Heart failure patient noninvasive liquid management nursing system

Abstract

The invention relates to the technical field of medical care, and discloses a heart failure patient noninvasive liquid management nursing system which comprises a data acquisition module used for acquiring biomarkers of a heart failure patient and analyzing a body fluid state change rule of the heart failure patient; the body fluid state monitoring module is used for measuring impedance values of different parts of the heart failure patient and analyzing the evolution trend of the retention condition of the liquid retention part; the risk state identification module is used for analyzing the heart failure severity of the heart failure patient; the risk control module is used for generating illness condition guidance suggestions of the heart failure patient, constructing a risk early warning mechanism of the heart failure patient and creating a health management system of the heart failure patient; and the liquid management module is used for analyzing the liquid management effect of the heart failure patient in combination with a health management system, illness condition guidance suggestions and a risk early warning mechanism so as to execute liquid management processing of the heart failure patient and obtain a liquid management result. The pertinence and effectiveness of liquid management nursing of the heart failure patient can be improved.

Description

Technical Field

[0001] The present invention relates to the field of medical care technology, and in particular to a non-invasive fluid management and care system for heart failure patients. Background Art

[0002] Non-invasive fluid management for heart failure patients refers to the process of monitoring and managing the fluid status of heart failure patients through non-invasive means, aiming to maintain their fluid balance, prevent and treat worsening of heart failure or other complications caused by fluid retention. Heart failure is a complex chronic disease. Patients with impaired heart function are unable to effectively pump enough blood to meet the body's needs, resulting in fluid retention in the body, forming edema, and may cause a series of serious complications, such as dyspnea and arrhythmia. Therefore, fluid management is crucial in the treatment of heart failure patients.

[0003] Traditional fluid management methods for heart failure patients mainly rely on manual measurement of indicators such as weight and urine volume to assess body fluid status, and individual differences among patients are not fully considered when formulating fluid management plans. This is not only time-consuming and labor-intensive, but may also result in errors, resulting in a lack of targeted and effective nursing effect in fluid management for heart failure patients. Summary of the Invention

[0004] The present invention provides a non-invasive fluid management and nursing system for heart failure patients, the main purpose of which is to improve the pertinence and effectiveness of fluid management and nursing for heart failure patients.

[0005] To achieve the above objectives, the present invention provides a non-invasive fluid management and nursing system for heart failure patients, comprising: a data acquisition module, a body fluid status monitoring module, a risk status identification module, a risk control module, and a fluid management module; The data acquisition module is used to obtain a heart failure patient to be cared for, collect biomarkers of the heart failure patient, and measure impedance values ​​of different parts of the heart failure patient, and identify the fluid retention site of the heart failure patient based on the impedance values ​​of the different parts; The body fluid status monitoring module is configured to identify the body fluid status of the heart failure patient based on the biomarkers, extract evaluation indicators of the body fluid status, construct a real-time monitoring network for the body fluid status based on the evaluation indicators, and analyze the changing patterns of the body fluid status of the heart failure patient based on the real-time monitoring network; The risk status identification module is configured to identify impedance changes of the impedance values ​​at different locations, analyze the retention status evolution trend of the fluid retention location based on the impedance changes, analyze the severity of heart failure in the heart failure patient based on the fluid status change pattern and the retention status evolution trend, and identify the potential risk status of the heart failure patient; The risk control module is configured to generate condition guidance suggestions for the heart failure patient based on the severity of the heart failure, define graded warning thresholds for the heart failure patient, and establish a risk warning mechanism for the heart failure patient based on the potential risk status and the graded warning thresholds; The liquid management module is used to create a health management system for the heart failure patient based on the risk warning mechanism, and analyze the liquid management effect of the heart failure patient in combination with the health management system, the disease guidance suggestions and the risk warning mechanism. When the liquid management effect meets the preset effect, the liquid management processing of the heart failure patient is executed to obtain the liquid management result.

[0006] Optionally, analyzing the evolution trend of the fluid retention condition at the fluid retention site according to the impedance change includes: collecting impedance change data of the fluid retention area according to the impedance change; generating a spatial distribution map of the fluid retention site based on the impedance change data; identifying the retention region corresponding to the fluid retention site according to the spatial distribution map, and extracting the regional area and regional distribution characteristics of the retention region; Analyzing depth information of the spatial distribution map, and identifying volume parameters of the retention area based on the depth information; The evolution trend of the retention condition of the fluid retention area is analyzed based on the area of ​​the region, the regional distribution characteristics and the volume parameter.

