Chronic disease assessment method and assessment system

Abstract

The application discloses a chronic disease evaluation method and an evaluation system, and comprises the following steps: obtaining the kth detection data of a patient to construct a comprehensive feature data set; when k is 1, calculating the disease risk value corresponding to different chronic diseases in a chronic disease risk model according to each physiological parameter in the comprehensive feature data set of the present detection, and outputting the disease risk value and intervention suggestions; when k is greater than 1, in the prognosis evaluation step, calculating a parameter change value according to each physiological parameter in the comprehensive feature data set of the present detection, and calculating the disease risk value corresponding to different chronic diseases in the chronic disease risk model according to each physiological parameter in the comprehensive feature data set of the present detection and the parameter change value, and outputting the disease risk value and intervention suggestions. The application performs long-period supervision and evaluation on the chronic disease risk of the patient, and dynamically optimizes the effectiveness of intervention.

Description

Technical Field

[0001] This invention relates to the field of health risk assessment technology, and in particular to a method and system for assessing chronic diseases. Background Technology

[0002] Chronic non-communicable diseases (NCDs) are characterized by long disease cycles, insidious onset, gradual progression, easy occurrence of complications, and high rates of disability and mortality. Early risk screening, periodic disease assessment, and personalized intervention guidance are key means to slow the progression of chronic diseases and reduce the risk of developing them. With the rapid development of testing technologies and smart healthcare, intelligent risk assessment based on various physiological test data has become the mainstream method to replace traditional manual experience-based judgment.

[0003] Currently, most existing chronic disease assessment technologies adopt a single, static assessment model. They rely solely on physiological parameter data obtained from a single physical examination or test, using pre-set models to calculate the risk of various chronic diseases and output fixed health intervention recommendations based on the results of a single assessment. However, existing technologies have significant technical limitations in practical application, failing to meet the dynamic and full-cycle assessment and management needs of chronic diseases. Relying solely on single static physiological parameters for risk calculation ignores the core characteristic of the gradual progression of chronic diseases and cannot evaluate the effectiveness of interventions, severely restricting the effectiveness of long-term monitoring and management of patients with chronic diseases. Summary of the Invention

[0004] This invention aims to address at least one of the technical problems existing in the prior art. To this end, this invention proposes a chronic disease assessment method and system to conduct long-term monitoring and assessment of patients' chronic disease risks, dynamically optimizing the effectiveness of interventions.

[0005] According to a first aspect of the present invention, a chronic disease assessment method includes: acquiring a patient's k-th test data, wherein the test data includes multiple physiological parameters, and constructing a comprehensive feature dataset of the patient's k-th test based on the various physiological parameters; when k is 1, performing an initial assessment step, in which the risk values ​​of different chronic diseases are calculated in a chronic disease risk model based on the various physiological parameters in the comprehensive feature dataset of the current test, and intervention recommendations are derived based on the risk values, and the risk values ​​and intervention recommendations are output; when k is greater than 1, performing a prognostic assessment step, in which parameter change values ​​are calculated based on the physiological parameters in the comprehensive feature dataset of the current test and the physiological parameters in the comprehensive feature dataset of the initial test, and the risk values ​​of different chronic diseases are calculated in a chronic disease risk model based on the various physiological parameters and parameter change values ​​in the comprehensive feature dataset of the current test, and the intervention recommendations output in the initial test are corrected based on the parameter change values, and the risk values ​​and intervention recommendations are output.

