Research On Evaluation Model Of Health Statement Based On Deep Learning From Multiple Physiological Big Data

Big data has opened a major era of transformation,is changing the way of our life and understanding of the world,become a source of new inventions and new services,more changes are under way.At the same time,AI also makes a series of changes in the field of health care.Among them,disease prevention and disease diagnosis become an important direction of medical field change.The big-data technology has brought new opportunities for the development of medical and health fields,and data-driven support for medical health information is rapidly developing.Medical data sources are extensive and have complex types and structu res,it leads to the difficult to establish an accurate feature learning model by using the general statistical learning algorithms or the machine learning algorithms.Based on that,this paper applies the deep-learning theory to the analysis of physiological data,and designs a deep artificial neural network that learns the key features of physiological time-series data in unsupervised learning.Then,we design a health state assessment model based on the theory of multivariate Gaussian distribution,which inputs are the data characteristics learned by the deep artificial neural network.The main contents of the thesis are as follows:(1)Research on Deep Feature Learning of Time Series DataBased on Convolution Neural Networks theory,a deep artificial neural network model of unsupervised learning is designed to learn the deep-seated characteristics of multidimensional physiological time-series data.Based on the automatic coding algorithm,the data feature is learned from unlabeled data by using unsupervis ed learning methods.Research and design the network model of the number of layers,network nodes and learning rate and other key parameters,so that the model can obtain the key features efficiently and accurately from the physiological timing data.(2)Research on Health Status Assessment ModelConstructing Health State Assessment Model Based on Multivariate Gaussian Distribution Theory.This paper research how to use the multivariate Gaussian distribution theory to design an evaluation model that can effic iently and accurately assess the user ’s health status.(3)System experiment and simulationUsing the Python and MATLAB software platform to complete the experiment and simulation which object is 40 sets of 8 × 8064 dimensional physiology data from 32 volunteers.First,determine the best parameters for a set of health assessment models.Then,using the model to learn the characteristics of physiological parameters;Finally,the state assessment model to obtain the results of health assessment.The experimental results show that the feature learning model based on the convolution neural network theory can effectively extract the deep features of multidimensional physiological large data.The health state assessment model based on the Multivariate Gaussian Distribution theory,can effectively evaluate the health status of the human body.