[0007] Optionally, identifying the potential risk status of the heart failure patient based on the changing pattern of the body fluid status and the evolution trend of the retention status includes: extracting body fluid state elements and retention status indicators corresponding to the body fluid state change law and the retention status evolution trend; calculating the strength of association between the fluid status elements and the retention status indicators; Combining the association strength, the body fluid status element and the retention status indicator, constructing a body fluid dynamic association network of the heart failure patient; Identifying node factors in the body fluid dynamic association network and calculating centrality indices of the node factors; determining key nodes of the body fluid dynamic association network according to the centrality index, and extracting key association paths of the key nodes in the body fluid dynamic association network; Based on the key association paths and the key nodes, the potential risk status of the heart failure patient is identified.

[0008] Optionally, defining a graded warning threshold for the heart failure patient according to the severity of the heart failure includes: Identifying physiological evaluation indicators corresponding to the severity of the heart failure; extracting the early warning indicator of the heart failure patient from the physiological evaluation indicator according to the contribution coefficient; Analyze the abnormal range of the early warning indicator under the severity of the heart failure; Based on the abnormal range and the severity of the heart failure, setting a grading judgment baseline for the early warning indicator; A graded warning threshold for the heart failure patient is defined based on the warning indicator and the graded judgment baseline.

[0009] Optionally, constructing a risk warning mechanism for the heart failure patient based on the potential risk state and the graded warning threshold includes: Based on the potential risk status, setting a risk status scoring mechanism for the heart failure patient; determining the risk level of the heart failure patient according to the risk status scoring mechanism; Based on the graded warning threshold, define the warning signal rules for the risk level; According to the risk level, setting a risk intervention method for the heart failure patient; By combining the risk status scoring mechanism, the early warning signal rules and the risk intervention method, a risk early warning mechanism for heart failure patients is constructed.

[0010] Optionally, the health management system for the heart failure patient is established based on the risk warning mechanism, including: Based on the risk warning mechanism, identifying the current health status of the heart failure patient; configuring a dynamic symptom monitoring network for the heart failure patient according to the current health condition; Building a self-management mechanism for the heart failure patient based on the dynamic symptom monitoring network; Setting a health education portal for the heart failure patient based on the self-management mechanism and the symptom dynamic monitoring network; By combining the dynamic symptom monitoring network, the self-management mechanism and the health education portal, a health management system for the heart failure patients is created.

[0011] Optionally, analyzing the effect of fluid management on the heart failure patient in combination with the health management system, the disease guidance and the risk warning mechanism includes: collecting physiological data, biomarker data, and patient self-reported data from the heart failure patient according to the health management system; Extracting the fluid management goals and treatment priorities for the heart failure patient based on the disease guidance recommendations; generating a current fluid status analysis report of the heart failure patient based on the physiological data, the biomarker data, the patient self-reported data, and the fluid management goal; Based on the risk warning mechanism, setting risk stratification for the heart failure patient; constructing a fluid management priority list for the heart failure patient based on the risk stratification and the treatment priority; creating a personalized fluid management approach for the heart failure patient based on the current fluid status analysis report and the fluid management priority list; Analyze the effect of fluid management on the heart failure patient based on the personalized fluid management method.

