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Drug addiction degree detection method

A detection method and technology of drug addiction, applied in the field of drug addiction detection, can solve problems such as easy relapse, non-uniformity, and difficulty in reflecting drug addicts' detoxification, and achieve the effect of fast definition

Pending Publication Date: 2020-12-18
SHANGHAI UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This kind of subjective judgment standard is not uniform, and it is difficult to reflect whether drug addicts are really detoxified
This phenomenon leads to the low success rate of drug withdrawal and the problem of easy relapse after detoxification

Method used

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Experimental program
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Effect test

Embodiment 1

[0037] see Figure 1 to Figure 5 , a drug addiction degree detection method, the detection system adopted includes a behavioral module, a drug addiction stimulation module, a data acquisition module, a CNN model building module, a CNN classification module, and a result evaluation module; using a computer to carry out the steps of the drug addiction degree detection method as follows:

[0038] 1) Collection of behavioral indicators:

[0039] Behavioral modules include behavioral scales, patient oral statements, and hospital expert opinions. Composition of personality trait scale (16PF); collect the behavioral indicators of the data required for building the model, and determine the classification of the degree of drug addiction in behavior;

[0040] 2) Stimulate drug addicts' response to drugs:

[0041] After obtaining the classified drug users, use the drug addiction stimulation module, including the stimulation trigger paradigm of drug craving, restore the real drug use s...

Embodiment 2

[0052] This embodiment is basically the same as Embodiment 1, and the special features are as follows:

[0053] The behavioral module in step 1) includes behavioral scales, patient dictation, and hospital expert opinions; classify drug addiction degree by collecting and training modelers' behavioral scales, patient dictation, and hospital expert opinions.

[0054] The numbering of each picture is set in the stimulus triggering paradigm of the drug craving degree in the described step 2), and the task state stage time 6min is set. Each block is 10s, a total of 16 pictures are shown to the subjects, and the display time of each picture is 0.6s. At the beginning, the first 4 pictures are randomly displayed as a group, which will contain two drug stimulation pictures, and then use The remaining 12 pictures that the subjects saw were all randomly displayed as neutral images; after a block ended, there was a 4s interval image display, the interval image was white background, black c...

Embodiment 3

[0061] see figure 1 , this drug addiction degree detection method, the detection system that this method adopts comprises: behavioral module (one), drug addiction stimulation module (two), data acquisition module (three), CNN model building module (four), CNN classification module ( Five), result evaluation module (six) composition.

[0062] Among them, the behavioral module (1) includes behavioral scales, patient dictation, and hospital expert opinions (1); collects behavioral indicators of data required for model building, and determines behavioral classification.

[0063] The drug addiction stimulation module (2) includes the stimulation trigger paradigm (2) of drug craving degree, which restores the real drug use scene according to the drug use-related pictures, and stimulates drug users' reactions to drugs.

[0064] The data acquisition module (3) includes near-infrared data acquisition (3), which uses portable NIRSIT equipment to collect forehead near-infrared data of d...

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Abstract

The invention discloses a drug addiction degree detection method, a computer system is used for drug addiction degree detection, and an adopted detection system is composed of a behavioristics module,a drug addiction stimulation module, a data acquisition module, a CNN model establishment module, a CNN classification module and a result evaluation module. The method comprises the following operation steps: 1) collecting behavioral indexes; 2) stimulating the response of drug addicts to drugs; 3) collecting near-infrared data; and 4) establishing a CNN model. 5) performing CNN classification;and 6) evaluating a result. The method solves the problem that the addiction degree of the drug addiction personnel can only be defined according to artificial subjective factors. According to the method, the artificial intelligence method is used for classifying physiological data of mild, moderate and severe drug addiction personnel, and the average accuracy is 75%. According to the method, drugaddiction degree definition is more objective and standardized, and drug rehabilitation and rehabilitation trend is more scientific, reasonable and humanized.

Description

technical field [0001] The invention relates to a method for detecting the degree of drug addiction, which is applied to the aspects of social security stability and medical-industrial integration. Background technique [0002] The current research in the field of drug addiction focuses on the pathological analysis and treatment methods of drug addiction. In terms of drug addiction detection, it is mainly evaluated by scales. This subjective judgment standard is not uniform, and it is difficult to reflect whether drug addicts are truly detoxified. This phenomenon has led to the low success rate of drug withdrawal, and the problem of easy relapse after detoxification. This is also an important reason why drugs have been banned repeatedly. Contents of the invention [0003] In order to solve the above-mentioned technical problems, the present invention provides a method for detecting the degree of drug addiction, which can objectively evaluate the degree of drug addiction...

Claims

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Application Information

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IPC IPC(8): A61B5/00
CPCA61B5/4845A61B5/4884A61B5/7246A61B5/7267
Inventor 杨帮华李杜谷雪林高守玮夏新星
Owner SHANGHAI UNIV
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