A Smart Service Warehousing Platform Supporting Big Data Deep Learning Modeling Analysis

Abstract

Chronic disease management is the most expensive, fastest growing and most difficult problem for medical care workers in various countries. Current Health care information systems do not have interoperability characteristics and lack of data model standards, which makes it very difficult to extract meaningful information for further analysis. Deep learning can help medical care giver analyze various features of collecting data of patients and possibly more accurately diagnose and improve medical treatment through early detection and prevention. Our approach uses P4 medical model, which is predictive, preventative, personalized and participatory, which identifies diseases at early stage of diseases development, therefore it helps patients improve their daily behavior and health status.
In this paper, an effective and reliable intelligent service warehousing platform, which is a service framework and a middle layer, is designed to maintain the quality of service of the intelligent health care system and to analyze and design to predict the risk factors that contribute to diabetes and kidney disease. The mathematical prediction model is provided to doctors to support their patient’s treatment. At the end we verified the availability and effectiveness of this service platform from the data of hospital.