Research On Key Technologies For Prediction Of Proactive First Aid For Pre-hospital Critical Illness

In the field of critical care emergency,with the gradual development of science and technology,the ability of computer equipment to store information and the ability of sensors to collect information are getting stronger and stronger,and the increasingly complex and diverse data need to be further utilized,especially time series and spatial and temporal data.Under the existing emergency mode,emergency resources cannot be deployed at any time,and this reactive emergency mode also restricts the development of emergency system construction.Based on the above-mentioned problems,a new method of processing medical emergency data and making scientific prediction to realize proactive emergency has become a key problem to be solved.Based on the above problems,this paper combines data analysis methods.This article combines data analysis methods,traditional time series prediction methods,and deep neural network prediction methods to implement an active emergency analysis and prediction system for critically ill patients.The purpose is to rationally allocate emergency resources,alleviate local emergency pressure,and provide a golden rescue period for critically ill patients,thereby transforming emergency treatment from passive to active.The contributions of this article are as follows:1.Based on the regression prediction problem of emergency data,an emergency time series dataset is constructed and the key algorithm SIL-TSP based on integrated learning for active emergency prediction is proposed.First,the emergency data are preprocessed and transformed into a critical care emergency time series dataset and its subset traffic accident emergency time series dataset by day.Then,the six traditional time series regression prediction models are experimented by finding the optimal model for each of them,and finally,the traditional model is used as the base model and then the averaging mechanism is used as the second layer model by using the idea of Stacking in integrated learning.2.Based on the distribution prediction problem of emergency data,the emergency spatiotemporal dataset is constructed and the key algorithm AST-CNN based on spatio-temporal attention mechanism is proposed for active emergency prediction.First,the emergency data are pre-processed and transformed into emergency spatio-temporal data by day.Then,the spatiotemporal features are extracted using convolutional neural network combined with attention mechanism,and then the distribution of critical care patients is predicted.3.Based on the problem of untimely and unreasonable processing of emergency data,a pre-hospital emergency database is established,a proactive emergency analysis and auxiliary prediction visualization system is constructed,and the proactive emergency prediction algorithm is applied to practice by building a complete system.The system combines the proactive EMS prediction algorithm,user management method and EMS data analysis method described in this paper.Through the pre-institutional critical care emergency data,this paper firstly implements SIL-TSP based on regression prediction model using the idea of integrated learning,by which a better result can be achieved on two emergency time series datasets.Then the AST-CNN is implemented by using the idea of attention mechanism and neural network,and the model achieves good results on the spatio-temporal dataset of first aid,which strongly promotes the landing and application of deep learning in the field of medical emergency.Finally,an active emergency visualization system is constructed.Through this system,medical and nursing staff can grasp timely information related to the visualization of regional pre-hospital emergency data as well as the possible regression and distribution prediction of critical illnesses,achieving the effect of changing the existing passive allocation of emergency resources to active allocation,changing the status quo of the passive emergency model and providing ideas for the development of proactive emergency care.