CN115101204A - Model, equipment and storage medium for quantitatively evaluating depression risk based on blood biochemical indexes - Google Patents
Model, equipment and storage medium for quantitatively evaluating depression risk based on blood biochemical indexes Download PDFInfo
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Abstract
The invention discloses a model, equipment and a storage medium for quantitatively evaluating depression risk based on blood biochemical indexes, which are used for respectively collecting blood biochemical test indexes and demographic variables of clinical depression patients and healthy people; the biochemical index (such as the biochemical index larger than the Cohen's d set value) and the demographic variable which can distinguish the depression patients and healthy people are used as independent variables, depression groups are used as dependent variables, a logistic regression model of the depression groups is established, depression risk values are calculated through the established logistic regression model of the depression groups, and risk quantitative evaluation is carried out on the depression patients according to the judgment threshold value. The method is beneficial to constructing standardized, objectively measurable depression risk assessment indexes and quantitative models in addition to scale testing and subjective judgment of doctors, assists the doctors in providing more accurate and objective depression assessment for patients, and overcomes the limitation of only using subjective assessment and expert assessment in the existing method.
Description
Technical Field
The invention relates to the technical field of depression, in particular to a model, equipment and a storage medium for quantitatively evaluating depression risk based on blood biochemical indexes.
Background
Depression is the most common type of serious mental illness worldwide with a lifetime prevalence of up to 10-20%, and is currently the fourth of all disease ranks in terms of socio-economic burden, and is projected to rise to the second by 2020 (Holden, 2000). Clinical pathology still is the diagnosis basis of depression so far, but because clinical symptoms of depression are complex and various and lack of corresponding objective indexes, the diagnosis and differential diagnosis of diseases depend on the subjective experience and conjecture of psychiatrists to a great extent, and the lack of objective scientific clinical diagnosis basis depends on the subjective judgment of doctors.
The diagnosis of depression relies mainly on clinical phenomenology and lacks the use of biochemical parameters. Therefore, the current predicament is that the diagnosis consistency in ten years of Major Depressive Disorder (MDD) is only 45.5%, while the long-term consistency rate in the seven-year cohort study of Bipolar Disorder (BD) is also only 71.9%. Clinical applications, transformations are still limited due to the strong subjectivity during clinical assessment or the complexity of the specific follow-up experimental design involved.
Disclosure of Invention
In order to solve the limitation of only using subjective evaluation and expert evaluation in the existing method, the invention provides a model, equipment and a storage medium for quantitatively evaluating the depression risk based on blood biochemical indexes, and provides more objective and accurate depression risk evaluation for doctors or experts.
The invention adopts the following technical scheme:
on one hand, the invention provides a model for quantitatively evaluating the depression risk based on blood biochemical indexes, which respectively collects the blood biochemical test indexes and the demographic variables of clinical depression patients and healthy people; taking biochemical indexes and demographic variables capable of distinguishing depression patients and healthy people as independent variables, taking depression groups as dependent variables, and establishing logistic regression models and judgment thresholds of the depression groups; calculating a depression risk value by the established logistic regression model of the depression group; and (5) performing quantitative risk assessment on the depression patients according to the judgment threshold value.
Preferably, when the biochemical indexes capable of distinguishing the depression patients from the healthy people are selected, the biochemical indexes of the depression patients and the healthy people which are larger than the set value of the effector Cohen's d are selected by setting the value of the effector Cohen's d.
Further preferably, the independent variable parameters in the established grouping regression model of depression include: adrenocorticotropic hormone, brain-derived neurotrophic factor, interleukin 18(IL-18), progesterone, serotonin biochemical test indexes, sex and age;
a logistic regression model for the depression group was established as follows:
y ═ 1/(1+ exp ((-2.263 × adrenocorticotropic hormone-1.113 brain-derived neurotrophic factor-0.13 × interleukin 18+0.747 × progesterone-0.451 × serotonin +0.894 × sex +0.002 × age +21.221 ×) fasting or not before taking antidepressant or antimanic drugs +20.498 + 2.441))).
Among the above parameters, gender: 1 for female, 0 for male; whether to take medicine or not: taking 1 but not 0; whether fasting before blood drawing: the meal before blood drawing is 1, and the empty stomach before blood drawing is 0.
Or further preferably, establishing the independent variable parameters in the logistic regression model of the depression group may further comprise: c-reactive protein (CRP), cortisol, testosterone, and tumor necrosis factor alpha (TNF-alpha);
the logistic regression model for establishing a group of depression can also be expressed using the following mathematical formula: y ═ 1/(1+ exp ((-3.632 × adrenocorticotropic hormone-1.1 × brain-derived neurotrophic factor-0.117 × interleukin 18+0.835 × progesterone-0.935 × serotonin +0.671 × C-reactive protein +0.38 × cortisol +0.283 × testosterone-0.006 tumor necrosis factor α +0.834 × sex-0.01 × age +20.137 × whether to take antidepressants or antimanic drugs +19.641 × whether to empty stomach-2.124 before drawing blood))).
