A psychological test scale combination generation method and system

Abstract

The present invention discloses a psychological test scale combination generation method and system in the field of psychological test scales. By acquiring a psychological scale historical data set, a classification process is performed, and a plurality of symptom labels are generated accordingly; the historical positive rate of the comprehensive scale symptom is calculated according to the psychological scale historical data set to obtain the first-order probability index; the positive support rate of the single evaluation item scale item is calculated according to the psychological scale historical data set, that is, the second-order probability index; the comprehensive scale and the single evaluation item scale are sorted and matched, so as to construct a multi-branch tree; the second-order positive rate of the user is calculated in real time to determine whether the user is positive in the symptom. The present invention can combine the comprehensive psychological test scale with the single evaluation item psychological test scale, and generate a dynamic psychological test scale according to the real-time option selection of the person being evaluated, so as to quickly and accurately obtain one or more positive symptoms or a judgment of no obvious symptoms.

Description

Technical Field

[0001] The invention belongs to the field of psychological test scales, and in particular relates to a psychological test scale combination generation method and system. Background Art

[0002] Adolescence is an important stage in the physical and mental development of a person, and it is also the period of life that is most susceptible to psychological stress. Studies have shown that current adolescents generally face psychological problems such as academic pressure, strained family relationships, interpersonal difficulties, anxiety, depression, etc. If these problems are not promptly and effectively intervened, they may have irreversible effects on the physical and mental health, growth and future of adolescents. In psychological assessment, the use of scales is a common assessment method. The purpose of psychological scales is to help adolescents understand their own psychological conditions, identify potential psychological problems, provide relevant psychological intervention and help, and promote the psychological health growth of adolescents. Through assessment, early detection and intervention, psychological problems that adolescents may face during their growth, such as emotional problems, behavioral problems, learning problems, etc., can be prevented and reduced to the greatest extent. At the same time, the adolescent mental health assessment system can also provide reference and guidance for parents, teachers and other relevant personnel in the society, helping them to better understand the importance of adolescent mental health and improve their awareness and attention to adolescent mental health.

[0003] However, the existing general testing of mental illness has the following problems: although the comprehensive psychological test scale covers a wide range of testable symptoms, the number of questions is too large, and although the single-item psychological test scale has a smaller number of questions than the comprehensive psychological test scale, the coverage is narrow and single, resulting in low efficiency in confirming positive symptoms in psychological tests, which is time-consuming and labor-intensive. When the person being tested feels tired, it will lead to evaluation errors and inaccurate results. Summary of the invention

[0004] One of the purposes of the present invention is to provide a method for generating a combination of psychological test scales, which can combine a comprehensive psychological test scale with a single assessment item psychological test scale, and generate a dynamic psychological test scale based on the real-time option selection of the person being evaluated, so as to quickly and accurately obtain a judgment of one or more positive symptoms or no obvious symptoms.

[0005] One of the purposes of the present invention is achieved in this way: a method for generating a combination of psychological test scales, the method comprising the following steps:

[0006] Step S101, obtaining a historical data set of psychological scales, wherein the historical data set of psychological scales includes a comprehensive scale, a single evaluation item scale, and diagnostic data; for the historical data set of psychological scales, classifying the comprehensive scale and each single evaluation item scale according to the detected symptoms, and generating a plurality of symptom labels of the comprehensive scale based on the classification results;

[0007] Step S102, calculating the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index, and calculating the positive support rate of each item of the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index; the first-order probability index of each symptom label is the ratio of the number of people with positive diagnosis results of the symptom in the set of comprehensive scales to the number of people using the set of comprehensive scales, and the second-order probability index of each item of the single evaluation item scale is the ratio of the total number of people who show positive results for the item in the set of single evaluation item scales to the number of people using the set of single evaluation item scales;

[0008] Step S103, arrange the first-order probability indexes in descending order from large to small, and arrange the disease labels corresponding to the first-order probability indexes in the comprehensive scale in descending order to obtain an optimized comprehensive scale, arrange the second-order probability indexes in descending order from large to small, and arrange the items corresponding to the second-order probability indexes in each single evaluation item scale in descending order to obtain corresponding question numbers, and obtain an optimized single evaluation item scale;

[0009] Step S104, matching each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure;

[0010] Step S105, sequentially generate each item under each symptom label of the optimized comprehensive scale in the multi-branch tree structure according to the arrangement order of each symptom label, calculate the first-order positive rate of the symptom label of the user according to the real-time option selection of the user in the optimized comprehensive scale, if the first-order positive rate is greater than or equal to the first-order preset value, proceed to the next step; if the first-order positive rate is less than the first-order preset value, it indicates that the corresponding symptom label of the user is negative, and determine whether the last symptom label of the optimized comprehensive scale has been reached according to the multi-branch tree structure, if not, proceed to generate the item of the next symptom label, if yes, terminate the item generation directly;

