Respiratory system disease prediction method, system and device and storage medium

Abstract

The invention relates to the technical field of respiratory medicine diagnosis, in particular to a respiratory system disease prediction method, system and device and a storage medium. According to the method, multiple types of patient data are obtained, the data are used in a combined mode, the illness probability and the illness degree of the patient are obtained through the column graph model based on the score corresponding to the data, and illness can be comprehensively explained through the patient data.

Description

Technical Field

[0001] The present application relates to the technical field of respiratory medicine diagnosis, and specifically to a respiratory disease prediction method, system, device and storage medium. Background Art

[0002] Bronchial asthma (abbreviated as asthma) is a heterogeneous disease characterized by chronic airway inflammation and airway hyperresponsiveness. Clinically, it manifests as recurrent symptoms such as wheezing, shortness of breath, chest tightness or cough, which often occur or worsen at night and early morning.

[0003] Current research on inflammatory indicators in bronchial asthma focuses more on how inflammatory factors mediate the onset, diagnosis and treatment of asthma. However, there is still huge research prospect in the control of asthma and the predictive value of inflammatory factors on the level of asthma control. How to accurately and comprehensively evaluate the development level of asthma is still a problem that clinical workers need to face. Summary of the Invention

[0004] In order to solve the technical problems existing in the prior art, the embodiment of the present application provides a respiratory disease prediction method based on machine learning, which predicts the development of respiratory diseases, especially bronchial asthma, by combining multiple physical and chemical indicators and combining them based on a nomogram.

[0005] In order to achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0006] In a first aspect, a method for predicting respiratory diseases is provided, the method comprising: obtaining a plurality of physicochemical indicator data and a plurality of test calibration data of a patient to be diagnosed, wherein the plurality of physicochemical indicator data are routine blood test data, and the plurality of test calibration data include blood gas analysis data, exhaled nitric oxide test data, and pulmonary function test data; obtaining a plurality of sub-scores corresponding to the plurality of physicochemical indicator data and the plurality of test calibration data based on a joint prediction model; the joint prediction model is a nomogram model; obtaining a total score for the patient to be diagnosed based on the plurality of sub-scores, and obtaining a probability of the patient to be diagnosed suffering from the respiratory disease based on a score-disease risk mapping relationship.

[0007] Furthermore, the plurality of routine blood test data include neutrophil-lymphocyte ratio, hematocrit value, eosinophil percentage, TyG index and IL-6 data.

[0008] Furthermore, the IL-6 data is the IL-6 data change rate, which is used to characterize the degree of change of the IL-6 data over time.

[0009] Furthermore, the blood gas analysis data is a carbon dioxide partial pressure value.

[0010] Furthermore, the lung function test data is the percentage of FEV1 to the expected value.

[0011] Furthermore, the exhaled nitric oxide test data is the exhaled nitric oxide data change rate, which is used to characterize the degree of change of the exhaled nitric oxide data over time.

[0012] Furthermore, the multiple sub-scores corresponding to the multiple physical and chemical indicator data and the multiple test calibration data are obtained based on the joint prediction model, including: based on the data-score mapping relationship in the nomogram model, obtaining the multiple sub-scores corresponding to the neutrophil-to-lymphocyte ratio, hematocrit value, eosinophil percentage, TyG index, IL-6 change rate, carbon dioxide partial pressure value, FEV1 percentage of the expected value and exhaled nitric oxide data change rate.

[0013] In a second aspect, a respiratory disease prediction system is provided, which includes: a data acquisition unit for acquiring multiple physical and chemical indicator data and multiple test calibration data of a patient to be diagnosed; a score calculation unit for respectively acquiring multiple sub-scores corresponding to the multiple physical and chemical indicator data and the multiple test calibration data based on a joint prediction model; a risk calculation unit for obtaining a total score for the patient to be diagnosed based on the multiple sub-scores, and obtaining the probability of the patient to be diagnosed suffering from the respiratory disease based on a score-disease risk mapping relationship.

[0014] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any one of the above-described respiratory disease prediction methods when executing the computer program.

[0015] In a fourth aspect, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the respiratory disease prediction method as described in any one of the above items is implemented.

[0016] In the technical solution provided in the embodiment of the present application, multiple types of patient data are obtained, multiple data are used together, and the probability and degree of illness of the patient are obtained based on the scores corresponding to the data through a nomogram model, wherein the patient data can fully explain the illness. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] The methods, systems, and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numerals represent similar structures in the various views of the drawings.