[0012] The embodiment of the present invention can identify the site of fluid retention and help evaluate the severity of the disease in heart failure patients by collecting the biomarkers of the heart failure patients and measuring the impedance values ​​of different parts of the heart failure patients; further, the embodiment of the present invention can accurately determine fluid retention by identifying the body fluid status of the heart failure patients based on the biomarkers and constructing a real-time monitoring network for the body fluid status, and help monitor the disease progression of heart failure patients and prevent serious events such as acute heart failure attacks; the embodiment of the present invention can formulate personalized rehabilitation care plans for patients and prevent complications by analyzing the evolution trend of the retention status of the fluid retention site according to the impedance changes; further, the embodiment of the present invention can accurately determine the fluid retention of the heart failure patients based on the biomarkers and build a real-time monitoring network for the body fluid status. Analyzing the severity of heart failure in the heart failure patient based on the changing rules of the body fluid state and the evolution trend of the retention condition can guide the doctor's treatment decision, optimize the patient's nursing advice, and help identify the potential risk status of the heart failure patient, so as to more accurately grasp the development of the disease and provide a basis for timely intervention; the embodiment of the present invention generates disease guidance suggestions for the heart failure patient based on the severity of the heart failure, which can not only comprehensively consider the patient's overall condition, but also closely follow the development of the disease and adjust the treatment plan in time; further, the embodiment of the present invention defines the graded warning threshold of the heart failure patient based on the severity of the heart failure, which can provide timely and accurate risk prompts for the clinic according to the real-time changes of the disease, based on the The potential risk status and the graded warning threshold are used to construct a risk warning mechanism for the heart failure patient, which can more accurately assess the patient's current risk level and possible risk changes in the future, and help medical staff adjust the treatment plan in a timely manner; the embodiment of the present invention creates a health management system for the heart failure patient based on the risk warning mechanism, and can formulate personalized disease guidance suggestions and risk intervention methods, so as to better meet the needs of different patients and improve the treatment effect and the patient's quality of life; further, the embodiment of the present invention analyzes the fluid management effect of the heart failure patient by combining the health management system, the disease guidance suggestions and the risk warning mechanism, and can verify the effectiveness of the fluid management measures for heart failure patients. The invention can improve the patient's fluid status, thereby reducing the risk of acute exacerbation of heart failure caused by fluid retention, and further reducing the number of re-hospitalizations and the occurrence of complications; finally, the embodiment of the present invention can more accurately assess the patient's fluid status by executing the fluid management process of the heart failure patient when the fluid management effect meets the preset effect, and obtain the fluid management result. Based on the patient's individual characteristics and dynamic risk warning, a personalized fluid management plan can be formulated and optimized to improve the pertinence and adaptability of the treatment. At the same time, it can reduce the error and burden of manual intervention and improve management efficiency. In addition, the patient's participation and self-management ability can be enhanced through the health management system, ultimately achieving the goal of improving patient prognosis, reducing the risk of acute heart failure attacks, and improving the quality of life. Therefore, the pertinence and effectiveness of fluid management care for heart failure patients are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a functional module diagram of a non-invasive fluid management and nursing system for heart failure patients provided by one embodiment of the present invention; Figure 2 A flowchart of a non-invasive fluid management nursing method for heart failure patients provided by one embodiment of the present invention; The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0015] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0016] In practice, the server-side device deployed in a non-invasive fluid management and nursing system for heart failure patients may be composed of one or more devices. The aforementioned non-invasive fluid management and nursing system for heart failure patients can be implemented as: a service instance, a virtual machine, and a hardware device. For example, the non-invasive fluid management and nursing system for heart failure patients can be implemented as a service instance deployed on one or more devices in a cloud node. Simply put, the non-invasive fluid management and nursing system for heart failure patients can be understood as software deployed on a cloud node, used to provide a non-invasive fluid management and nursing service for heart failure patients to each user terminal. Alternatively, the non-invasive fluid management and nursing system for heart failure patients can be implemented as a virtual machine deployed on one or more devices in a cloud node. Application software for managing each user terminal is installed in the virtual machine. Alternatively, the non-invasive fluid management and nursing system for heart failure patients can be implemented as a server-side device composed of numerous hardware devices of the same or different types, with one or more hardware devices configured to provide a non-invasive fluid management and nursing service for heart failure patients to each user terminal.

[0017] In terms of implementation, a non-invasive fluid management and nursing system for heart failure patients and a user terminal are mutually compatible. Specifically, if the non-invasive fluid management and nursing system for heart failure patients is an application installed on a cloud service platform, the user terminal is a client that establishes a communication connection with the application; or if the non-invasive fluid management and nursing system for heart failure patients is implemented as a website, the user terminal is implemented as a webpage; or if the non-invasive fluid management and nursing system for heart failure patients is implemented as a cloud service platform, the user terminal is implemented as a mini-program within an instant messaging application.

[0018] Reference Figure 1 , which is a functional module diagram of a non-invasive fluid management and nursing system for heart failure patients provided by one embodiment of the present invention.

[0019] The non-invasive fluid management and nursing system 100 for heart failure patients described in the present invention can be installed in a cloud server. In terms of implementation, it can be implemented as one or more service devices, or as an application installed in the cloud (e.g., a server or server cluster for non-invasive fluid management and nursing of heart failure patients), or it can be developed as a website. Depending on the functionality implemented, the non-invasive fluid management and nursing system 100 for heart failure patients includes a data acquisition module 101, a fluid status monitoring module 102, a risk status identification module 103, a risk control module 104, and a fluid management module 105.

[0020] In an embodiment of the present invention, based on the tracking of non-invasive fluid management and nursing care for heart failure patients, each of the above modules can be independently implemented and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. In a non-invasive fluid management and nursing system for heart failure patients provided by an embodiment of the present invention, the scope of application of a non-invasive fluid management and nursing architecture for heart failure patients can be adjusted by adding modules and directly calling them without modifying the program code, thereby realizing cluster-type horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding a non-invasive fluid management and nursing system for heart failure patients. In actual applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0021] The following describes various components and specific workflows of a non-invasive fluid management and nursing system for heart failure patients in conjunction with specific embodiments.