[0011] Step S106, generate and test the items of the optimized single evaluation item scale corresponding to the disease label, calculate the second-order positive rate of the user's disease in real time according to the option selected by the user in the optimized single evaluation item scale, and once the second-order positive rate is greater than or equal to the second-order preset value, end the item generation of the disease and determine that the disease is positive; if after all items of the optimized single evaluation item scale are tested, the calculated second-order positive rate of the user's disease is less than the second-order preset value, then determine that the disease is negative;

[0012] Step S107, judging whether the last symptom label of the optimized comprehensive scale has been reached according to the multi-branch tree structure, if not, returning to step S105 to generate the item of the next symptom label; if yes, terminating the item generation.

[0013] As a further improvement of the present invention, the comprehensive scale contains several items for psychological testing, and the single evaluation item scale contains several items for psychological testing. The number of items corresponding to the corresponding symptom labels in the comprehensive scale is small and does not need to be sorted; the items in the single evaluation item scale are specifically used to test a certain symptom; the items in the comprehensive scale and the single evaluation item scale are all single-choice. The items in the comprehensive scale and the single evaluation item scale refer to the questions for testing the subject.

[0014] As a further improvement of the present invention, in step S101, each symptom label of the comprehensive scale includes a plurality of corresponding items.

[0015] As a further improvement of the present invention, the first-order positive rate is the ratio of the number of positive items under the disease label of the user in the optimized comprehensive scale to the number of all items under the disease label, and the first-order preset value is the ratio of the lowest score of positive symptoms to the total score; the second-order positive rate is the ratio of the number of positive items to the total number of items in the optimized single evaluation item scale of the user, and the second-order preset value is the ratio of the lowest score of positive symptoms to the total score. There are multiple options for each item under the corresponding disease label of the optimized comprehensive scale, and each option may be "none, mild, moderate, severe and extreme", and the corresponding scores are "0 points, 1 point, 2 points, 3 points and 4 points". When "moderate, severe and extreme" are selected, the item is positive, then 2 points is the lowest positive score, and the first-order preset value is the ratio of the lowest score of positive symptoms * the number of items and the total score * the number of items, that is, the ratio of the lowest score of positive symptoms of a single item to the total score, and the lowest score of positive symptoms and the total score are both fixed values ​​that have been specified; for the optimized single evaluation item scale, the lowest score of positive symptoms and the total score are both fixed values ​​that have been specified.

[0016] The second purpose of the present invention is to provide a psychological test scale combination generation system, which can combine a comprehensive psychological test scale with a single assessment item psychological test scale, and generate a dynamic psychological test scale through the combination of various functional units according to the real-time option selection of the person being evaluated, so as to quickly and accurately obtain a judgment of one or more positive symptoms or no obvious symptoms.

[0017] The second object of the present invention is achieved as follows: a generation system of a psychological test scale combination generation method, comprising a symptom label generation unit, the symptom label generation unit is connected to a probability index generation unit, the probability index generation unit is connected to a sorting optimization unit, the sorting optimization unit is connected to a matching discrimination unit, the matching discrimination unit is connected to a positive diagnosis unit, wherein:

[0018] The symptom label generating unit is used to obtain a historical data set of psychological scales, which includes a comprehensive scale, a single evaluation item scale and diagnostic data, classify the comprehensive scale and each single evaluation item scale according to the detected symptoms, and generate a plurality of symptom labels of the comprehensive scale based on the classification results;

[0019] The probability index generating unit is used to calculate the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index, and calculate the positive support rate of each item of the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index; the first-order probability index of each symptom label is the ratio of the number of people with positive diagnosis results of the symptom in the set of comprehensive scales to the number of people using the set of comprehensive scales, and the second-order probability index of each item of the single evaluation item scale is the ratio of the total number of people with positive diagnosis results of the item in the set of single evaluation item scales to the number of people using the set of single evaluation item scales;

[0020] The sorting optimization unit is used to arrange the first-order probability indexes in descending order from large to small according to their values, and arrange the disease labels corresponding to the first-order probability indexes in the comprehensive scale in descending order to obtain an optimized comprehensive scale, arrange the second-order probability indexes in descending order from large to small according to their values, and arrange the items corresponding to the second-order probability indexes in each single evaluation item scale in descending order to obtain corresponding question numbers, and obtain an optimized single evaluation item scale;

