Big Data Analysis Service Method Based On Health Cloud Platform

With the rapid progress of the Internet,cloud computing,big data and other technologies,traditional health care industry is entering the information age.This paper focuses on multi-sourced heterogeneous information fusion,early warning and doctor recommendation for typical diseases,and studies the high-mortality cardiovascular diseases with a big number of patients and a long incubation period,which also have significant impact on the economic life.Guided by the methodology of systems engineering and the mainline of “data-information-knowledge management”,with the goal of collaborative optimization,we will study intelligent fusion methods of multi-sourced heterogeneous health information,build early warning models of typical diseases based on long short-term memory algorithm,establish new doctor recommendation method based on relative attribute learning,and develop real-time analysis service support system,by using theories and methods in fields of big data,intelligent information processing and knowledge management.This paper will provide theoretical and practical supports for our government in improving healthcare service performance and better allocating healthcare resources.Therefore,this paper focuses on deep mining of the users' health data through machine learning algorithms,to predict the disease and recommend the right doctor for the patient.This paper first collects medical information,medical institution information and patient evaluation information from the medical information platform through web crawler technology,and combines the information obtained from different platforms and further preprocessing.Secondly,based on the health cloud platform,the use of long and short memory network(LSTM)model to predict the risk of patients with congestive heart failure,monitor the health status of patients with real-time and reduce the probability of disease.In addition,by combining the RankSVM algorithm,the selective sampling algorithm and integrated sorting strategy,a comprehensive recommendation based on relative attribute learning is proposed based on the multiple evaluation indexes.Finally,this paper developes disease risk prediction and doctor recommendation system,and designs the various functional modules API interface,and tests system performance.