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Pain fluctuation feature selection method and device, storage medium and equipment

A feature selection and volatility technology, applied in the field of scientific information, can solve the problems of inaccurate pain volatility results of patients, lack of representativeness and universality of feature selection, and achieve the effect of ensuring interpretability and high accuracy

Pending Publication Date: 2021-08-17
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention provides a pain volatility feature selection method, device, storage medium and equipment, which are used to solve the lack of representativeness and universality in the feature selection in the existing patient pain prediction model, resulting in the predicted patient pain volatility Technical issues with inaccurate results

Method used

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  • Pain fluctuation feature selection method and device, storage medium and equipment
  • Pain fluctuation feature selection method and device, storage medium and equipment
  • Pain fluctuation feature selection method and device, storage medium and equipment

Examples

Experimental program
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Embodiment 1

[0051] figure 1 It is a flow chart of the steps of the feature selection method of pain volatility described in the embodiment of the present invention, figure 2 It is a frame diagram of the feature selection method of pain volatility described in the embodiment of the present invention.

[0052] like figure 1 and figure 2 As shown, the embodiment of the present invention provides a feature selection method of pain volatility, comprising the following steps:

[0053] S10. Obtain the aggregated data of the patient's pain severity score, and analyze and process the aggregated data by adding a time interval variable to obtain pain volatility data.

[0054] It should be noted that, in step S10 , the main purpose is to obtain the pain severity score of the patient, and add the time interval variable to the set data for analysis and processing to obtain the pain volatility data. In this embodiment, a patient's pain severity score is taken as an example to illustrate the conten...

Embodiment 2

[0093] image 3 A frame diagram of the feature extraction device for pain volatility described in the embodiment of the present invention.

[0094] The embodiment of the present invention also provides a pain volatility feature selection device, including a data acquisition and analysis module 10, a prediction model building module 20, a first feature selection module 30, a second feature selection module 40, and a result feature output module 50;

[0095] The data acquisition and analysis module 10 is used to acquire the set data of the patient's pain severity score, and add the time interval variable to the set data for analysis and processing to obtain the pain volatility data;

[0096] The prediction model building module 20 is used to train the pain volatility data of the LASSO regression model and the random forest model by using five-fold cross-validation to obtain the LASSO regression prediction model and the random forest prediction model;

[0097] The first feature ...

Embodiment 3

[0110] An embodiment of the present invention provides a computer-readable storage medium, and the computer storage medium is used to store computer instructions, and when the computer is run on the computer, the computer can execute the above-mentioned method for feature selection of pain volatility.

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Abstract

The embodiment of the invention relates to a pain volatility feature selection method and device, a storage medium and equipment, and the method obtains pain volatility data through adding a time interval variable in the processing process of set data, enables the improved pain volatility data to be affected by a time interval, and improves the accuracy of the pain volatility feature selection, the change condition of the pain severity along with the time can be reflected more accurately; feature selection is performed on a feature result output by the LASSO regression prediction model based on an LASSO regression mode of logistic regression to obtain first feature data, and the first feature data is integrated with second feature data, third feature data and fourth feature data which are subjected to feature selection with a feature result output by the random forest prediction model; according to the method, the selected result features have representativeness and universality, the interpretability of the patient pain prediction model is guaranteed, high accuracy of the prediction result is still kept, and the problem that feature selection in an existing patient pain prediction model lacks representativeness and universality is solved.

Description

technical field [0001] The invention relates to the technical field of scientific information, in particular to a feature selection method, device, storage medium and equipment for pain volatility. Background technique [0002] Pain is one of the most common health-related problems and one of the most common reasons why patients seek medical help, so the use of pain to analyze the physical health of patients is becoming more and more widely used, and predicting changes in patient pain over time is one of them . Predicting the change of a patient's pain over time means that the patient will record his pain severity and other conditions several times during the prescribed follow-up period, and score himself according to his pain sensation each time. This score is the pain severity score. [0003] Before predicting the change of pain in patients over time, it is necessary to have a clear definition of the index of pain severity score. The earliest index was defined as: the ave...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N7/00
CPCG06N7/01G06F18/24323G06F18/214Y02P90/30
Inventor 吴晓鸰陈蔚星张辉凌捷
Owner GUANGDONG UNIV OF TECH
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