[0021] The matching discrimination unit is used to match each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure, and sequentially generate each item under each symptom label of the optimized comprehensive scale in the multi-branch tree structure according to the arrangement order of each symptom label, and calculate the first-order positive rate of the user's symptom label according to the real-time option selection of the user in the optimized comprehensive scale. If the first-order positive rate is greater than or equal to the first-order preset value, the positive diagnosis unit is entered; if the first-order positive rate is less than the first-order preset value, indicating that the corresponding symptom label of the user is negative, the item generation of the next symptom label is sequentially performed;

[0022] The positive diagnosis unit is used to diagnose whether the disease is positive, generate and test the items of the optimized single evaluation item scale corresponding to the disease label, and calculate the second-order positive rate of the user's disease in real time according to the user's option selection in the optimized single evaluation item scale. Once the second-order positive rate is greater than or equal to the second-order preset value, the project generation of the disease is terminated and the disease is determined to be positive; if after all items of the optimized single evaluation item scale are tested, the calculated second-order positive rate of the user's disease is less than the second-order preset value, the disease is determined to be negative; after determining whether the disease is negative or positive, determine whether it has reached the last disease label of the optimized comprehensive scale according to the multi-branch tree structure. If not, return to the matching judgment unit to generate the next disease label project; if so, terminate the project generation.

[0023] As a further improvement of the present invention, the disease label generating unit comprises:

[0024] The historical data set module is used to obtain historical data sets of psychological scales, including comprehensive scales, single-item scales, and diagnostic data;

[0025] A classification processing module is used to perform comprehensive scale and single assessment item scale symptom classification processing on the psychological scale historical data set to obtain symptom classification results;

[0026] The label generation module generates a plurality of symptom labels of comprehensive scales based on the classification results.

[0027] As a further improvement of the present invention, the probability index generating unit includes:

[0028] First-order probability index module: used to calculate the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index;

[0029] Second-order probability index module: used to calculate the positive support rate of each item in the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index.

[0030] As a further improvement of the present invention, the sorting optimization unit includes:

[0031] A first-order sorting module: used to sort the first-order probability indexes in descending order and sort the multiple symptom labels of the comprehensive scale to obtain an optimized comprehensive scale;

[0032] Second-order sorting module: arranges the second-order probability index in descending order, and arranges the items of the single evaluation item scale in descending order to obtain an optimized single evaluation item scale.

[0033] As a further improvement of the present invention, the matching determination unit includes:

[0034] Matching module: Match each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure;

[0035] Branch discrimination module: according to the real-time option selection of the user in the comprehensive scale, the first-order positive rate of the user's disease is calculated. If the first-order positive rate is greater than or equal to the first-order preset value, the positive diagnosis unit is entered. If the first-order positive rate is less than the first-order preset value, it indicates that the user's corresponding disease label is negative; the first-order positive rate is the ratio of the number of positive items under the disease label of the user in the optimized comprehensive scale to the number of all items under the disease label, and the first-order preset value is the ratio of the lowest score of positive symptoms to the total score;

[0036] Symptom analysis module: If the positive diagnosis unit is not entered, it is determined whether the last symptom label of the optimized comprehensive scale is reached according to the multi-branch tree structure. If not, the project generation of the next symptom label of the optimized comprehensive scale is entered. If yes, the project generation is terminated.

[0037] As a further improvement of the present invention, the second-order positive rate in the positive diagnosis unit is the ratio of the number of positive items in the user's optimized single evaluation item scale to the total number of items, and the second-order preset value is the ratio of the minimum score of positive symptoms to the total score.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention obtains the historical data set of the psychological scale, performs classification processing, and generates multiple disease labels accordingly; calculates the historical positive rate of the comprehensive scale disease according to the historical data set of the psychological scale, and obtains the first-order probability index; calculates the positive support rate of the single evaluation item scale item according to the historical data set of the psychological scale, that is, the second-order probability index; performs sorting and matching processing of the comprehensive scale and the single evaluation item scale, thereby constructing a multi-branch tree; calculates the first-order positive rate of the user in real time according to the real-time option selection of the user in the comprehensive scale, and judges whether the condition of entering the branch, that is, the single evaluation item scale, is met; calculates the second-order positive rate of the user in real time according to the real-time option selection of the user in the single evaluation item scale, and judges whether the user is positive in the disease. The comprehensive psychological test scale and the single evaluation item psychological test scale can be combined, and a dynamic psychological test scale can be generated according to the real-time option selection of the person being evaluated, so as to quickly and accurately obtain one or more positive symptoms or a judgment of no obvious symptoms. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 This is a multi-branch tree structure diagram of Example 1 of the present invention, in which the comprehensive scale uses "SCL-90" and the single assessment item scale uses "Yale Brown Obsessive-Compulsive Disorder Severity Scale".

