%0 Journal Article
%T 基于ISSA-BP的出血性脑卒中临床智能诊疗模型<br>Intelligent Diagnosis and Treatment Model of Hemorrhagic Stroke Based on ISSA-BP
%A 程依儿
%J Modeling and Simulation
%P 168-178
%@ 2324-870X
%D 2025
%I Hans Publishing
%R 10.12677/mos.2025.143212
%X 出血性脑卒中是威胁人体健康的主要慢性非传染性疾病之一&#65292;而血肿周围的水肿作为脑出血后继发性损伤的标志。随着医学领域的不断进步和智能算法的发展&#65292;血肿扩张的准确诊断和治疗变得更加迫切。本文基于ISSA-BP神经网络算法预测所有患者发生血肿扩张的概率模型&#65292;建立FCM聚类的高斯拟合不同亚组的水肿体积随时间进展曲线&#65292;并求解水肿真实数据和拟合曲线之间的残差&#65292;最后利用方差分析模型得到不同治疗方法对水肿体积进展模式的影响&#65292;为临床提供了重要的参考。<br>Hemorrhagic stroke is one of the major chronic noncommunicable diseases that threaten human health, and edema around hematoma is a sign of secondary injury after cerebral hemorrhage. With the continuous advancement of the medical field and the development of intelligent algorithms, the accurate diagnosis and treatment of hematoma expansion has become more urgent. In this paper, based on the probability model of predicting hematoma expansion in all patients based on ISSA-BP neural network algorithm, the FCM clustering Gaussian fitting curve of edema volume progression over time for different subgroups was established, and the residual difference between the real data of edema and the fitting curve was solved. Finally, analysis of variance model was used to determine the effect of different treatment methods on the progression pattern of edema volume, which provided an important reference for clinical practice.
%K 出血性脑卒中&#65292
%K ISSA-BP神经网络算法&#65292
%K FCM聚类<br>Hemorrhagic Stroke
%K ISSA-BP Neural Network Algorithm
%K FCM Clustering
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=109252