Continuous health care evaluating for acute ischemic stroke patients with significant factor neural network relapse prediction model

Abstract

The effect of continuous medical service intervention on health management for people who have suffered from Acute Ischemic Stroke (AIS) is an important issue in health care tracking. To pick out core aspects related to health, a relapse prediction model, evaluate the efficiency of continuous care and boost post-discharge results, a structured study is designed. After investigation and scientific verification, important signs and symptoms were chosen to set up a Significant Factors Neural Network Relapse Prediction Model (SFNNR) which aims to predict possible relapses based on previous patterns in medical data. The continuous care group was compared with the control group, and it turned out that participants in continuous care had significantly better results with fewer chances of having relapses and controlling chronic risks while displaying less psychological stress compared to the control group; furthermore, the continuous medical service showed great value on long-term management of AIS patients. The study points out that the integrated care approach should be taken more seriously as it can help healthcare staff predict the risk of relapse accurately so as to come up with personalized plans to control the relapse probability of the patients.