[0019] Figure 1 This is a flow chart of a respiratory disease prediction method provided in an embodiment of the present application.

[0020] Figure 2 This is a schematic diagram of the joint prediction model provided in the embodiment of the present application.

[0021] Figure 3 This is a schematic diagram of the structure of a respiratory disease prediction system provided in an embodiment of the present application.

[0022] Figure 4 This is a schematic diagram of the terminal device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to better understand the above technical solution, the technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0024] In the following detailed description, numerous specific details are set forth by way of example in order to provide a thorough understanding of the relevant teachings. However, it will be apparent to one skilled in the art that the present application can be practiced without these details. In other instances, well-known methods, procedures, systems, compositions, and / or circuits have been described at a relatively high level, without detail, to avoid unnecessarily obscuring aspects of the present application.

[0025] Flowcharts are used in this application to illustrate the execution processes performed by the system according to the embodiments of the present application. It should be clearly understood that the execution processes of the flowcharts may not be executed in sequence. Instead, these execution processes may be executed in reverse order or simultaneously. In addition, at least one additional execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0026] Before further explaining the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are explained. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0027] (1) In response to, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0028] (2) Based on, used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0029] See Figure 1 In an embodiment of the present application, in order to achieve the technical effects described in the background art, a prediction method is provided for the development level of respiratory diseases, especially bronchial asthma, and especially for the high-risk risk of bronchial asthma, by combining multiple data, including the following steps:

[0030] Step S11: Acquire multiple physical and chemical index data and multiple test calibration data of the patient to be diagnosed, wherein the multiple physical and chemical index data are routine blood test data.

[0031] In this embodiment, the routine blood test data includes the neutrophil-lymphocyte ratio, hematocrit value, eosinophil percentage, TyG index, and IL-6 data. The calibration data for multiple tests includes blood gas analysis data, exhaled nitric oxide test data, and pulmonary function test data, wherein the blood gas analysis data is the carbon dioxide partial pressure value, and the pulmonary function test data is the percentage of FEV1 to the predicted value.

[0032] The neutrophil-to-lymphocyte ratio (NLR) refers to the ratio of the absolute neutrophil count to the lymphocyte count. Neutrophils and lymphocytes play different roles in triggering asthma-induced airway inflammation. Neutrophils are crucial cells in the immune defense system, forming a crucial component of the host's response to invading pathogens and the first line of defense in the innate immune system against infection. They account for approximately 70% of peripheral blood white blood cells. Neutrophils play a crucial role in the human immune system. They not only have direct bactericidal functions but also regulate the functions of epithelial cells, mast cells, and macrophages, thereby maintaining the body's immune balance. Neutrophils play a particularly prominent role in inflammatory responses. They rapidly respond and migrate to the site of inflammation, effectively controlling infection and promoting the resolution of inflammation by phagocytosing and digesting pathogens and releasing antimicrobial substances. Therefore, neutrophils play a crucial role in inflammatory responses and are an indispensable component of maintaining human health. When infection or irritation induces inflammation, neutrophils are activated and release NETs. These NETs, composed of granules and a nuclear component, dislodge and kill extracellular pathogens through a series of activated signaling pathways. However, NETs can also cause potential damage, depending on the site, timing, and extent of the inflammatory response. Therefore, excessive production of NETs at a specific time or location can lead to tissue damage in the host. Airway neutrophils are associated with resistance to corticosteroids in asthma. Despite their active role in combating infection, NETs are present in the airways of patients with chronic airway inflammatory diseases. Furthermore, NET accumulation is associated with activation of innate immune responses and contributes to the pathogenesis of chronic airway inflammatory diseases. Lymphocytes play a crucial role in immunoregulation and immune responses. NLR, an inflammatory marker that has been extensively studied in recent years, has been shown in numerous studies to be closely associated with sepsis, pancreatitis, malignant tumors, and various infectious diseases. It can be used as a predictor of severity in critically ill patients.