Among the above parameters, gender: 1 for female, 0 for male; whether to take medicine or not: taking is 1, not taking is 0; whether fasting before blood drawing: the meal before blood drawing is 1, and the empty stomach before blood drawing is 0.
Further, the collected biochemical test indexes of clinical depression patients and healthy people can also comprise: corticosterone (adrenalone), thyroxine, gamma aminobutyric acid, dopamine, oxytocin, cortisone, neuropeptide Y, endocannabinoid, mature brain-derived neurotrophic factor, norepinephrine NE, interleukin 10(IL-10), interleukin 12, interleukin 4, nerve growth factor and 5-hydroxytryptamine, a plurality of biochemical indexes capable of distinguishing depression patients and healthy people are selected as independent variable parameters, and a logistic regression model and a judgment threshold value of depression groups are established.
The demographic variables also included marital, child, education, occupation, income, and presence or absence of religious beliefs for the clinical depression patients and healthy population collected.
In another aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores at least one program that is loaded and executed by the processor to implement the model for quantitatively evaluating depression risk based on blood biochemical indicators.
In another aspect, the present invention further provides a computer readable storage medium, wherein at least one program is stored in the storage medium and loaded into and executed by a processor to implement the model for quantitatively evaluating the depression risk based on blood biochemical indicators.
In another aspect, the present invention also provides a computer program product comprising computer instructions which, when executed by a processor, implement the model for quantitatively evaluating a risk of depression based on blood biochemical indicators.
The technical scheme of the invention has the following advantages:
A. the quantitative evaluation model for the depression risk based on the blood biochemical test indexes and the demographic variables is beneficial to constructing a standardized and objectively measurable depression risk evaluation index and a quantitative model in addition to scale test and subjective judgment of a doctor, assists the doctor in providing more accurate and objective depression evaluation for a patient, and overcomes the limitation of only using subjective evaluation and expert evaluation in the existing method.
B. The method establishes the depression grouping model after carrying out logistic regression on a plurality of selected blood biochemical indexes, can establish a plurality of different models according to the number and the types of the selected biochemical indexes, respectively inputs the tested blood biochemical test indexes and parameters such as gender, age and the like to quickly obtain a calculated value, can quickly judge the incidence risk probability of depression through the obtained value, and is favorable for quickly screening the depression of blood test crowds.
C. Since depression is a symptom cluster with multiple complex causes, including multiple subtypes, and even the generation mechanism thereof may be completely different, the required symptomatic drugs are completely different, and the current medical practice is to try different drugs to observe the curative effect. The method provided by the invention is used for establishing a discrimination model based on blood biochemical test indexes, which is helpful for accurately distinguishing the subtype of depression and can help doctors to select symptomatic medicines more quickly and accurately.
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In order to more clearly illustrate the embodiments of the invention, the drawings that are required for the embodiments will be briefly described below, it being apparent that the drawings in the following description are some embodiments of the invention, and that other drawings may be derived from those drawings without inventive effort by a person skilled in the art.
Fig. 1 is a method for establishing a depression risk model provided by the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a model for quantitatively evaluating depression risk based on blood biochemical indexes, which is used for respectively collecting blood biochemical test indexes and demographic variables of clinical depression patients and healthy people; selecting biochemical indexes and demographic variables with obvious differences of depression patients and healthy people as independent variables, preferably selecting the effective amount Cohen's d more than 0.3, and certainly searching the biochemical indexes with obvious differences by adopting other statistical means, and formulating different selection standards according to sample differences; establishing a logistic regression model of the depression groups by taking the depression groups as dependent variables, setting a judgment threshold value, calculating a depression risk value through the established depression group regression model, and quantitatively evaluating the risk of the depression patient by combining the judgment threshold value.
The biochemical index independent variables established by the invention comprise key biochemical indexes and demographic variables such as serotonin, brain-derived neurotrophic factor, Nerve Growth Factor (NGF), adrenocortical hormone, C-reactive protein (CRP), interleukin 18(IL-18), tumor necrosis factor alpha (TNF-alpha), cortisol, progesterone, testosterone and the like, and the risk quantitative evaluation of the depression is carried out, and the specifically established depression group model is as follows:
a logistic regression model for the depression group was established as follows:
y ═ 1/(1+ exp ((-2.263 × adrenocorticotropic hormone-1.113 × -brain-derived neurotrophic factor-0.13 × interleukin 18+0.747 × progesterone-0.451 × serotonin +0.894 × + gender +0.002 × -age +21.221 × -fasting before taking antidepressant or antimanic +20.498 × -2.441))).