[0006] The chronic disease assessment method according to embodiments of the present invention has at least the following beneficial effects: This invention provides a chronic disease assessment method. Upon acquiring a patient's initial test data, an initial assessment step is performed. Physiological parameters from the comprehensive feature dataset of the initial test are used to derive the risk value for different chronic diseases. Preliminary intervention suggestions are provided under physician guidance or intelligently. Patients can then follow these suggestions for physical therapy. In subsequent tests, a prognostic assessment step is performed. Parameter changes are calculated based on the corresponding physiological parameters in the current and initial comprehensive feature datasets. These changes determine the effectiveness of the intervention suggestions from the initial test. The chronic disease risk value is also derived from the physiological parameters and parameter changes in the current comprehensive feature dataset. For test data showing improved parameter changes, the risk value can be reduced accordingly, and the physician or system can continue to provide the previous intervention suggestions. Conversely, for test data showing worsened parameter changes, the risk value can be increased accordingly, drawing the attention of both the physician and the patient. The physician or system can then adjust the previous intervention suggestions accordingly. This application provides long-term monitoring and assessment of the patient's chronic disease risk, dynamically optimizing the effectiveness of interventions.

[0007] According to some embodiments of the present invention, after obtaining the patient's k-th test data, the method includes: normalizing the actual values ​​of various physiological parameters in the test data to obtain the normalized characteristic values ​​of the corresponding physiological parameters; constructing a comprehensive feature dataset corresponding to the patient's k-th test using the normalized characteristic values ​​of the physiological parameters; and calculating the risk values ​​of different chronic diseases in the chronic disease risk model based on the various physiological parameters in the comprehensive feature dataset based on the current test, and calculating the risk values ​​of different chronic diseases based on the various physiological parameters and parameter change values ​​in the chronic disease risk model based on the comprehensive feature dataset based on the current test, using the normalized characteristic values ​​of the physiological parameters to calculate the risk values ​​of different chronic diseases.

[0008] According to some embodiments of the present invention, the chronic disease risk model has risk scoring rules corresponding to different chronic diseases, and each risk scoring rule has a risk assessment value associated with at least one physiological parameter; in the chronic disease risk model, the disease risk value is calculated based on the risk assessment value and the comprehensive assessment value of the corresponding physiological parameter, wherein the comprehensive assessment value is derived from at least the actual value or normalized characteristic value of the corresponding physiological parameter.

[0009] According to some embodiments of the present invention, the disease risk value is: ; in, chronic diseases The risk value of the disease, For the first Risk assessment values ​​for several physiological parameters For the first A comprehensive evaluation value of several physiological parameters.

[0010] According to some embodiments of the present invention, in the initial evaluation step, the comprehensive evaluation value is the actual value or normalized characteristic value of the physiological parameter corresponding to this test; in the prognostic evaluation step, the comprehensive evaluation value is calculated by weighting the parameter change value of this test and the actual value or normalized characteristic value of the physiological parameter corresponding to this test.

[0011] According to some embodiments of the present invention, in the prognostic assessment step, the comprehensive evaluation value is calculated by weighting the trend score, the parameter change value of the current test, and the actual value or normalized characteristic value of the physiological parameter corresponding to the current test. The trend score is calculated by fusing the change in physiological parameters of each test data in multiple tests with the change in physiological parameters of the previous test data.

[0012] According to some embodiments of the present invention, the trend score value is: ; ; in, For the first The score for the trend of change of the physiological parameters For the first The data from the second test and the first The first detection data The change in a physiological parameter For the first Fusion coefficient of the second detection. As the baseline value for the fusion coefficient, Let be the fusion constant. This represents the total number of tests.

[0013] According to some embodiments of the present invention, the detection data includes fingertip video data, facial video data, micro-expression data, and patient personal data, wherein the patient personal data includes one or more combinations of gender, age, height, and weight, and various physiological parameters are derived from the fingertip video data, facial video data, micro-expression data, and patient personal data.

[0014] The assessment system according to a second aspect of the present invention is used to perform the chronic disease assessment method disclosed in any of the above embodiments.

