Data desensitization method for sudden acute infectious diseases

Abstract

The invention relates to a data desensitization method for sudden acute infectious diseases. The method comprises the following steps: 101, inputting file data: clicking to select a original data and select a file to be desensitized; 102, performing field desensitization setting: setting a data storage mode, selecting a desensitization field, and choosing a desensitization mode; 103, outputting the desensitized file data: setting an output path, starting data desensitization, and displaying the desensitized result file under a target path after the desensitization is completed. The method can independently select desensitization methods according to data characteristics of different sudden acute infectious diseases, can select and extract valid data from a complete data table to generate a new table, and can quickly complete the desensitization, extraction and collation of analysis data on the basis of maintaining the integrity of the original data.

Description

technical field [0001] The invention belongs to the technical field of medical equipment, and in particular relates to a data desensitization method for sudden acute infectious diseases, which realizes batch data desensitization of data that may be marked with patient privacy information or not involved in statistical analysis. Background technique [0002] Etiological data or epidemiological survey data are the basis for statistical analysis of various sudden acute infectious diseases. However, most of this type of data involves personal privacy information, and sensitive data needs to be desensitized before analysis. At present, the etiological or epidemiological investigation data obtained by laboratory testing are usually uploaded to the epidemic data monitoring network developed by the National Centers for Disease Control and Prevention to desensitize sensitive data. The general process is to organize the etiological data or epidemiological investigation data into an e...

AI Technical Summary

Problems solved by technology

[0004] At present, when local disease control or laboratories conduct statistical analysis on some data, they are still in the strategy of manually picking valid data, which not only increases the workload of scientific researchers, but also easily causes poor data consistency.

In addition, although sensitive data has been processed in the data downloaded from the epidemic data monitoring system, the data format is fixed, and data redundancy often occurs during statistical analysis, which is not conducive to data analysis. , air detection indicators, logistics information or geospatial information, etc.) data incompatibility problems occur when performing multi-scale calculations

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