In the above formula, gender: 1 for female, 0 for male; whether to take medicine or not: taking 1 but not 0; whether fasting before blood drawing: meal before blood drawing is 1, empty stomach before blood drawing is 0. On the premise that the judgment threshold is 0.5, the model hit rate is 0.714, the correct rejection rate is 0.912, and the comprehensive accuracy rate is 0.858.
Of course, when a logistic regression model of the depression group is established, the following independent variable parameters can be included in the model: c-reactive protein (CRP), cortisol, testosterone, and tumor necrosis factor alpha (TNF- α) to make the model more accurate for the assessment of depression, so the logistic regression model for the grouping of depression established for the above independent variable parameters can also be expressed using the following mathematical formula:
y ═ 1/(1+ exp ((-3.632 × adrenocorticotropic hormone-1.1 × brain-derived neurotrophic factor-0.117 × interleukin 18+0.835 × progesterone-0.935 × serotonin +0.671 × C-reactive protein +0.38 × cortisol +0.283 × testosterone-0.006 tumor necrosis factor α +0.834 × sex-0.01 × age +20.137 × whether to take antidepressants or antimanic drugs +19.641 × whether to empty stomach-2.124 before drawing blood))).
In the above formula, gender: 1 for female, 0 for male; whether to take medicine or not: taking is 1, not taking is 0; whether or not fasting before blood drawing: the meal before blood drawing is 1, and the empty stomach before blood drawing is 0.
On the premise that the judgment threshold is 0.5, the model hit rate of the model is 0.781, the correct rejection rate is 0.929, and the comprehensive accuracy rate is 0.875.
The set threshold value range is between 0 and 1, the threshold value range is selected according to the proportion of the depression in the tested sample, when the proportion of the depression patients in the overall sample is less than 50%, the judgment threshold value range is set to be greater than or equal to 0.5, when the proportion of the depression patients in the overall sample is greater than 50%, the judgment threshold value range is set to be less than or equal to 0.5, namely when the proportion of the depression patients in the overall sample is increased, the judgment threshold value range is correspondingly decreased.
Acquiring a biochemical test index value, substituting the acquired value into one of the logistic regression models of the depression groups to acquire a depression risk value of the patient, and when the acquired value is greater than a set judgment threshold value, considering that the depression risk of the patient is greater, otherwise, the depression risk is smaller. The objective index is calculated by the logistic regression model of the depression group, so that the method is beneficial to constructing a standardized and objectively measurable depression risk evaluation index and a quantitative model outside a scale test, and overcomes the limitation of only using subjective evaluation and expert evaluation in the existing method.
During modeling, in addition to the biochemical test indexes of clinical depression patients and healthy people, biochemical test indexes of adrenalin, corticosterone (adrenalone), thyroxine, oxytocin, neuropeptide Y, endocannabinoids, forebrain-derived neurotrophic factors, mature brain-derived neurotrophic factors, norepinephrine NE, interleukin 10(IL-10), interleukin 12, interleukin 4, gamma aminobutyric acid, dopamine and the like can be additionally acquired, the biological indexes can be selected according to set selection requirements, the selected biological indexes are obviously different indexes of the healthy people and depression disorder patients, and the corresponding depression risk model is established according to the selected biochemical indexes and demographic variables serving as independent variables. The demographic variables may also include the independent variables of marital, child, education, occupation, income and presence or absence of religious beliefs of patients with clinical depression and healthy people.
Of course, the more independent variable parameters that are involved in the established logistic regression model, the more accurate the assessment of patients with depression, such as all the biochemical indicators listed above are used to establish the model. The method can select different biochemical indexes to form a discrimination function according to the biochemical indexes, the accessibility of population variables and the test cost requirement, and is also the fastest mode for modeling and evaluating the depression risk.
The present invention also provides a computer readable storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by the processor to run the above model and automatically calculate a depression assessment value Y.
The invention also provides a computer program product which, when run on an electronic device, causes the electronic device to perform the model building and model calculation described above.
The present invention can store the above-mentioned model in a computer-readable storage medium, and the above-mentioned storage medium can be read-only memory, magnetic disk or optical disk, etc.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are intended to be within the scope of the invention.