[0040] Figure 2 This is a structural diagram of a psychological test scale combination generation system according to Embodiment 2 of the present invention.

[0041] Figure 3 This is the module composition diagram of each unit of this system. Implementation Example 1

[0042] A method for generating a psychological test scale combination, the method comprising the following steps:

[0043] Step S101, obtain a historical data set of psychological scales, the historical data set of psychological scales includes a comprehensive scale, a single assessment item scale and diagnostic data; classify the historical data set of psychological scales according to the detected symptoms for the comprehensive scale and each single assessment item scale, and generate several symptom labels of the comprehensive scale based on the classification results; each symptom label of the comprehensive scale includes several corresponding items; the comprehensive scale contains several items for psychological testing, and the single assessment item scale contains several items for psychological testing. The number of items corresponding to the corresponding symptom labels in the comprehensive scale is small and does not need to be sorted; the items of the single assessment item scale are specifically used to test a certain symptom; the items of the comprehensive scale and the single assessment item scale are all single-choice;

[0044] First, the diagnostic data in the historical data set of the psychological scale refers to the historical user option selection and the final diagnosis results. Secondly, the comprehensive scale uses the "SCL-90" as an example to classify the symptoms, and classifies it into nine disease labels: somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, terror, paranoia and psychosis. The single-item scale uses the "Yale Brown Obsessive-compulsive Disorder Severity Scale" as an example to classify the symptoms, and classifies it into the obsessive-compulsive symptom.

[0045] Step S102, calculate the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index, and calculate the positive support rate of each item of the single assessment item scale according to the historical data set of the psychological scale as the second-order probability index; the first-order probability index of each symptom label is the ratio of the number of people with positive diagnosis results of the symptom in the set of comprehensive scales to the number of people using the set of comprehensive scales, and the second-order probability index of each item of the single assessment item scale is the ratio of the total number of people who show positive results for the item in the set of single assessment item scales to the number of people using the set of single assessment item scales; the first-order probability index is calculated by the diagnostic data in the historical data set of the psychological scale; the second-order probability index is also calculated by the diagnostic data in the historical data set of the psychological scale. Taking the Yale Brown Obsessive-Compulsive Disorder Severity Scale as an example, the value of its second-order probability index is the ratio of the total number of people who show positive results for the item to the number of people using the set of scales;

[0046] Step S103, arrange the first-order probability indexes in descending order from large to small, and arrange the disease labels corresponding to the first-order probability indexes in the comprehensive scale in descending order to obtain an optimized comprehensive scale, arrange the second-order probability indexes in descending order from large to small, and arrange the items corresponding to the second-order probability indexes in each single evaluation item scale in descending order to obtain corresponding question numbers, and obtain an optimized single evaluation item scale;

[0047] In this embodiment, the comprehensive scale takes "SCL-90" as an example, assuming that the first-order probability indexes of somatization, obsessive-compulsive symptoms, and interpersonal sensitivity are 70%, 84%, and 78% respectively, then the order of the symptoms in the optimized comprehensive scale is obsessive-compulsive symptoms-interpersonal sensitivity-somatization. Next, the single assessment item scale takes "Yale Brown Obsessive-compulsive Disorder Severity Scale" as an example, assuming that the second-order probability indexes of the original questions 1-3 are 75%, 67%, and 83%, then the order of appearance of these three questions in the optimized single assessment item scale is 3-1-2;

[0048] Step S104, matching each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure;

[0049] In this embodiment, it should be emphasized that, taking the obsessive-compulsive symptoms in the comprehensive scale "SCL-90" as an example, its symptom label is obsessive-compulsive symptoms, and matching with the single-assessment item scale "Yale Brown Obsessive-compulsive Disorder Severity Scale" whose symptom label is also obsessive-compulsive symptoms. Secondly, the first-order positive rate is calculated according to the real-time option selection of the user in the comprehensive scale. Taking the obsessive-compulsive symptoms in the comprehensive scale "SCL-90" as an example, if the ratio of the number of moderate, moderately severe or severe positive items to the total number of items of the symptom is greater than or equal to the first-order preset value, the next step of testing is entered, that is, the items of the single-assessment item scale "Yale Brown Obsessive-compulsive Disorder Severity Scale" after optimization are generated;