[0033] Hematocrit (HCT) is an indicator of the relative concentration of red blood cells (RBCs) in the blood. It measures the oxygen-carrying capacity of RBCs and is also a major determinant of blood viscosity. Elevated HCT levels often occur as a secondary response to chronic hypoxemia, leading to secondary erythrocytosis. Chronic hypoxemia can occur secondary to various conditions, including lung diseases such as chronic obstructive pulmonary disease, muscle disorders such as obesity-hypoventilation syndrome, and airway diseases such as obstructive sleep apnea. In patients with asthma, prolonged airway remodeling can lead to chronic hypoxemia, which in turn increases HCT levels. Wheezing causes significant fluid loss from the airways, leading to hemoconcentration and an elevated HCT. Chronic airway inflammation in asthma requires RBCs to adapt to the hypoxic environment, resulting in a secondary increase in RBC mass and, consequently, an elevated HCT. However, the specific mechanisms underlying this increase require further investigation. Elevated HCT is significantly associated with a rapid decrease in platelet count and severe organ involvement. It will help clinicians to conduct timely monitoring and clinical management at the first presentation to minimize the risk of serious organ involvement and thus reduce the severity of the disease.

[0034] Eosinophils (EOS), originating from hematopoietic stem and progenitor cells in the bone marrow, are granulocytes involved in a variety of inflammatory responses. In asthma, EOS are typically recruited to tissues during a type 2 inflammatory response in the context of mucosal stress. Although eosinophilic asthma is typically caused by allergic reactions, it can also occur in non-allergic individuals. Basic proteins in crystalline granules may be associated with mucosal epithelial damage, while membrane-derived lipid mediators, particularly platelet-activating factor and leukotriene C4, may directly influence bronchial smooth muscle contraction, microvascular permeability, and mucus hypersecretion. The number of eosinophils and their products correlates with disease severity, and successful treatment is generally associated with the resolution of localized eosinophils.

[0035] Infiltration of numerous inflammatory cells and inflammatory mediators in the airways of asthmatics leads to airway wall edema, thickening of the reticular basement membrane, and collagen deposition in the subepithelial space. This is believed to be associated with asthma severity, forced expiratory volume in one second, and AHR. FEV1% is a better predictor of chronic respiratory symptoms in the general population than other lung function measures and is a reliable indicator of the onset and prognosis of respiratory diseases such as COPD and asthma.

[0036] The TyG index is determined based on the following formula: LN[triglyceride (mg / dl)*plasma glucose (mg / dl) / 2]. TyG is a simple and effective surrogate marker for assessing insulin resistance. However, the research reported in this embodiment found that an elevated TyG index is associated with an increased risk of acute asthma exacerbations, particularly in critically ill patients whose insulin sensitivity decreases to 50% to 70%. Therefore, the applicant believes that combining the TyG index with multiple bronchial asthma-related markers can help identify patients at high risk of death, allowing for potential early intervention.

[0037] In this embodiment, the IL-6 data and the exhaled nitric oxide test data are not the data at the current detection time compared with the above data, but the change status at multiple time points, that is, the IL-6 data is the IL-6 data change rate, which is used to characterize the degree of change of the IL-6 data over time; the exhaled nitric oxide test data is the exhaled nitric oxide data change rate, which is used to characterize the degree of change of the exhaled nitric oxide data over time.

[0038] Among them, the exhaled nitric oxide test data (FeNO) is closely related to the level of airway inflammation. Within a certain range, when the FeNO value increases, it indicates that the airway inflammation is more severe. The level of FeNO is higher in patients with poor asthma control. When FeNO exceeds 25ppb, it indicates poor asthma control. When the FeNO value is greater than 29.9ppb and the area under the ROC curve is 0.70, the measurement of FeNO is significantly correlated with uncontrolled asthma. Therefore, in this embodiment, the purpose of the applicant using the rate of change of this data as one of the indicators is to indicate whether the patient's asthma has worsened over a period of time through the data changes of the patient to be diagnosed, and to evaluate the worsening state based on the changing state.

[0039] IL-6 is a small molecule protein produced by various immune mediators and cells, and is involved in the process of cellular stress and inflammatory damage in the human body. IL-6 levels in asthma patients are higher than in healthy people, which to a certain extent confirms that IL-6 is indeed involved in the inflammatory response process of asthma, and also suggests that when asthma is controlled, the patient's IL-6 will also decrease. IL-6 promotes the release of polymorphonuclear leukocytes (PMNs) from the bone marrow into the peripheral blood, which increases the binding of PMNs to endothelial cells and inhibits the spontaneous apoptosis of neutrophils. Neutrophils can activate the ERK1 / 2 and p38 / MAPK signaling pathways to produce IL-6. Elevated IL-6 levels in asthma patients may lead to inflammatory reactions and allergic airway inflammation, so in this embodiment, the IL-6 data is also the rate of change of IL-6. The data changes of the patient to be diagnosed indicate whether the patient's asthma has worsened over a period of time, and the worsening state is assessed based on the change state. When the rate of change increases, it indicates that the patient's asthma symptoms have increased, and when the rate of change is high, it indicates that the patient is currently in a severe asthma attack stage.

