Stroke Clinical Data Management And Disease Auxiliary Prediction System

With the continuous improvement of people's living standards,the accelerating pace of life and the unreasonable dietary structure,the incidence of some chronic diseases has risen sharply.Among them,stroke is a chronic disease with high morbidity,high fatality rate,high disability rate,and high recurrence rate.It is the most common cause of death from heart disease and malignancy in most countries.It seriously jeopardizes the lives and health of the people,and it also brings a heavy economic and spiritual burden on the families and the society of patients.At present,the data related to stroke patients in the First People's Hospital of Yunnan Province are mainly scattered in the the Hospital Information System,Electronic Medical Record,Clinical Laboratory Information System,Picture Archiving and Communication System,and Medical Order Systems.This decentralized storage method causes these clinical data not to be collectively collected,processed and analyzed by doctors for medical research on stroke disease.For the above problem,this paper analyzes and designs the architecture and function of the stroke information management system according to the needs of the Department of Neurology of the First People's Hospital of Yunnan Province.Using the Java language,Bootstrap front-end development framework and Oracle database,a set of data specification,fast response,and easily expandable stroke clinical database system was designed and developed.We have also designed and implemented a set of interfaces to reasonably regulate the automatic collection of data.The clinical data of patients with stroke can be automatically extracted from the distributed business system to the stroke data warehouse for centralized management of stroke clinical data.And provide a quick and easy access to information,you can easily modify and update the information of patients with stroke.We also designed and implemented stroke defect scoring,statistical analysis,and user rights management.In addition,based on clinical indicators detection,an algorithm based on optimal risk and prevention model was used for disease-aided prediction,and thecorresponding algorithm was implemented in the stroke system.Finally,the results of the online operation of the system showed that the 439 clinical indicators related to stroke patients can be extracted from the decentralized business system into the system platform,which facilitates the centralized management of stroke clinical data;this system makes stroke clinical Data is easy to query and obtain.In addition,the function of the degree of stroke defect movement can effectively save hospital costs and improve work efficiency;the system's disease-assisted prediction function can initially predict the probability of the patient's illness and obtain the risk factors and prevention factors of the patient's detection index.