现血肿体积和水肿体积之间存在显著的正相关关系。

This study aims to model the occurrence and progression of edema around the hematoma,and explore the relationship between therapeutic interventions and edema progression.First,the data of the first 100 patients with edema volume and time points were fitted using quadratic,cubic polynomial,and one-dimensional Gaussian function models to find the best fitting model.The results show that the one-dimensional Gaussian function model performed well in terms of fitting effect and was highly consistent with actual data.Therefore,a curve of edema volume over time for all patients was established.Next,the patients were divided into four subgroups using K-means clustering,and edema volume over time curves were constructed for each subgroup to analyze the trend of edema volume changes among patient groups,providing important clues for individualized treatment.The results show that there are significant differences between subgroups,which may have a positive impact on the formulation and optimization of treatment strategies.Finally,a multivariate analysis of variance was conducted on the patient’s treatment data to investigate the effects of different treatments on hematoma and edema volume.The results show that ventricular drainage and decompressive craniectomy have a significant effect on hematoma and edema volume.This provides strong evidence for doctors to make treatment choices.Additionally,this study found a significant positive correlation between hematoma volume and edema volume using Spearman’s rank correlation coefficient.