Fast statistical query method for mass medical data

Abstract

The invention discloses a fast statistical query method for mass medical data. The method includes: firstly, collecting, filtering and processing medical data from different data sources, and storing processed medical data into an HDFS (Hadoop distributed file system) or HBase; using the HDFS or HBase to create and initialize statistics trees for one or certain statistics-based medical data; subjecting the newly added medical data to single or multiple incremental computations, and updating the statistics tree by results of the incremental computations; allowing a server to respond to a statistical query request from a user according to the one or a plurality of created statistics trees, to analyze the statistical query request into sub-queries based on one or the plurality of statistics trees, and to combine into a plurality of sub-query results fed back to the user. The position of statistical data can be located quickly by the method, access to mass data is avoided, system load is lightened, and query efficiency and access concurrency are increased.

Description

technical field [0001] The invention relates to the technical field of medical data, in particular to a fast statistical query method for massive medical data. Background technique [0002] At present, with the continuous and stable development of my country's economy and the rapid development of modern technology, people are paying more and more attention to their own health. In addition to meeting the needs of daily work and life, people also urgently hope that they can check their health information at any time through the Internet or mobile phones. Seasonal information of epidemics, understanding the medication situation of each disease, and getting some personalized recommendation services for one's own disease, etc. For public health institutions, they hope that the medical data of residents in various communities can be automatically summarized and statistically analyzed. The statistical results are used for epidemic trend analysis and outbreak warning, so as to provid...

AI Technical Summary

Problems solved by technology

At present, there are two main algorithms for realizing rapid statistics of medical data in the existing technology: one is to use traditional databases to implement real-time statistics, but the efficiency of statistical queries using this method is very low; the other is to use MapReduce to hdfs Parallel statistical analysis of the data on the database, and then save the analysis results to the hbase server, and finally realize the statistical query on the hbase server. Compared with the traditional relational database processing method, the time complexity of this algorithm is obviously superior, but still It does not effectively solve the problem of increasing the amount of calculation caused by the increase in the amount of data, and still cannot guarantee the stability of the algorithm in the statistical process

[0005] It can be seen that due to the large amount of data generated by the national medical information service system every day, users have high requirements for real-time processing of statistical queries. Therefore, traditional databases, commercial parallel databases or NOSQL databases are simply used for online statistical analysis of massive medical data and then returned. The way of the result can no longer meet the actual situation and needs

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