[0015] The evaluation system according to embodiments of the present invention has at least the following beneficial effects: The assessment system of this invention performs the chronic disease assessment method disclosed in any of the above embodiments to conduct long-term monitoring and assessment of the patient's chronic disease risk and dynamically optimize the effectiveness of interventions.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which: Figure 1 This is a flowchart of one embodiment of the chronic disease assessment method of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0020] In the description of this invention, "several" means one or more, "multiple" means two or more, "greater than", "less than", "exceeding" are understood to exclude the number itself, and "above", "below", "within" are understood to include the number itself.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] like Figure 1 As shown, the chronic disease assessment method according to a first aspect embodiment of the present invention includes: S110. Obtain the patient's k-th test data, which includes various physiological parameters. Construct a comprehensive feature dataset of the patient's k-th test based on these physiological parameters. S120. When k is 1, the first assessment step is executed. In the first assessment step, the risk values ​​of different chronic diseases are calculated in the chronic disease risk model based on the physiological parameters in the comprehensive feature dataset of this test. Intervention suggestions are given based on the risk values, and the risk values ​​and intervention suggestions are output. S130. When k is greater than 1, the prognostic assessment step is executed. In the prognostic assessment step, the parameter change value is calculated based on the physiological parameters in the comprehensive feature dataset of this test and the physiological parameters in the comprehensive feature dataset of the first test. Based on the physiological parameters and parameter change values ​​in the comprehensive feature dataset of this test, the risk value of the corresponding chronic disease is calculated in the chronic disease risk model. After correcting the intervention recommendation output in the first test based on the parameter change value, the risk value and intervention recommendation are output.

[0023] In some embodiments of the present invention, the detection data includes fingertip video data, facial video data, micro-expression data, and patient personal data, wherein the patient personal data includes one or more combinations of gender, age, height, and weight, and various physiological parameters are derived from the fingertip video data, facial video data, micro-expression data, and patient personal data.

[0024] The system can acquire fingertip video data (PPG signal) using a fingertip camera photoplethysmography (PPG) scanner, and facial video data (rPPG signal) using a conventional camera or other remote photoplethysmography (RPG) scanner. Patient data includes gender, age, height, weight, medical history, medication use, and lifestyle habits. In addition to collecting patient data, environmental parameters such as light intensity, shooting distance, head posture angle, occlusion, and acquisition time can also be monitored. Based on these environmental parameters, the system can perform noise reduction, filtering, and baseline drift correction on the fingertip video data, facial video data, and micro-expression data, removing abnormal frames caused by sudden changes in lighting, severe occlusion, or strenuous movement. Furthermore, it can prompt patients to re-acquire data of poor quality.

[0025] In the first test data obtained from the initial test, various physiological parameters can be compared with the standard parameter range of normal people, the degree of deviation of each indicator can be calculated, and an individualized first health baseline for the patient can be generated to determine whether any physiological parameter of the patient exceeds the standard. At the same time, the risk value of chronic diseases can be calculated based on the physiological parameters.

[0026] For example, for the first Physiological parameters of the item, This is the patient's first test result. This refers to the normal standard range for this physiological parameter. This represents the deviation of this physiological parameter.

[0027] The deviation of the first detection can then be obtained: ; when When within the normal range, Recorded as low deviation or zero deviation; when When the value is higher or lower than the normal range, it depends on the deviation. It can determine the degree of abnormality in the patient's physiological parameters.

[0028] For example, the normal range of systolic blood pressure is 90-120 mmHg; the patient's first measured systolic blood pressure was 150 mmHg; the system recorded that the patient's systolic blood pressure deviated significantly from the upper limit of normal, and used 150 mmHg as the patient's individual baseline value before intervention.

[0029] In the process of providing intervention recommendations based on disease risk values, different chronic diseases can have multiple intervention recommendations. The disease risk value system can match the corresponding level of intervention recommendations, or physicians can formulate intervention recommendations. In the subsequent prognostic assessment steps, the intervention recommendations can be modified based on changes in parameter values. For example, if the patient's condition improves, the intervention recommendations can be maintained or downgraded; if the patient's condition worsens, the intervention recommendations can be upgraded.