[0050] Step S105, sequentially generate each item under each symptom label of the optimized comprehensive scale in the multi-branch tree structure according to the arrangement order of each symptom label, calculate the first-order positive rate of the symptom label of the user according to the real-time option selection of the user in the optimized comprehensive scale, if the first-order positive rate is greater than or equal to the first-order preset value, proceed to the next step; if the first-order positive rate is less than the first-order preset value, it indicates that the corresponding symptom label of the user is negative, and determine whether the last symptom label of the optimized comprehensive scale has been reached according to the multi-branch tree structure, if not, proceed to generate the item of the next symptom label, if yes, terminate the item generation directly;

[0051] Step S106, generate and test the items of the optimized single evaluation item scale corresponding to the disease label, calculate the second-order positive rate of the user's disease in real time according to the option selected by the user in the optimized single evaluation item scale, and once the second-order positive rate is greater than or equal to the second-order preset value, end the item generation of the disease and determine that the disease is positive; if after all items of the optimized single evaluation item scale are tested, the calculated second-order positive rate of the user's disease is less than the second-order preset value, then determine that the disease is negative;

[0052] Optimize the single-item scale. Take the optimized Yale Brown Obsessive-Compulsive Severity Scale as an example. The second-order positive rate is the ratio of the number of positive items to the total number of items. A positive item means that the subject has symptoms in this item. Secondly, the second-order positive rate is calculated in real time. Once it is greater than or equal to the preset value, if the optimized Yale Brown Obsessive-Compulsive Severity Scale items are not completed, the item generation of the disease is terminated and the disease is judged to be positive, so as to improve efficiency and test speed;

[0053] Step S107, judging whether the last symptom label of the optimized comprehensive scale has been reached according to the multi-branch tree structure, if not, returning to step S105 to generate the item of the next symptom label; if yes, terminating the item generation.

[0054] The first-order positive rate is the ratio of the number of positive items under the disease label of the user in the optimized comprehensive scale to the number of all items under the disease label, and the first-order preset value is the ratio of the lowest score of positive symptoms to the total score; the second-order positive rate is the ratio of the number of positive items to the total number of items in the optimized single evaluation item scale, and the second-order preset value is the ratio of the lowest score of positive symptoms to the total score. There are multiple options for each item under the corresponding disease label of the optimized comprehensive scale, and each option may be "none, mild, moderate, severe and extreme", and the corresponding scores are "0 points, 1 point, 2 points, 3 points and 4 points". When "moderate, severe and extreme" is selected, the item is positive, then 2 points is the lowest positive score, and the first-order preset value is the ratio of the lowest score of positive symptoms * the number of items and the total score * the number of items, that is, the ratio of the lowest score of positive symptoms and the total score of the single-channel item, and the lowest score and the total score of positive symptoms are both fixed values; for the optimized single evaluation item scale, the lowest score and the total score of positive symptoms are both fixed values.

[0055] This embodiment obtains a historical data set of psychological scales, performs classification processing, and generates multiple symptom labels accordingly; calculates the historical positive rate of comprehensive scale symptoms based on the historical data set of psychological scales to obtain the first-order probability index; calculates the positive support rate of single evaluation item scale items based on the historical data set of psychological scales, that is, the second-order probability index; sorts and matches the comprehensive scale and the single evaluation item scale to construct a multi-branch tree; calculates the first-order positive rate of the user in real time based on the real-time option selection of the user in the comprehensive scale to determine whether the conditions for entering the branch, that is, the single evaluation item scale, are met; calculates the second-order positive rate of the user in real time based on the real-time option selection of the user in the single evaluation item scale to determine whether the user is positive for the symptom. Example 2

[0056] A psychological test scale combination generation system includes a symptom label generation unit, the symptom label generation unit is connected to a probability index generation unit, the probability index generation unit is connected to a sorting optimization unit, the sorting optimization unit is connected to a matching discrimination unit, and the matching discrimination unit is connected to a positive diagnosis unit, wherein:

[0057] The symptom label generating unit is used to obtain a historical data set of psychological scales, which includes a comprehensive scale, a single evaluation item scale and diagnostic data, classify the historical data set of psychological scales according to the comprehensive scale and each single evaluation item scale according to the detected symptoms, and generate a plurality of symptom labels of the comprehensive scale based on the classification results;

[0058] The probability index generating unit is used to calculate the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index, and calculate the positive support rate of each item of the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index; the first-order probability index of each symptom label is the ratio of the number of people with positive diagnosis results of the symptom in the set of comprehensive scales to the number of people using the set of comprehensive scales, and the second-order probability index of each item of the single evaluation item scale is the ratio of the total number of people with positive diagnosis results of the item in the set of single evaluation item scales to the number of people using the set of single evaluation item scales;