[0022] The data acquisition module 101 is used to obtain a heart failure patient to be cared for, collect biomarkers of the heart failure patient, and measure impedance values ​​of different parts of the heart failure patient, and identify the fluid retention site of the heart failure patient based on the impedance values ​​of different parts.

[0023] The embodiment of the present invention can provide data support objects for subsequent body fluid management by obtaining heart failure patients to be cared for. The heart failure patients refer to individuals diagnosed with heart failure. Heart failure is a syndrome caused by abnormal heart structure or function, which results in the heart being unable to effectively pump blood to the whole body, thereby causing a series of clinical symptoms and signs.

[0024] Furthermore, the embodiments of the present invention can identify the sites of fluid retention and help assess the severity of the disease in heart failure patients by collecting biomarkers of the heart failure patients and measuring the impedance values ​​of different parts of the heart failure patients. The biomarkers refer to a class of substances that can reflect the physiological and pathological states of an organism, such as troponin and high-sensitivity C-reactive protein. The impedance values ​​of different parts refer to the resistance values ​​of different parts of the human body to electric current measured by bioelectrical impedance technology, such as chest impedance values ​​and limb impedance values.

[0025] Optionally, the biomarker collection of the heart failure patient can be obtained by detecting the patient's exhaled gas, and the impedance value measurement of different parts of the heart failure patient can be achieved using a bioelectrical impedance analyzer.

[0026] The embodiment of the present invention identifies the fluid retention site of the heart failure patient based on the impedance values ​​of the different sites, thereby intuitively understanding the degree and site of fluid retention and optimizing the use of diuretics. The fluid retention site refers to the specific body part where fluid abnormally accumulates in the heart failure patient, such as the lungs and lower limbs.

[0027] As an embodiment of the present invention, identifying the site of fluid retention in the heart failure patient based on the impedance values ​​of the different sites includes: identifying the body site corresponding to the impedance values ​​of the different sites; measuring the segment length of the body site, and calculating the body fluid volume of the body site based on the segment length and the impedance values ​​of the different sites; determining the body fluid distribution baseline value of the body site based on the body fluid volume; identifying the body fluid distribution deviation of the body site based on the body fluid distribution baseline value and the body fluid volume; and identifying the fluid retention site in the heart failure patient based on the body fluid distribution deviation.

[0028] Among them, the different parts refer to the various areas into which the body of a heart failure patient is divided, such as the chest, abdomen, left lower limb, right lower limb, etc., the segment length refers to the linear size of each divided body part, such as the calf length from the ankle to the knee, the body fluid volume refers to the volume of fluid contained in each body part, the fluid distribution baseline value refers to the reference value of the body fluid distribution of the body parts of a heart failure patient under normal or stable conditions, and the body fluid distribution deviation refers to the difference between the current body fluid distribution and the baseline value.

[0029] Optionally, the determination of the baseline value of the fluid distribution in the body part according to the body fluid volume can be obtained through the body fluid distribution data of healthy people, the identification of the body fluid distribution deviation in the body part based on the body fluid distribution baseline value and the body fluid volume can be achieved by utilizing the difference between the body fluid volume and the body fluid distribution baseline value, and the identification of the fluid retention site in the heart failure patient according to the body fluid distribution deviation can be obtained through the logical threshold of the body fluid distribution deviation.

[0030] In an optional embodiment of the present invention, the body fluid volume of the body part is calculated based on the segment length and the impedance value of the different parts using the following formula: ; in, represents the volume of body fluid at body part i, k represents the calibration constant, represents the segment length of body part i, represents the impedance value of body part i among the impedance values ​​of different parts, and i represents the category index of the body part.

[0031] It should be noted that It represents the impedance value of body part i. The lower the impedance value, the higher the relative content of body fluid in that part. The length of body part i reflects the spatial size of that part and, combined with impedance information, helps to more accurately estimate body fluid volume. For example, at the same impedance, a longer limb can theoretically hold more body fluid. Furthermore, the use of the calibration constant k ensures the accuracy of the formula. Due to differences in the electrical properties of different human tissues and the accuracy and measurement methods of different measuring devices, the k value needs to be determined through measurement and calibration of a standard model with known body fluid volume.

[0032] The body fluid status monitoring module 102 is used to identify the body fluid status of the heart failure patient based on the biomarkers, extract evaluation indicators of the body fluid status, construct a real-time monitoring network for the body fluid status according to the evaluation indicators, and analyze the changing patterns of the body fluid status of the heart failure patient based on the real-time monitoring network.

[0033] The embodiments of the present invention can accurately determine fluid retention and help monitor the progression of heart failure patients by identifying the body fluid status of the heart failure patient based on the biomarkers. The body fluid status refers to the comprehensive situation of the total amount, distribution, composition and dynamic balance of body fluids in the human body.