[0030] This invention provides a chronic disease assessment method. Upon acquiring a patient's initial test data, an initial assessment step is performed. Physiological parameters from the comprehensive feature dataset of the initial test are used to derive the risk value for different chronic diseases. Preliminary intervention suggestions are provided under physician guidance or intelligently. Patients can then follow these suggestions for physical therapy. In subsequent tests, a prognostic assessment step is performed. Parameter changes are calculated based on the corresponding physiological parameters in the current and initial comprehensive feature datasets. These changes determine the effectiveness of the intervention suggestions from the initial test. The chronic disease risk value is also derived from the physiological parameters and parameter changes in the current comprehensive feature dataset. For test data showing improved parameter changes, the risk value can be reduced accordingly, and the physician or system can continue to provide the previous intervention suggestions. Conversely, for test data showing worsened parameter changes, the risk value can be increased accordingly, drawing the attention of both the physician and the patient. The physician or system can then adjust the previous intervention suggestions accordingly. This application provides long-term monitoring and assessment of the patient's chronic disease risk, dynamically optimizing the effectiveness of interventions.

[0031] Because the test data contains multiple physiological parameters, and the numerical standards for different physiological parameters are different, in some embodiments of the present invention, after obtaining the patient's k-th test data, the following steps are included: The actual values ​​of each physiological parameter in the test data are normalized to obtain the corresponding normalized feature values ​​for each physiological parameter. For example, the values ​​are all converted to the range of [0, 1]. A comprehensive feature dataset corresponding to the k-th test of the patient is constructed using the normalized feature values ​​of each physiological parameter. In the comprehensive feature dataset, the element features composed of physiological parameters can be classified. ,in This indicates the element category of the fingertip video data. Elements representing the categories of facial video data The element category representing micro-expression data, This represents the element category of a patient's personal data.

[0032] In the calculation of the risk values ​​of different chronic diseases in the chronic disease risk model based on the various physiological parameters in the comprehensive feature dataset of this test, and in the calculation of the risk values ​​of different chronic diseases based on the various physiological parameters and parameter changes in the comprehensive feature dataset of this test, the normalized characteristic values ​​of various physiological parameters are used to calculate the risk values ​​of different chronic diseases.

[0033] In some embodiments of the present invention, the chronic disease risk model has risk scoring rules corresponding to different chronic diseases, and each risk scoring rule has a risk assessment value associated with at least one physiological parameter; the disease risk value is calculated in the chronic disease risk model based on the risk assessment value and the comprehensive assessment value of the corresponding physiological parameter, wherein the comprehensive assessment value is derived from at least the actual value or normalized characteristic value of the corresponding physiological parameter.

[0034] Different chronic diseases are associated with at least one physiological parameter. For example, hypertension is associated with physiological parameters such as blood lipids, facial pressure, exercise habits, and sleep habits. Different physiological parameters have different risks of causing hypertension, and therefore have different risk assessment values. By using the comprehensive assessment value of each associated physiological parameter and its corresponding risk assessment value, the disease risk value can be obtained. Since the proposed solution in this application considers the correction of the disease risk value based on the changing trend of physiological parameters under intervention conditions, the comprehensive assessment value can directly use the actual value or normalized characteristic value of the physiological parameter, or it can be corrected before being used to calculate the disease risk value.

[0035] Specifically, the disease risk value is: ; in, chronic diseases The risk value of the disease, For the first Risk assessment values ​​for several physiological parameters For the first A comprehensive evaluation value of several physiological parameters.

[0036] The risk value of a disease is obtained by multiplying and combining the comprehensive assessment values ​​of various related physiological parameters with their respective risk assessment values.

[0037] In the initial evaluation step, the comprehensive evaluation value is the actual value or normalized characteristic value of the physiological parameter corresponding to this test; In the prognostic assessment step, the comprehensive evaluation value is calculated by weighting the parameter change value of this test and the actual value or normalized characteristic value of the corresponding physiological parameter of this test.