[0040] In this embodiment, by combining the above-mentioned multiple indicators, prediction of bronchial asthma and evaluation of asthma disease are achieved, and whether the subsequent onset of the disease is high-risk is determined.

[0041] Step S12: Based on the joint prediction model, a plurality of sub-scores corresponding to the plurality of physical and chemical index data, the plurality of clinical data, and the plurality of imaging group feature data are respectively obtained.

[0042] In this embodiment, the joint prediction model is a nomogram model. Figure 2 As shown, including the score interval corresponding to the distribution of each indicator data, the corresponding sub-score can be determined according to the current numerical distribution of each indicator data. This method is commonly used in existing disease prediction and evaluation programs and will not be described in detail in this embodiment.

[0043] The acquisition of each sub-score is based on the household number-score mapping relationship in the nomogram model, and multiple sub-scores corresponding to the neutrophil-to-lymphocyte ratio, hematocrit value, eosinophil percentage, TyG index, IL-6 change rate, carbon dioxide partial pressure value, FEV1 percentage of the expected value and exhaled nitric oxide data change rate are obtained respectively.

[0044] Step S13: Obtain a total score for the patient to be diagnosed based on the multiple sub-scores, and obtain the disease risk of the patient to be diagnosed for the respiratory disease based on a score-disease risk mapping relationship.

[0045] In this embodiment, a total score of the patient to be diagnosed is obtained by summing up the multiple sub-scores, and the risk of respiratory diseases of the patient to be diagnosed is obtained based on the total score and the score-risk of disease mapping relationship.

[0046] Specifically, the score-disease risk mapping relationship is a mapping relationship between score and probability, that is, the disease probability can be determined by the total score.

[0047] In order to further assess the high risk of illness, a score-degree mapping relationship is also provided in this embodiment. This mapping relationship is used to indicate the relationship between the score and the degree of illness, and is used to indicate the current level of illness.

[0048] In this embodiment, multiple types of patient data are obtained, the multiple data are used together, and the probability and degree of illness of the patient are obtained based on the scores corresponding to the data through a nomogram model, wherein the patient data can fully explain the illness.

[0049] In this embodiment, see Figure 3 Based on steps S11 to S13, a virtual system, namely a respiratory disease prediction system 30, is further provided for executing the processing of steps S11 to S13. The system includes the following units:

[0050] The data acquisition unit 31 is used to acquire multiple physical and chemical index data and multiple test calibration data of the patient to be diagnosed;

[0051] A score calculation unit 32 is used to obtain a plurality of sub-scores corresponding to the plurality of physical and chemical index data and the plurality of test calibration data based on a joint prediction model;

[0052] The risk calculation unit 33 is configured to obtain a total score for the patient to be diagnosed based on the plurality of sub-scores, and obtain a probability of the patient to be diagnosed suffering from the respiratory disease based on a score-disease risk mapping relationship.

[0053] See Figure 4The above method can also be integrated into the provided terminal device 40. In view of the fact that the device may have relatively large differences due to different configurations or performance, it can include one or more processors 401 and memory 402. The memory 402 can store one or more applications or data. Among them, the memory 402 can be a temporary storage or a persistent storage. The application stored in the memory 402 can include one or more modules (not shown in the figure), each of which can include a series of computer-executable instructions in the terminal device. Furthermore, the processor 401 can be configured to communicate with the memory 402, and the terminal device can execute the series of computer-executable instructions in the memory 402. The terminal device can also include one or more power supplies 403, one or more wired or wireless network interfaces 404, one or more input / output interfaces 405, one or more keyboards 406, etc.