[0034] Optionally, based on the biomarkers, the identification of the fluid status of the heart failure patient can be obtained by combining multiple biomarker analyses, such as combining brain natriuretic peptide (BNP) or N-terminal pro-brain natriuretic peptide (NT-proBNP) with soluble growth-stimulated gene expression 2 protein (sST2), troponin (cTn), etc., to evaluate cardiac function, myocardial damage and fluid retention from different angles. If the brain natriuretic peptide (BNP) is elevated, it indicates that there may be fluid retention.

[0035] Furthermore, by extracting the evaluation indicators of the body fluid status, the embodiments of the present invention can promptly detect subtle changes in the disease condition and take intervention measures before the disease worsens. The evaluation indicators refer to a series of parameters that measure and reflect the body fluid status of the patient, such as weight and lung rales.

[0036] Optionally, the extraction of evaluation indicators of body fluid status can be achieved using principal component analysis.

[0037] The embodiment of the present invention constructs a real-time monitoring network for the body fluid status based on the evaluation indicators, which can timely capture abnormal changes in the body fluid status, prevent serious events such as acute heart failure attacks, and at the same time achieve precise treatment of heart failure patients. The real-time monitoring network refers to a system that can collect, transmit, analyze and feedback the patient's body fluid status-related data in real time and continuously to achieve dynamic monitoring and management of the patient's body fluid status.

[0038] As an embodiment of the present invention, the real-time monitoring network for the body fluid status is constructed based on the evaluation indicators, including: performing outlier removal processing on the evaluation indicators to obtain standard evaluation indicators; extracting key evaluation indicators of the body fluid status from the standard evaluation indicators; constructing a state monitoring network for the body fluid status based on the key evaluation indicators; setting a dynamic feedback mechanism for the body fluid status based on the state monitoring network; outputting real-time monitoring results of the body fluid status based on the dynamic feedback mechanism, and creating a data visualization interface for the real-time monitoring results; and constructing a real-time monitoring network for the body fluid status by combining the state monitoring network, the dynamic feedback mechanism and the data visualization interface.

[0039] Among them, the outlier removal processing refers to the process of identifying and eliminating data points that deviate significantly from the normal range, the standard evaluation index refers to the evaluation index after the outlier removal processing, the key evaluation index refers to the most representative index closely related to the body fluid status screened out from the standard evaluation index, such as total fluid volume, metabolic status, etc., the state monitoring network refers to a system for real-time monitoring of body fluid status, the dynamic feedback mechanism refers to a mechanism that can output monitoring data results in real time, the real-time monitoring results refer to the real-time evaluation information about the body fluid status output by the state monitoring network according to the dynamic feedback mechanism, and the data visualization interface refers to a user-friendly interface that displays the real-time monitoring results to the user in an intuitive manner.

[0040] Optionally, the outlier removal processing of the evaluation indicators can be achieved using the triple standard deviation method, the state monitoring network construction of the body fluid status based on the key evaluation indicators can be obtained through a sensor network, the dynamic feedback mechanism setting of the body fluid status based on the state monitoring network can be achieved using a regression model, and the creation of the data visualization interface of the real-time monitoring results based on the dynamic feedback mechanism can be obtained through a visualization tool, such as a Tableau tool.

[0041] Furthermore, the embodiments of the present invention can avoid serious respiratory dysfunction and worsening of heart failure in heart failure patients and improve the quality of life of patients by analyzing the changing patterns of the body fluid status of the heart failure patients based on the real-time monitoring network. The changing patterns of the body fluid status refer to the changing patterns or trends in the distribution, volume, composition, etc. of the body fluid of heart failure patients over time. For example, if a patient gains several kilograms in weight within a few days, accompanied by worsening dyspnea and increased moist rales in the lungs, it indicates a rapid increase in body fluid volume.

[0042] As an embodiment of the present invention, the analysis of the changing pattern of the body fluid status of the heart failure patient based on the real-time monitoring network includes: obtaining multidimensional physiological parameters of the heart failure patient based on the real-time monitoring network, wherein the multidimensional physiological parameters include cardiac parameters, body fluid parameters and blood oxygen and blood pressure parameters; identifying the parameter changing trend of the multidimensional physiological parameters; extracting the body fluid status characteristics of the heart failure patient based on the parameter changing trend; calculating the correlation between the multidimensional physiological parameters and the body fluid status characteristics based on the parameter changing trend; extracting the key influencing factors of the body fluid status characteristics from the multidimensional physiological parameters based on the correlation; and analyzing the changing pattern of the body fluid status of the heart failure patient based on the key influencing factors.