[0038] In this process, the patient undergoes physical therapy according to the intervention recommendations, and the system acquires test data during the second or subsequent tests, comparing the physiological parameters of the current test with those of the first test.

[0039] Specifically, the parameter changes in this detection (the kth time) are as follows: ; in, For the kth time Specifically, in the subsequent calculation of comprehensive evaluation values, the changes in these physiological parameters can also be normalized.

[0040] In the prognostic assessment step, the comprehensive evaluation value ,in, These are the weighted values ​​of the physiological parameters measured in this test. The weighted average of the parameter variation values. and The value can be set by staff according to actual needs, for example... It is 0.7. It is 0.3.

[0041] Understandably, in the second or subsequent tests, when the direction of change of a certain abnormal physiological parameter moves closer to the normal range, the system determines that the indicator has an improving trend, proving that effective intervention has been achieved and the risk value of the disease can be further reduced. When the direction of change moves away from the normal range, the system determines that there is a deteriorating trend.

[0042] For example, if a patient's systolic blood pressure is 140 mmHg in the second test, which is still higher than the normal range, but is 10 mmHg lower than the first test, then the indicator can be judged as abnormal but improved. This can reduce the short-term risk of disease during risk assessment and suggest continued intervention.

[0043] If the patient's third test result is 130 mmHg, it is determined that the patient is showing a continuous improvement trend. However, if the third test result rises to 155 mmHg, the system determines that the intervention effect is unstable or there is a risk of deterioration.

[0044] Based on the above, in some embodiments of the present invention, it is also necessary to consider the changing trend of physiological parameters during multiple tests. In the prognostic evaluation step, the comprehensive evaluation value is calculated by weighting the changing trend score, the parameter change value of the current test, and the actual value or normalized feature value of the physiological parameter corresponding to the current test. The changing trend score is calculated by integrating the physiological parameters of each test data and the physiological parameters of the previous test data in multiple tests.

[0045] Specifically, the trend of change score is: ; ; in, For the first The score for the trend of change of the physiological parameters For the first The data from the second test and the first The first detection data The change in a physiological parameter For the first Fusion coefficient of the second detection. As the baseline value for the fusion coefficient, Let be the fusion constant. This represents the total number of tests.

[0046] Understandably, in multiple tests, the changes in physiological parameters between two adjacent tests are considered, and the fusion coefficient is also taken into account. As can be seen from the calculation formula, the change in the most recently detected physiological parameter has a higher proportion in the weighted fusion calculation of the trend score, and the fusion coefficient baseline value is higher. and fusion constant It can be adjusted by staff according to the actual situation.

[0047] Taking four measurements as an example, the systolic blood pressure in the first measurement was 150 mmHg, the second was 140 mmHg, the third was 155 mmHg, and the fourth was 135 mmHg. According to... The formula for the fusion coefficient of the fourth detection. The fusion coefficient can be 0.8, representing the third detection. The fusion coefficient can be 0.5, which is the value of the second detection. It can be 0.3, from which we can obtain This is equivalent to an improvement. mmHg, can After normalization, it is added to the calculation of the comprehensive evaluation value. Specifically, , The weighted average of the trend score values. The value can be set by staff according to actual needs.

[0048] The assessment system according to a second aspect of the present invention is used to perform the chronic disease assessment method disclosed in any of the above embodiments.

[0049] The assessment system of this invention performs the chronic disease assessment method disclosed in any of the above embodiments to conduct long-term monitoring and assessment of the patient's chronic disease risk and dynamically optimize the effectiveness of interventions.

[0050] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the chronic disease assessment method disclosed in any of the above embodiments.