Claims (9)
1. A model for quantitatively evaluating the depression risk based on blood biochemical indexes is characterized in that blood biochemical test indexes and demographic variables of clinical depression patients and healthy people are respectively collected; taking biochemical indexes and demographic variables capable of distinguishing depression patients and healthy people as independent variables, taking depression groups as dependent variables, and establishing a logistic regression model and a judgment threshold value of the depression groups; calculating a depression risk value by the established logistic regression model of the depression group; and (5) performing quantitative risk assessment on the depression patients according to the judgment threshold value.
2. The model for quantitatively evaluating the risk of depression based on biochemical indicators of blood as claimed in claim 1, wherein when the biochemical indicators capable of distinguishing patients with depression from healthy people are selected, the biochemical indicators of patients with depression and healthy people are selected by setting the value of Cohen's d, and the biochemical indicators of patients with depression and healthy people are selected to be larger than the set value of Cohen's d.
3. The model for quantitatively evaluating the risk of depression based on blood biochemical indicators as set forth in claim 2, wherein the independent variable parameters in the logistic regression model establishing the depression group include: adrenocorticotropic hormone, brain-derived neurotrophic factor, interleukin 18(IL-18), progesterone, serotonin biochemical test indexes, sex and age;
a logistic regression model for the depression group was established as follows:
y ═ 1/(1+ exp ((-2.263 × adrenocorticotropic hormone-1.113 × -brain-derived neurotrophic factor-0.13 × interleukin 18+0.747 × progesterone-0.451 × serotonin +0.894 × + gender +0.002 × -age +21.221 × -fasting before taking antidepressant or antimanic +20.498 × -2.441))).
4. The model for quantitatively evaluating the risk of depression based on blood biochemical indicators as recited in claim 3, wherein the establishing of the independent variable parameters in the logistic regression model for the grouping of depression further comprises: c-reactive protein (CRP), cortisol, testosterone, and tumor necrosis factor alpha (TNF-alpha);
the logistic regression model for establishing a group of depression can also be expressed using the following mathematical formula: y ═ 1/(1+ exp ((-3.632 × adrenocorticotropic hormone-1.1 × brain-derived neurotrophic factor-0.117 × interleukin 18+0.835 × progesterone-0.935 × serotonin +0.671 × C-reactive protein +0.38 × cortisol +0.283 × testosterone-0.006 tumor necrosis factor α +0.834 × sex-0.01 × age +20.137 × whether to take antidepressants or antimanic drugs +19.641 × whether to empty stomach-2.124 before drawing blood))).
5. The model for quantitatively evaluating the risk of depression according to the biochemical blood index as claimed in claim 4, wherein the collected biochemical test indexes of clinical depression patients and healthy people can further comprise: corticosterone (adrenalone), thyroxine, gamma aminobutyric acid, dopamine, oxytocin, cortisone, neuropeptide Y, endocannabinoid, mature brain-derived neurotrophic factor, norepinephrine NE, interleukin 10(IL-10), interleukin 12, interleukin 4, nerve growth factor and 5-hydroxytryptamine, a plurality of biochemical indexes capable of distinguishing depression patients and healthy people are selected as independent variable parameters, and a logistic regression model and a judgment threshold value of depression groups are established.
6. The model for quantitative assessment of depression risk based on blood biochemical indicators as claimed in claim 5, wherein the demographic variables further include marital, child, education, occupation, income and presence or absence of religious beliefs of the collected clinical depression patients and healthy population.
7. An electronic device comprising a processor and a memory, wherein the memory stores at least one program that is loaded and executed by the processor to implement the model for quantitative assessment of depression risk based on blood biochemical indicators as claimed in any one of claims 1 to 6.
8. A computer readable storage medium having stored thereon at least one program which is loaded into and executed by a processor to implement the model for quantitative assessment of depression risk based on blood biochemical indicators according to any one of claims 1 to 6.
9. A computer program product comprising computer instructions which, when executed by a processor, implement the model for quantitatively evaluating the risk of depression based on blood biochemical indicators as claimed in any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052877A (en) * | 2022-12-19 | 2023-05-02 | 李珊珊 | Diabetes patient depression risk assessment method and assessment system construction method |
| CN117219262A (en) * | 2023-09-13 | 2023-12-12 | 内蒙古卫数数据科技有限公司 | Depression degree distinguishing method based on blood routine biochemical data |
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2022
- 2022-06-22 CN CN202210713709.0A patent/CN115101204A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116052877A (en) * | 2022-12-19 | 2023-05-02 | 李珊珊 | Diabetes patient depression risk assessment method and assessment system construction method |
| CN117219262A (en) * | 2023-09-13 | 2023-12-12 | 内蒙古卫数数据科技有限公司 | Depression degree distinguishing method based on blood routine biochemical data |
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