[0043] Among them, the multidimensional physiological parameters refer to a variety of physiological indicators obtained from heart failure patients, covering aspects such as cardiac function, fluid status, blood oxygen and blood pressure, such as heart rate, urine volume, blood pressure, etc. The parameter change trend refers to the change pattern of multidimensional physiological parameters over time, such as increase, decrease or periodic fluctuation. The fluid status characteristics refer to the typical manifestations of the fluid status of heart failure patients identified through the change trend and correlation degree of multidimensional physiological parameters, such as rapid weight gain, significant increase in natriuretic peptide levels, accompanied by enlargement of heart chambers and decreased ejection fraction, indicating that the patient may be in a state of aggravated fluid retention and worsening cardiac function. The correlation degree refers to the statistical correlation between multidimensional physiological parameters and fluid status characteristics. The key influencing factors refer to the physiological parameters that have the greatest impact on fluid status characteristics, such as natriuretic peptide levels, ejection fraction, weight, etc.

[0044] Optionally, the parameter change trend identification of the multidimensional physiological parameters can be achieved using a time series analysis method, and the extraction of the body fluid status characteristics of the heart failure patient based on the parameter change trend can be obtained through a cluster analysis algorithm. The calculation of the degree of correlation between the multidimensional physiological parameters and the body fluid status characteristics based on the parameter change trend can be achieved using the Pearson correlation coefficient method, and the analysis of the change rules of the body fluid status of the heart failure patient based on the key influencing factors can be determined by a machine learning algorithm, such as a classification algorithm.

[0045] In an optional embodiment of the present invention, based on the correlation degree, the key influencing factors of the body fluid status characteristics are extracted from the multidimensional physiological parameters using the following formula: ; in, The key factors that influence the characteristics of body fluid status, represents the jth physiological parameter related to the liquid state feature in the multidimensional physiological parameters, S represents the set of liquid state features, s represents the specific liquid state feature in the feature set, represents the probability of the jth physiological parameter and the liquid state characteristic appearing simultaneously, represents the probability of observing the jth physiological parameter alone, represents the probability of observing the liquid state feature alone, j represents the parameter index of the multidimensional physiological parameter related to the liquid state feature, and X represents the parameter set of the multidimensional physiological parameter related to the liquid state feature.

[0046] It should be noted that for every possible pair and S, calculate the logarithm of the ratio of the probability of their simultaneous occurrence to the product of the probability of their independent occurrence, and then add up all these values. The larger the value, the better the parameter. The information provided is more helpful in determining the fluid status characteristic S, so the parameter with a larger value It is considered to be a key factor that has a significant impact on the body fluid status characteristic S.

[0047] The risk status identification module 103 is used to identify the impedance changes of the impedance values ​​of different parts, analyze the retention status evolution trend of the fluid retention part according to the impedance changes, analyze the severity of heart failure of the heart failure patient based on the law of the body fluid status changes and the retention status evolution trend, and identify the potential risk status of the heart failure patient.

[0048] The embodiments of the present invention can provide an important basis for judging the fluid balance state in the patient's body by identifying the impedance changes of the impedance values ​​of different parts. The impedance changes refer to the changes in the electrical impedance of tissues in different parts of the human body over time or changes in physiological state, including the amplitude, speed, trend, etc. of the changes.

[0049] Optionally, the impedance change identification of the impedance values ​​of different parts can be obtained by measuring the local impedance changes of different body parts using a multi-electrode array.

[0050] Furthermore, the embodiment of the present invention can formulate a personalized rehabilitation care plan for the patient and prevent complications by analyzing the evolution trend of the retention condition of the fluid retention area according to the impedance change. The evolution trend of the retention condition refers to the overall direction and situation of the change of the fluid volume in the fluid retention area of ​​the heart failure patient over time.

[0051] As an embodiment of the present invention, the analyzing the evolution trend of the retention condition of the liquid retention site based on the impedance change includes: collecting impedance change data of the liquid retention site based on the impedance change; generating a spatial distribution map of the liquid retention site based on the impedance change data; identifying the retention area corresponding to the liquid retention site based on the spatial distribution map, and extracting the regional area and regional distribution characteristics of the retention area; analyzing the depth information of the spatial distribution map, and identifying the volume parameters of the retention area based on the depth information; and analyzing the evolution trend of the retention condition of the liquid retention site in combination with the regional area, the regional distribution characteristics and the volume parameters.

