A method for assessing mental health status

Abstract

This application provides a method for assessing mental health status, including: 1) Using machine learning methods to establish and train a mental health status assessment model based on network behavior characteristics based on individual network behavior characteristics and demographic characteristics in known samples ; 2) Obtain the network behavior characteristics and demographic characteristics of the new individual, and obtain the mental health status of the new individual according to the mental health status assessment model based on the network behavior characteristics. The invention eliminates the influence of individual subjective factors on data collection, and contributes to more accurate assessment of mental health status. In addition, the present invention can more conveniently collect individual data required for mental health status assessment, contributes to large-scale individual data collection in a wider range, and realizes large-scale mental health assessment.

Description

technical field [0001] The present invention relates to the technical field of mental health status evaluation and Internet, in particular, the invention relates to a mental health status evaluation method. Background technique [0002] At present, in the world, mental health problems have become the primary cause of individual "disability", and its negative consequences account for 37% of all disease hazards. Combined with its "global", " According to the development trend of "chronic" and "prevalent", the existence of mental health problems will not only cause huge consumption and waste of social resources, increase the burden on the whole society, but also directly threaten the health of individuals. Quality of daily life and subjective well-being. Specific to the actual situation in our country, according to the survey and research results, the overall level of people's mental health is also not optimistic, and the overall incidence of mental health problems has reached...

AI Technical Summary

Problems solved by technology

Although some of the subjective bias factors can be checked through some control methods (for example, validity scales, experimental design), it is very cumbersome and inefficient to implement similar controls repeatedly in similar studies, and some invalid data cannot be ruled out (for example, wrong answer, missing answer, blind answer)

[0007] (2) The details and scale of the collected data are limited

First of all, due to the constraints of time and space and some social conditions, it is difficult to recruit a sufficient number of individuals to fill in the scale for each experiment, and it is also difficult to track and measure the same group of individuals

Secondly, in order to facilitate individuals to complete the survey, it is impossible to design a large number of items on the scale, so that more details of individual behavior samples cannot be collected

In addition, the distribution, collection and entry of scales have also brought adverse effects on large-scale data collection.

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