[0051] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0052] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0053] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0054] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0055] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0056] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0057] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

[0058] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0059] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for assessing chronic diseases, characterized in that, include: Obtain the patient's k-th test data, which includes various physiological parameters. Construct a comprehensive feature dataset of the patient's k-th test based on these physiological parameters. When k is 1, the first assessment step is executed. In the first assessment step, the risk values ​​of different chronic diseases are calculated in the chronic disease risk model based on the various physiological parameters in the comprehensive feature dataset of this test. Intervention suggestions are given based on the risk values, and the risk values ​​and intervention suggestions are output. When k is greater than 1, the prognostic assessment step is executed. In the prognostic assessment step, the parameter change value is calculated based on the physiological parameters in the comprehensive feature dataset of this test and the physiological parameters in the comprehensive feature dataset of the first test. Based on the physiological parameters and parameter change values ​​in the comprehensive feature dataset of this test, the risk value of the corresponding chronic disease is calculated in the chronic disease risk model. After correcting the intervention recommendation output in the first test based on the parameter change value, the risk value and intervention recommendation are output.

2. The chronic disease assessment method according to claim 1, characterized in that, After obtaining the patient's k-th test data, the following is included: The actual values ​​of each physiological parameter in the test data are normalized to obtain the normalized feature value of each physiological parameter. The normalized feature value of each physiological parameter is used to construct a comprehensive feature dataset corresponding to the kth test of the patient. In the calculation of the risk values ​​of different chronic diseases in the chronic disease risk model based on the various physiological parameters in the comprehensive feature dataset of this test, and in the calculation of the risk values ​​of different chronic diseases based on the various physiological parameters and parameter changes in the comprehensive feature dataset of this test, the normalized characteristic values ​​of various physiological parameters are used to calculate the risk values ​​of different chronic diseases.

3. The chronic disease assessment method according to claim 2, characterized in that, The chronic disease risk model has risk scoring rules corresponding to different chronic diseases, and each risk scoring rule has a risk assessment value associated with at least one physiological parameter. In the chronic disease risk model, the disease risk value is calculated based on the risk assessment value and the comprehensive evaluation value of the corresponding physiological parameters, wherein the comprehensive evaluation value is derived from at least the actual value or normalized characteristic value of the corresponding physiological parameters.

4. The chronic disease assessment method according to claim 3, characterized in that, The disease risk value is: ； in, chronic diseases The risk value of the disease, For the first Risk assessment values ​​for several physiological parameters For the first A comprehensive evaluation value of several physiological parameters.

5. The chronic disease assessment method according to claim 3, characterized in that, In the initial evaluation step, the comprehensive evaluation value is the actual value or normalized characteristic value of the physiological parameter corresponding to this test; In the prognostic assessment step, the comprehensive evaluation value is calculated by weighting the parameter change value of this test and the actual value or normalized characteristic value of the corresponding physiological parameter of this test.

6. The chronic disease assessment method according to claim 5, characterized in that, In the prognostic assessment step, the comprehensive evaluation value is calculated by weighting the trend score, the parameter change value of this test, and the actual value or normalized characteristic value of the physiological parameter corresponding to this test. The trend score is calculated by integrating the physiological parameter of each test data in multiple tests with the physiological parameter of the previous test data.

7. The chronic disease assessment method according to claim 5, characterized in that, The score for the trend of change is: ； ； in, For the first The score for the trend of change of the physiological parameters For the first The data from the second test and the first The first detection data The change in a physiological parameter For the first Fusion coefficient of the second detection. As the baseline value for the fusion coefficient, Let be the fusion constant. This represents the total number of tests.

8. The chronic disease assessment method according to claim 1, characterized in that, The detection data includes fingertip video data, facial video data, micro-expression data, and patient personal data. The patient personal data includes one or more combinations of gender, age, height, and weight. Various physiological parameters are derived from the fingertip video data, facial video data, micro-expression data, and patient personal data.

9. An evaluation system, characterized in that, Used to perform the chronic disease assessment method as described in any one of claims 1 to 8.

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