[0059] The sorting optimization unit is used to arrange the first-order probability indexes in descending order from large to small according to their values, and arrange the disease labels corresponding to the first-order probability indexes in the comprehensive scale in descending order to obtain an optimized comprehensive scale, arrange the second-order probability indexes in descending order from large to small according to their values, and arrange the items corresponding to the second-order probability indexes in each single evaluation item scale in descending order to obtain corresponding question numbers, and obtain an optimized single evaluation item scale;

[0060] The matching judgment unit is used to match each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure, and sequentially generate each item under each symptom label of the optimized comprehensive scale in the multi-branch tree structure according to the arrangement order of each symptom label, and calculate the first-order positive rate of the user's symptom label according to the user's real-time option selection in the optimized comprehensive scale. If the first-order positive rate is greater than or equal to the first-order preset value, the positive diagnosis unit is entered. If the first-order positive rate is less than the first-order preset value, indicating that the user's corresponding symptom label is negative, the next symptom label item generation is sequentially performed; the first-order positive rate determines whether it meets the conditions for entering the positive diagnosis unit, that is, the symptom needs to be measured on a single evaluation item scale;

[0061] The positive diagnosis unit is used to diagnose whether the disease is positive, generate and test the items of the optimized single evaluation item scale corresponding to the disease label, and calculate the second-order positive rate of the user's disease in real time according to the user's option selection in the optimized single evaluation item scale. Once the second-order positive rate is greater than or equal to the second-order preset value, the project generation of the disease is terminated and the disease is determined to be positive; if after all items of the optimized single evaluation item scale are tested, the calculated second-order positive rate of the user's disease is less than the second-order preset value, the disease is determined to be negative; after determining whether the disease is negative or positive, determine whether it has reached the last disease label of the optimized comprehensive scale according to the multi-branch tree structure. If not, return to the matching judgment unit to generate the next disease label project; if so, terminate the project generation.

[0062] The disease label generating unit comprises:

[0063] The historical data set module is used to obtain historical data sets of psychological scales, including comprehensive scales, single-item scales, and diagnostic data;

[0064] A classification processing module is used to perform comprehensive scale and single assessment item scale symptom classification processing on the psychological scale historical data set to obtain symptom classification results;

[0065] The label generation module generates a plurality of symptom labels of comprehensive scales based on the classification results.

[0066] The probability index generating unit comprises:

[0067] First-order probability index module: used to calculate the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index;

[0068] Second-order probability index module: used to calculate the positive support rate of each item in the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index.

[0069] The sorting optimization unit comprises:

[0070] A first-order sorting module: used to sort the first-order probability indexes in descending order and sort the multiple symptom labels of the comprehensive scale to obtain an optimized comprehensive scale;

[0071] Second-order sorting module: arranges the second-order probability index in descending order, and arranges the items of the single evaluation item scale in descending order to obtain an optimized single evaluation item scale.

[0072] The matching determination unit comprises:

[0073] Matching module: Match each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure;

[0074] Branch discrimination module: according to the real-time option selection of the user in the comprehensive scale, the first-order positive rate of the user's disease is calculated. If the first-order positive rate is greater than or equal to the first-order preset value, the positive diagnosis unit is entered. If the first-order positive rate is less than the first-order preset value, it indicates that the user's corresponding disease label is negative; the first-order positive rate is the ratio of the number of positive items under the disease label of the user in the optimized comprehensive scale to the number of all items under the disease label, and the first-order preset value is the ratio of the lowest score of positive symptoms to the total score;

[0075] Symptom analysis module: If the positive diagnosis unit is not entered, it is determined whether the last symptom label of the optimized comprehensive scale is reached according to the multi-branch tree structure. If not, the project generation of the next symptom label of the optimized comprehensive scale is entered. If yes, the project generation is terminated.

[0076] The second-order positive rate in the positive diagnosis unit is the ratio of the number of positive items in the user's optimized single evaluation item scale to the total number of items, and the second-order preset value is the ratio of the lowest score of positive symptoms to the total score.

[0077] The embodiment of the present invention can perform classification processing according to the acquired historical data set of psychological scales, generate multiple symptom labels accordingly, and then calculate the historical positive rate of the comprehensive scale symptoms according to the historical data set of psychological scales to obtain the first-order probability index, and then calculate the positive support rate of the single evaluation item scale items according to the historical data set of psychological scales, that is, the second-order probability index, and then sort and match the comprehensive scale and the single evaluation item scale to construct a multi-branch tree, and then calculate the first-order positive rate of the user in real time according to the real-time option selection of the user in the comprehensive scale to determine whether the condition for entering the branch, that is, the single evaluation item scale, is met. Finally, calculate the second-order positive rate of the user in real time according to the real-time option selection of the user in the single evaluation item scale to determine whether the user is positive in the symptom, so that the comprehensive psychological scale and the single evaluation item psychological scale can be combined, and a dynamic psychological scale can be generated according to the real-time option selection of the person being evaluated, so as to quickly and accurately obtain one / multiple positive symptoms or a judgment of no obvious symptoms.

