Intelligent Analysis And Application Of Multi-source Medical Data

The health data,mainly obtained from medical examinations such as blood test and ElectroCardioGram(ECG),is an important way for people to perceive their physical conditions.In recent years,the development of wearable smart devices provides new ways and tools for people to get their health data.However,health data obtained from different hospitals and different ways is diversified,multi-layered.How to deal with and analysis these multi-sourced data is becoming a pressing issue.The goal of this report is to analysis health data using deep leaning and to excavatepotential information within the data,which provides better decision support for hospital management and more accurate assessments of health risk for people.The main contributions of this report are as follows:Firstly,for the multi-source problems of paper medical examinations,this paper presents a model of multi-source medical examinationsclassifier based on deep learning.The model performs morphological operations such as noise reduction,anti-skew,swelling,and corrosion on medical examinations images;then,an improved inception convolution neural network is proposed to classify the threshold-segmented images.It lays the foundation of textsrecognition in medical examinations.Secondly,in view of the difficulty of text recognition in images,this paper proposes a deep learning model: SCRNN which is suitable for recognizing the text of multi-source medical documents.This model combines convolution neural networks with long-term short-term neural networks to fully extract the spatial features of the images and the contextual relationships of the texts,so as to structure the data of the medical examination so that the personal health data can be collected,and historical health can be gradually formed.file.Compared with the open source engine Tesseract,the recognition accuracy of the SCRNN Chinese-English mixed phrase group is relatively high,and the accuracy rate is 97.2%.Finally,to achieve the automatic diagnosis of heart diseases through electrocardiogram,a neural network named M-ECG based on Long Short TimeMemory network is proposed to diagnose ECG in real time.Labeled bipolar leads of electrocardiogram is used to train M-ECG,and signals within three continuous heart beat from the two polars are utilized as the input data during the training process,which effectively improves the prediction accuracy of M-ECG.The proposed model has been validated on the public MIT-BIH arrhythmia data set,and experimental results demonstrate that M-ECG achieves better accuracy and performance over1D-CNN and improves the prediction accuracy compared to other deep learning networks.