Multilayer cloud computing framework coordinated integrated reduction method for gestational-age newborn brain medical records

Abstract

The invention discloses a multilayer cloud computing framework coordinated integrated reduction method for gestational-age newborn brain medical records. The multilayer cloud computing framework coordinated integrated reduction method comprises the steps of firstly constructing a multilayer MapReduce coordinated neuro-subpopulational structure, extracting each neuro-subpopulational elite optimal weighted margin WCi, adaptively dividing a large-scale brain medical record tissue attribute to n neuro-subpopulations which co-evolute through the MapReduce, and acquiring optimal dividing curved surfaces of different brain medical record tissues; and designing a neural network model with a five-layer structure, constructing an elite energy matrix NSMP, performing integrated coordinated reduction of a brain medical record curved surface Sub-curvei by each neuro-subpopulation optimal energy elite Elitist-leaderi, obtaining the optimal reduced set Redi<Ensemble> of a respective divided curved surface, and finally extracting a global optimal attribute reduced set Red<Ensemble> of the brain medical record tissue. According to the multilayer cloud computing framework coordinated integrated reduction method, the reduction efficiency and the reduction efficiency for the gestational-age newborn brain medical records in a big data environment are quickly improved through the multilayer MapReduce frame and coordinating with the neuro-subpopulational elite in a cloud computing environment. Furthermore the multilayer cloud computing framework coordinated integrated reduction method for the gestational-age newborn brain medical records has an important meaning for brain medical record selection, role extraction, clinical decision supporting service, etc.

Description

Technical field: [0001] The invention relates to the field of intelligent processing of medical information, in particular to a multi-layer MapReduce collaborative method for integrating and reducing cerebral medical records of pregnant newborns. Background technique: [0002] With the advent of the era of big data, in the electronic medical record database, the scale of the electronic medical record medical data center nodes is tens of thousands or even hundreds of thousands, and the medical data stored in the data center is measured in petabytes or even exabytes. The accumulated medical data of various electronic medical records poses severe challenges to its storage analysis, medical rules and knowledge acquisition technology. The valuable medical rules and knowledge contained in electronic medical record medical data have become the driving force for people to store and process medical record data. Therefore, how to shorten the processing time of large-scale and massive ...

AI Technical Summary

Problems solved by technology

However, the current use of these platforms for electronic medical records is only simple resource integration and sharing on the basis of the original medical record data warehouse, and knowledge reduction, diagnostic rule mining, and intelligent clinical decision support services for neonatal brain medical records in the cloud computing environment There are relatively few related studies

Images