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Symptom correlation early warning algorithm based on exponential weighted moving average

An exponentially weighted, moving average technology, applied in the field of big data analysis, to achieve the effect of accurate correlation coefficient and good early warning effect

Active Publication Date: 2018-12-14
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

There is no mathematical basis for symptom correlation analysis, combined with reality, the number of symptoms will have a temporal impact, but traditional correlation analysis does not add these covariates

Method used

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  • Symptom correlation early warning algorithm based on exponential weighted moving average
  • Symptom correlation early warning algorithm based on exponential weighted moving average
  • Symptom correlation early warning algorithm based on exponential weighted moving average

Examples

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Embodiment 1

[0028] Embodiment 1: as Figure 1-3 As shown, a symptom correlation early warning algorithm based on exponential weighted moving average, the specific method steps are as follows:

[0029] Step 1: Obtain the database established by the symptom table of a patient in a certain area for three months. The symptom incidence information table includes: number, visit time, symptom type and the number of symptoms corresponding to the symptom;

[0030] Step2: Symptom data preprocessing: Screen the collected symptom type information fields, compare the symptom type information fields with the symptoms required for research, and eliminate symptom data irrelevant to the research and useless symptom data that cannot be identified. The symptom incidence information collected in the database is sorted out to get Table 1: Symptom incidence information table:

[0031]

[0032] Table 1: Symptom morbidity information table

[0033] Step3: Calculate exponentially weighted moving average of s...

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Abstract

The invention relates to a symptom correlation early warning algorithm based on exponential weighted moving average, and belongs to the field of big data analysis. Specifically, a symptom incidence database is established, the collected symptom information is screened and processed and the symptom which is not in the database is eliminated according to the requirement of the familiar symptom. Theconventional correlation algorithm only calculates the basic average number of symptoms and ignores the influence of the time before and after the symptom data. According to the symptom correlation early warning algorithm based on the exponential weighted moving average, the mean obtained by exponential weighting is taken as the basis of the correlation algorithm so that the historical retrospective data and the current data are combined to make the correlation coefficient more accurate, then data comparison is performed according to the obtained correlation coefficient to find out the data abnormity and obtain the time point of early warning and the data source early warning and thus the better early warning effect can be achieved.

Description

technical field [0001] The invention relates to a symptom correlation early warning algorithm based on exponentially weighted moving average, which belongs to the field of big data analysis. Background technique [0002] With the development of society, various infectious diseases are ravaging the human body, bringing great pain to countless families, and with the progress of society, the level and speed of personnel mobility are gradually increasing, which also makes infectious diseases spread among people. The spread of infectious diseases has been intensified, so many departments have implemented plans for early warning of infectious disease outbreaks. There is no mathematical basis for the symptom correlation analysis, combined with reality, the number of symptoms will have a temporal impact, but the traditional correlation analysis does not add these covariates. Contents of the invention [0003] The technical problem to be solved by the present invention is to provi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G16H50/80
CPCG16H50/80
Inventor 粘冬晓杜庆治龙华邵玉斌
Owner KUNMING UNIV OF SCI & TECH
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