[0052] The impedance change data refers to data generated by changes in impedance values ​​associated with fluid retention sites over time or other factors. The spatial distribution map refers to an image that graphically displays the spatial distribution of fluid retention sites. The retention region refers to a specific area in the spatial distribution map where fluid retention is more pronounced. The regional area refers to the two-dimensional area occupied by the retention region on the spatial distribution map. The regional distribution characteristics refer to the geometric and statistical characteristics of the fluid retention region on the spatial distribution map, including but not limited to shape, size, location, and distribution pattern. The depth information refers to information about the depth position of the fluid retention region in three-dimensional space. For example, in the spatial distribution map, at a depth level close to the body surface, the fluid retention region may appear as a small ellipse. As depth increases, the fluid retention region may gradually become larger and take on an irregular shape closer to the lungs. The detailed distribution of the fluid retention region at different depths presented in the spatial distribution map is the depth information of the spatial distribution map. The volume parameter refers to the spatial size occupied by the fluid retention region in three-dimensional space.

[0053] Optionally, the impedance change data collection of the fluid retention site based on the impedance change can be obtained by electrical impedance imaging technology, and the spatial distribution map of the fluid retention site based on the impedance change data can be generated by an image reconstruction algorithm, such as a back projection algorithm. The identification of the retention area corresponding to the fluid retention site based on the spatial distribution map can be obtained by image processing technology, such as region growing technology. The area extraction of the retention area based on the spatial distribution map can be achieved by a contour extraction method. The depth information analysis of the spatial distribution map can be obtained by a three-dimensional reconstruction technology, such as VTK three-dimensional reconstruction technology. The volume parameter identification of the retention area based on the depth information can be determined by a voxel counting method, such as by counting the number of voxels (three-dimensional pixels) in the fluid retention area and multiplying it by the volume of each voxel to obtain the total volume of the fluid retention area.

[0054] The embodiment of the present invention can guide the doctor's treatment decision and optimize the patient's care recommendations by analyzing the severity of the heart failure of the heart failure patient based on the law of changes in the body fluid status and the evolution trend of the retention condition. The severity of heart failure refers to the severity of the patient's heart failure and impaired cardiac pumping function obtained based on the law of changes in the patient's body fluid status and the evolution trend of the retention condition. For example, the law of changes in body fluid status shows abnormal fluctuations in biomarkers, and the retention area of ​​the fluid retention site gradually expands and the fluid gradually increases, which may cause the patient's heart failure to worsen.

[0055] Optionally, based on the changing pattern of the body fluid status and the evolution trend of the retention condition, the heart failure severity analysis of the heart failure patient can be determined by a multivariate regression model, such as a random forest model.

[0056] Furthermore, the embodiments of the present invention can more accurately grasp the development of the disease and provide a basis for timely intervention by identifying the potential risk status of the heart failure patient based on the changing patterns of the body fluid status and the evolution trend of the retention condition. The potential risk status refers to the adverse health conditions that the heart failure patient may be about to face or easily cause based on the current changing patterns of the body fluid status and the evolution trend of the retention condition, such as the risk of acute decompensation and the risk of lung infection.

[0057] As an embodiment of the present invention, the identifying the potential risk status of the heart failure patient based on the body fluid state change law and the retention status evolution trend includes: extracting the body fluid state elements and retention status indicators corresponding to the body fluid state change law and the retention status evolution trend; calculating the correlation strength between the body fluid state elements and the retention status indicators; constructing a body fluid dynamic association network of the heart failure patient by combining the correlation strength, the body fluid state elements and the retention status indicators; identifying the node factors in the body fluid dynamic association network and calculating the centrality index of the node factors; determining the key nodes of the body fluid dynamic association network according to the centrality index, and extracting the key association paths of the key nodes in the body fluid dynamic association network; identifying the potential risk status of the heart failure patient based on the key association paths and the key nodes.

[0058] Among them, the body fluid status element refers to the key factor that can reflect the law of changes in the body fluid status of heart failure patients, such as weight changes, biomarker levels, etc. The retention status index refers to the quantitative parameter used to measure the evolution trend of fluid retention status, such as the degree of jugular vein distension, ascites volume, etc. The association strength refers to the degree of mutual connection between the body fluid status element and the retention status index. The body fluid dynamic association network refers to a network structure constructed based on the body fluid status element and the retention status index and their association strength. In this network, each body fluid status element and retention status index is regarded as a node, and the association strength between them is represented by an edge. The node factor refers to the node represented by the specific body fluid status element or retention status index in the body fluid dynamic association network. Centrality index refers to a quantitative index that measures the importance of node factors in the body fluid dynamic association network, including but not limited to degree centrality, closeness centrality and betweenness centrality. The key node refers to a node with an important position in the body fluid dynamic association network determined based on the centrality index. For example, the node of BNP level may be identified as a key node because it has a strong connection with multiple other nodes reflecting body fluid status and retention status, and is in a key position in information transmission and has a high centrality index. The key association path refers to a path of great significance connecting key nodes in the body fluid dynamic association network, for example, a path formed from a node representing rapid weight gain, through several intermediate nodes, to a node representing the expansion of the lung fluid retention area.

