Design And Implementation Of A Heterogeneous Clinical Data Analysis System Based On Deep Learning Technology

The application of computer-aided diagnosis system can reduce the workload of doctors to a certain extent.However,the traditional computer-aided diagnosis system has been unable to deal with the clinical data sets that are gradually increasing in scale.The emergence of deep learning technology makes the use of computer technology to It is possible to analyze and diagnose large-scale clinical data.Doctors usually use medical imaging data,electronic medical record text data,inspection index data and other heterogeneous multimodal data as the basis for diagnosis.However,most of the common computer-aided diagnosis systems based on deep learning technology currently use only one clinical Examining the data for analysis,the obtained characteristic information has limitations,so that the generated auxiliary diagnosis opinion lacks a comprehensive consideration of the patient's condition.Therefore,this paper designs and implements a heterogeneous clinical data analysis system based on deep learning technology,and analyzes a variety of clinical data generated during the comprehensive diagnosis process to obtain more accurate auxiliary diagnosis opinions.The system adopts deep learning technology to model and analyze heterogeneous clinical data according to modalities.On the basis of modeling the data,a multi-modal fusion strategy based on decision-making is used,and two fusion mechanisms are adopted: voting and weighting.Achieve fusion analysis of heterogeneous clinical data.In addition,this article also uses the Alzheimer's disease public data set with complete patient clinical data to compare and evaluate the system's data processing,model performance and fusion mechanism effects.The experimental results verify the effectiveness of the analysis model and fusion mechanism designed and implemented in this paper,and prove that the method of considering heterogeneous multi-modal data is not only closer to the real diagnosis process,but also helps the doctor to grasp the patient's overall condition and obtain More accurate judgment results.