[0078] The present invention is not limited to the above-mentioned embodiments. On the basis of the technical solution disclosed in the present invention, technicians in this field can make some substitutions and deformations to some technical features therein according to the disclosed technical content without creative labor, and these substitutions and deformations are all within the protection scope of the present invention.

Claims

1. A method for generating a combination of psychological test scales, characterized in that: The method comprises the following steps: Step S101, obtaining a historical data set of psychological scales, wherein the historical data set of psychological scales includes a comprehensive scale, a single evaluation item scale, and diagnostic data; for the historical data set of psychological scales, classifying the comprehensive scale and each single evaluation item scale according to the detected symptoms, and generating a plurality of symptom labels of the comprehensive scale based on the classification results; Step S102, calculating the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index, and calculating the positive support rate of each item of the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index; the first-order probability index of each symptom label is the ratio of the number of people with positive diagnosis results of the symptom in the set of comprehensive scales to the number of people using the set of comprehensive scales, and the second-order probability index of each item of the single evaluation item scale is the ratio of the total number of people who show positive results for the item in the set of single evaluation item scales to the number of people using the set of single evaluation item scales; Step S103, arrange the first-order probability indexes in descending order from large to small, and arrange the disease labels corresponding to the first-order probability indexes in the comprehensive scale in descending order to obtain an optimized comprehensive scale, arrange the second-order probability indexes in descending order from large to small, and arrange the items corresponding to the second-order probability indexes in each single evaluation item scale in descending order to obtain corresponding question numbers, and obtain an optimized single evaluation item scale; Step S104, matching each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure; Step S105, sequentially generate each item under each symptom label of the optimized comprehensive scale in the multi-branch tree structure according to the arrangement order of each symptom label, calculate the first-order positive rate of the symptom label of the user according to the real-time option selection of the user in the optimized comprehensive scale, if the first-order positive rate is greater than or equal to the first-order preset value, proceed to the next step; if the first-order positive rate is less than the first-order preset value, it indicates that the corresponding symptom label of the user is negative, and determine whether the last symptom label of the optimized comprehensive scale has been reached according to the multi-branch tree structure, if not, proceed to generate the item of the next symptom label, if yes, terminate the item generation directly; Step S106, generate and test the items of the optimized single evaluation item scale corresponding to the disease label, calculate the second-order positive rate of the user's disease in real time according to the option selected by the user in the optimized single evaluation item scale, and once the second-order positive rate is greater than or equal to the second-order preset value, end the item generation of the disease and determine that the disease is positive; if after all items of the optimized single evaluation item scale are tested, the calculated second-order positive rate of the user's disease is less than the second-order preset value, then determine that the disease is negative; Step S107, judging whether the last symptom label of the optimized comprehensive scale has been reached according to the multi-branch tree structure, if not, returning to step S105 to generate the item of the next symptom label; if yes, terminating the item generation.

2. A psychological test scale combination generation method according to claim 1, characterized in that: The comprehensive scale contains several items for psychological testing, and the single assessment item scale contains several items for psychological testing.

3. A psychological test scale combination generation method according to claim 1 or 2, characterized in that: In step S101, each symptom label of the comprehensive scale includes a number of corresponding items.

4. A psychological test scale combination generation method according to claim 1 or 2, characterized in that: The first-order positive rate is the ratio of the number of positive items under the disease label of the user in the optimized comprehensive scale to the number of all items under the disease label, and the first-order preset value is the ratio of the lowest score of positive symptoms to the total score; the second-order positive rate is the ratio of the number of positive items in the optimized single evaluation item scale of the user to the total number of items, and the second-order preset value is the ratio of the lowest score of positive symptoms to the total score.

