Method for establishing student growth portraits based on group sparse fusion hospital big data

Abstract

The invention provides a method for establishing a student growth portrait based on group sparse fusion hospital big data, and the method comprises: obtaining multi-source high-dimensional data of students, including student basic information data, school learning data, daily life data and hospital regular training practice data; performing data preprocessing on the multi-source high-dimensional data; compressing and storing the preprocessed sample data set based on a triple representation method in a sparse algorithm; grouping the feature tags with strong correlation by using a binary K-meansclustering algorithm, and constructing a student growth portrait tag system which is more suitable for actual conditions of students; combining the labels into different feature label groups, and acquiring clustering distribution information of the sample data feature label groups based on local learning; and under the guidance of clustering distribution information, obtaining feature weights bymeans of group sparse regression, wherein the feature weights are used for evaluating the importance of features, and selecting corresponding important feature tags. A student growth portrait label system is established according to the high-dimensional data, and a student growth portrait fused with hospital big data stereoscopicity is constructed. By implementing the method, the collected data can be more complete, the constructed student portrait index system is more stereoscopic, complete and accurate, and subjective randomness is avoided to a certain extent.

Description

technical field [0001] The invention relates to the technical field of big data analysis, in particular to a method for establishing student growth portraits based on group sparse fusion hospital big data. Background technique [0002] "User portrait" was first proposed by Alan Cooper, the father of interaction design. He believes that user portrait is a virtual representative of real users and a target user model based on a series of real data. Massanari and Holden believe that user portrait is a user model generated by describing users with hobbies, preferences, snapshots and other elements (Massanari, 2010; Miaskiewicz, 2011). With the advancement of technology, domestic and foreign scholars have extracted similar user portrait characteristics in the process of user portrait research. However, in addition to the characteristics of labeling, user portraits are also dynamic and time-sensitive. Therefore, in order to ensure the effectiveness of user portraits, the portrait ...

AI Technical Summary

Problems solved by technology

[0010] (1) The student data used is not complete enough

The existing college student portrait index system is based on the data of the school. For medical students who need to practice in the hospital for at least one year, it is incomplete to only use the data of the school.

[0011] (2) The indicators provided are not comprehensive enough

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