[0054] In a specific embodiment, the terminal device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the terminal device, and the one or more programs are configured to be executed by one or more processors, including computer-executable instructions for performing the following:

[0055] Acquiring multiple physical and chemical indicator data and multiple test calibration data of the patient to be diagnosed, wherein the multiple physical and chemical indicator data are routine blood test data, and the multiple test calibration data include blood gas analysis data, exhaled nitric oxide test data, and pulmonary function test data;

[0056] Obtaining a plurality of sub-scores corresponding to the plurality of physical and chemical index data and the plurality of test calibration data based on the joint prediction model;

[0057] A total score of the patient to be diagnosed is obtained based on the multiple sub-scores, and a probability of the patient to be diagnosed suffering from the respiratory disease is obtained based on a score-disease risk mapping relationship.

[0058] The following is a detailed introduction to the various components of the processor:

[0059] In this embodiment, the processor is an application specific integrated circuit (ASIC), or is configured to implement one or more integrated circuits of the embodiments of the present application, such as one or more microprocessors (digital signal processors, DSPs), or one or more field programmable gate arrays (FPGAs).

[0060] Optionally, the processor can execute various functions by running or executing the software program stored in the memory and calling the data stored in the memory, such as executing the above Figure 1 The method shown.

[0061] In a specific implementation, as an embodiment, the processor may include one or more microprocessors.

[0062] The memory is used to store the software program for executing the solution of the present application, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0063] Alternatively, the memory may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor or exist independently and be coupled to the processing unit through the interface circuit of the processor, and the embodiments of the present application do not specifically limit this.

[0064] It should be noted that the structure of the processor shown in this embodiment does not constitute a limitation on the device. The actual device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0065] In addition, the technical effects of the processor can refer to the technical effects of the method described in the above method embodiment, and will not be repeated here.

[0066] It should be understood that the processor in the embodiments of the present application may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0067] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0068] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0069] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0070] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0071] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0072] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0073] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0074] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0075] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0076] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0077] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for predicting respiratory diseases, characterized in that: The method comprises: Acquiring multiple physical and chemical indicator data and multiple test calibration data of the patient to be diagnosed, wherein the multiple physical and chemical indicator data are routine blood test data, and the multiple test calibration data include blood gas analysis data, exhaled nitric oxide test data, and pulmonary function test data; Based on a joint prediction model, a plurality of sub-scores corresponding to the plurality of physical and chemical index data and the plurality of test calibration data are respectively obtained; the joint prediction model is a nomogram model; A total score of the patient to be diagnosed is obtained based on the multiple sub-scores, and a probability of the patient to be diagnosed suffering from the respiratory disease is obtained based on a score-disease risk mapping relationship.

2. The respiratory disease prediction method according to claim 1, characterized in that: The multiple routine blood test data include neutrophil-lymphocyte ratio, hematocrit value, eosinophil percentage, TyG index and IL-6 data.

3. The respiratory disease prediction method according to claim 2, characterized in that: The IL-6 data is the IL-6 data change rate, which is used to characterize the degree of change of the IL-6 data over time.

4. The respiratory disease prediction method according to claim 1, characterized in that: The blood gas analysis data is the carbon dioxide partial pressure value.

5. The respiratory disease prediction method according to claim 1, characterized in that: The pulmonary function test data is the percentage of FEV1 to the predicted value.

6. The respiratory disease prediction method according to claim 1, characterized in that: The exhaled nitric oxide test data is the exhaled nitric oxide data change rate, which is used to represent the degree of change of the exhaled nitric oxide data over time.

7. The respiratory disease prediction method according to any one of claims 1 to 6, characterized in that: The method of obtaining multiple sub-scores corresponding to the multiple physical and chemical indicator data and the multiple test calibration data based on the joint prediction model includes: based on the data-score mapping relationship in the nomogram model, obtaining multiple sub-scores corresponding to the neutrophil-to-lymphocyte ratio, hematocrit value, eosinophil percentage, TyG index, IL-6 change rate, carbon dioxide partial pressure value, FEV1 percentage of the expected value and exhaled nitric oxide data change rate.

8. A respiratory disease prediction system, characterized in that: The system comprises: A data acquisition unit, used to acquire multiple physical and chemical index data and multiple test calibration data of the patient to be diagnosed; a score calculation unit, configured to respectively obtain a plurality of sub-scores corresponding to the plurality of physical and chemical index data and the plurality of test calibration data based on a joint prediction model; The risk calculation unit is used to obtain a total score of the patient to be diagnosed based on the multiple sub-scores, and to obtain a probability of the patient to be diagnosed suffering from the respiratory disease based on a score-disease risk mapping relationship.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the respiratory disease prediction method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the respiratory disease prediction method according to any one of claims 1 to 7 is implemented.

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