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System for assisting in diagnosing weakness

An auxiliary diagnosis and debilitating technology, applied in the field of diagnostic systems, can solve problems such as bias and misdiagnosis, limitations, and no stable and effective diagnostic methods for biological indicators, and achieve the goal of reducing bias and misdiagnosis, reducing workload, and high sensitivity Effect

Pending Publication Date: 2021-12-10
WEST CHINA HOSPITAL SICHUAN UNIV
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

At present, the diagnostic criteria and assessment strategies for frailty in the world are still limited to physical function. The method of detecting physical function is heavy, it is difficult to promote screening on a large scale, and it relies on the subjective judgment of doctors, which is easy to cause bias and misdiagnosis
Although researchers found that factors such as IL-6 and CRP are related to collective frailty, there are thousands of biomarkers that may be related to frailty, such as inflammatory factors and hormones, and their correlation and accuracy with frailty diagnosis are even higher. Unpredictable, so there is still no stable and effective diagnostic method for biological indicators

Method used

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  • System for assisting in diagnosing weakness

Examples

Experimental program
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Effect test

Embodiment 1

[0038] Embodiment 1, the establishment of the system of the present invention's auxiliary diagnosis of weakness

[0039] 1. Collect physical function measurement indicators and blood samples of healthy and debilitated natural persons. Blood samples are used to measure the levels of specific debilitating immune-inflammatory factors (including CPR, PGRN, IFNγ, IL1β, IL3, IL4, IL6, IL8, IL10, IL11, IL12, IL17, IL22, IL23, C3, C4, Agrin, IgA, GMCSF, IgE, IgG, IgM, BDNF, VEGFR1, TNFα, TGFβ), construct a database (database module).

[0040] 2. The data in the database is divided into training set and test set according to the ratio of 2:1. The training set is analyzed and trained by the random forest algorithm to determine the best parameters of the random forest (training module); the verification set is used to verify the random forest model Accuracy (testing module), the model that achieved the best performance was used to build a frailty diagnosis model (machine learning module)...

experiment example 1

[0051] Experimental Example 1. Importance Analysis of the Levels of Immune Inflammation Factors of the Present Invention

[0052] When inferring the importance of immune inflammatory factors, the impact of immune inflammatory factors on machine learning results was evaluated based on the %IncMSE value (mean square error influence degree) obtained by the random forest model. The %IncMSE values ​​of a large number of immune and inflammatory factors were sorted. The higher the %IncMSE value, the more important the feature and the greater the impact on the results. The results found that CPR, PGRN, IFNγ, IL1β, IL3, IL4, IL6, IL8, IL10, IL11, IL12, IL17, IL22, IL23, C3, C4, Agrin, IgA, GMCSF, IgE, IgG, IgM, BDNF, VEGFR1, The %IncMSE values ​​of TNFα and TGFβ are significantly larger than other immune inflammatory factors, indicating that they participate in the machine learning diagnosis of debilitation in the present invention and have extremely high value and weight. Among them, t...

experiment example 2

[0054] Experimental example 2, the accuracy, specificity and sensitivity analysis of the immune inflammatory factor level of the present invention

[0055] 1. Collection of subjects: 25 frail patients diagnosed according to the clinical criteria for frailty diagnosis, and 99 non-frail normal people. Blood samples were collected and sent for inspection of immune and inflammatory factors: CPR, PGRN, IFNγ, IL1β, IL3, Levels of IL4, IL6, IL8, IL10, IL11, IL12, IL17, IL22, IL23, C3, C4, Agrin, IgA, GMCSF, IgE, IgG, IgM, BDNF, VEGFR1, TNFα, TGFβ.

[0056] The standard diagnostic methods described are as follows:

[0057] (1) Weight loss > 3kg or 5% within 1 year;

[0058] (2) Consciously tired, feeling strenuous or lacking in energy to do anything for more than 3 days in the last week;

[0059] (3) Decreased grip strength (men <23kg, women <14kg);

[0060] (4) Decreased pace: it takes more than 7 seconds to walk 4.5 meters);

[0061] (5) Decreased weekly activity (male <383 kcal...

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Abstract

The invention provides a system for assisting in diagnosing weakness. The system comprises a data acquisition module, a database module, a machine learning module, a training module, a test module and a result output module. Compared with a clinical diagnosis result, an auxiliary diagnosis result of the system is high in specificity and sensitivity, the system can serve as an effective means for large-area popularization and screening of the weakness morbidity rate of people, the workload of clinical doctors for measuring the body function of a detected person is greatly reduced, and bias and misdiagnosis caused by human subjectivity are also reduced. According to the method, a very valuable reference is provided for medical aging health assessment work, and a health screening means is also provided for the general health era of all people.

Description

technical field [0001] The invention belongs to the field of diagnosis systems, and in particular relates to a system for assisting diagnosis of infirmity. Background technique [0002] As populations age and the economic burden of age-related diseases continues to grow, research on aging issues has received unprecedented attention as countries prepare to ensure that social and healthcare systems can meet this challenge. The essence of aging is degeneration, which is manifested as the decline of body structure and function, and the decline of adaptability and resistance, including geriatric syndrome and frailty without obvious clinical lesions. At present, although researchers have made a lot of achievements in aging-related molecular biology, clinical medicine, and epidemiology, the assessment forms of aging are complicated and diverse, and the standards are different, which brings great difficulties to the implementation of intervention measures. [0003] Frailty refers t...

Claims

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Application Information

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IPC IPC(8): G16H50/20G16H50/70G06N20/00
CPCG16H50/20G16H50/70G06N20/00
Inventor 周黎行
Owner WEST CHINA HOSPITAL SICHUAN UNIV
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