5. A system for generating a psychological test scale combination according to any one of claims 1 to 4, characterized in that: It includes a symptom label generation unit, which is connected to a probability index generation unit, which is connected to a sorting optimization unit, which is connected to a matching discrimination unit, which is connected to a positive diagnosis unit, wherein: The symptom label generating unit is used to obtain a historical data set of psychological scales, which includes a comprehensive scale, a single evaluation item scale and diagnostic data, classify the comprehensive scale and each single evaluation item scale according to the detected symptoms, and generate a plurality of symptom labels of the comprehensive scale based on the classification results; The probability index generating unit is used to calculate the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index, and calculate the positive support rate of each item of the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index; the first-order probability index of each symptom label is the ratio of the number of people with positive diagnosis results of the symptom in the set of comprehensive scales to the number of people using the set of comprehensive scales, and the second-order probability index of each item of the single evaluation item scale is the ratio of the total number of people with positive diagnosis results of the item in the set of single evaluation item scales to the number of people using the set of single evaluation item scales; The sorting optimization unit is used to arrange the first-order probability indexes in descending order from large to small according to their values, and arrange the disease labels corresponding to the first-order probability indexes in the comprehensive scale in descending order to obtain an optimized comprehensive scale, arrange the second-order probability indexes in descending order from large to small according to their values, and arrange the items corresponding to the second-order probability indexes in each single evaluation item scale in descending order to obtain corresponding question numbers, and obtain an optimized single evaluation item scale; The matching discrimination unit is used to match each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure, and sequentially generate each item under each symptom label of the optimized comprehensive scale in the multi-branch tree structure according to the arrangement order of each symptom label, and calculate the first-order positive rate of the user's symptom label according to the real-time option selection of the user in the optimized comprehensive scale. If the first-order positive rate is greater than or equal to the first-order preset value, the positive diagnosis unit is entered; if the first-order positive rate is less than the first-order preset value, indicating that the corresponding symptom label of the user is negative, the item generation of the next symptom label is sequentially performed; The positive diagnosis unit is used to diagnose whether the disease is positive, generate and test the items of the optimized single evaluation item scale corresponding to the disease label, and calculate the second-order positive rate of the user's disease in real time according to the user's option selection in the optimized single evaluation item scale. Once the second-order positive rate is greater than or equal to the second-order preset value, the project generation of the disease is terminated and the disease is determined to be positive; if after all items of the optimized single evaluation item scale are tested, the calculated second-order positive rate of the user's disease is less than the second-order preset value, the disease is determined to be negative; after determining whether the disease is negative or positive, determine whether it has reached the last disease label of the optimized comprehensive scale according to the multi-branch tree structure. If not, return to the matching judgment unit to generate the next disease label project; if so, terminate the project generation.

6. A generation system for a psychological test scale combination generation method according to claim 5, characterized in that: The disease label generating unit comprises: The historical data set module is used to obtain historical data sets of psychological scales, including comprehensive scales, single-item scales, and diagnostic data; A classification processing module is used to perform comprehensive scale and single assessment item scale symptom classification processing on the psychological scale historical data set to obtain symptom classification results; The label generation module generates a plurality of symptom labels of comprehensive scales based on the classification results.

7. A generation system for a psychological test scale combination generation method according to claim 6, characterized in that: The probability index generating unit comprises: First-order probability index module: used to calculate the historical positive rate of each symptom label of the comprehensive scale according to the historical data set of the psychological scale as the first-order probability index; Second-order probability index module: used to calculate the positive support rate of each item in the single evaluation item scale according to the historical data set of the psychological scale as the second-order probability index.

8. A generation system for a psychological test scale combination generation method according to claim 7, characterized in that: The sorting optimization unit comprises: A first-order sorting module: used to sort the first-order probability indexes in descending order and sort the multiple symptom labels of the comprehensive scale to obtain an optimized comprehensive scale; Second-order sorting module: arrange the second-order probability index in descending order, and arrange the items of the single evaluation item scale in descending order to obtain an optimized single evaluation item scale.

9. A generation system for a psychological test scale combination generation method according to claim 8, characterized in that: The matching determination unit comprises: Matching module: Match each symptom label of the optimized comprehensive scale with each optimized single evaluation item scale to obtain a multi-branch tree structure; Branch discrimination module: according to the real-time option selection of the user in the comprehensive scale, the first-order positive rate of the user's disease is calculated. If the first-order positive rate is greater than or equal to the first-order preset value, the positive diagnosis unit is entered. If the first-order positive rate is less than the first-order preset value, it indicates that the user's corresponding disease label is negative; the first-order positive rate is the ratio of the number of positive items under the disease label of the user in the optimized comprehensive scale to the number of all items under the disease label, and the first-order preset value is the ratio of the lowest score of positive symptoms to the total score; Symptom analysis module: If the positive diagnosis unit is not entered, it is determined whether the last symptom label of the optimized comprehensive scale is reached according to the multi-branch tree structure. If not, the project generation of the next symptom label of the optimized comprehensive scale is entered. If yes, the project generation is terminated.

10. A generation system for a psychological test scale combination generation method according to claim 9, characterized in that: The second-order positive rate in the positive diagnosis unit is the ratio of the number of positive items in the user's optimized single evaluation item scale to the total number of items, and the second-order preset value is the ratio of the lowest score of positive symptoms to the total score.

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