[0059] Optionally, the construction of the body fluid dynamic association network of the heart failure patient in combination with the association strength, the body fluid status elements and the retention status indicators can be implemented using the built-in functions of the MATLAB tool, and the extraction of the key association paths of the key nodes in the body fluid dynamic association network can be obtained through path analysis tools, such as the shortest_path function tool in the NetworkX library. The identification of the potential risk status of the heart failure patient based on the key association paths and the key nodes can be implemented using a network evolution model, such as a random block model, and the calculation of the centrality index of the node factor can be implemented through the igraph package of the R language.

[0060] The risk control module 104 is used to generate disease guidance suggestions for the heart failure patient according to the severity of the heart failure, and define the graded warning thresholds for the heart failure patient, and build a risk warning mechanism for the heart failure patient based on the potential risk status and the graded warning thresholds.

[0061] The embodiment of the present invention generates medical condition guidance suggestions for the heart failure patient based on the severity of the heart failure, which can not only comprehensively consider the patient's overall condition, but also closely follow the development of the disease and adjust the treatment plan in a timely manner. The medical condition guidance suggestions refer to a series of targeted action guidelines provided to patients and medical staff based on the assessment of the severity of heart failure in heart failure patients. For example, if a patient progresses from mild to moderate heart failure, it is recommended to switch from relatively relaxed life management and conventional drug treatment to strengthened drug intervention, increased frequency of review, etc.

[0062] Optionally, the generation of the condition guidance suggestions for the heart failure patient based on the severity of the heart failure can be obtained through a rule engine, such as using predefined rules to generate personalized suggestions based on the severity of the heart failure and patient characteristics.

[0063] Furthermore, the embodiment of the present invention defines a graded warning threshold for the heart failure patient according to the severity of the heart failure, and can provide timely and accurate risk warnings to the clinic according to the real-time changes in the condition. The graded warning threshold refers to the critical value or range for graded warning of the severity of the heart failure patient's condition. For example, a left ventricular ejection fraction between 40% and 50% may indicate mild heart function impairment, while a left ventricular ejection fraction of less than 30% may indicate severe heart failure.

[0064] As an embodiment of the present invention, defining the graded warning threshold of the heart failure patient according to the severity of the heart failure includes: identifying the physiological evaluation index corresponding to the severity of the heart failure; calculating the contribution coefficient of the physiological evaluation index to the severity of the heart failure; extracting the warning index of the heart failure patient from the physiological evaluation index according to the contribution coefficient; analyzing the abnormal range of the warning index under the severity of the heart failure; setting the graded judgment baseline of the warning index based on the abnormal range and the severity of the heart failure; and defining the graded warning threshold of the heart failure patient according to the warning index and the graded judgment baseline.

[0065] Among them, the physiological evaluation indicators refer to quantitative parameters used to evaluate the physiological state of patients with heart failure, including indicators of cardiac function, hemodynamics, metabolic state, etc. The contribution coefficient refers to the weight or importance of each physiological evaluation indicator on the severity of heart failure. The early warning indicator refers to an indicator screened out from the physiological evaluation indicators, which is sensitive to changes in heart failure and has a high contribution coefficient, such as cardiac function indicators. The abnormal range refers to the normal or abnormal numerical range of the early warning indicator under different heart failure severities. For example, a brain natriuretic peptide less than 100pg / mL indicates normal, between 100-300pg / mL indicates a slight increase, and greater than 300pg / mL indicates a significant increase. The graded judgment baseline refers to the thresholds of different warning levels set according to the severity of heart failure and the abnormal range of the early warning indicator.

[0066] Optionally, the abnormal range analysis of the early warning indicator under the severity of the heart failure can be achieved using a statistical method, such as the percentile method.

[0067] In an optional embodiment of the present invention, the contribution coefficient of the physiological evaluation index to the severity of heart failure is calculated using the following formula: ; in, represents the contribution coefficient of the fth physiological evaluation index to the severity of heart failure, Indicates the weight adjustment factor of the fth physiological evaluation index, such as the weight adjustment factor =1, It can be 0.3, represents the normalized value of the fth physiological evaluation index, It represents the mean value of the comprehensive evaluation index corresponding to the severity of heart failure. Represents the normalized value of the g-th physiological evaluation index, such as the normalized value of the physiological evaluation index in interval, It can be 1, represents the clinical significance score of the fth physiological evaluation index, such as the clinical significance score in interval, It can be 80, represents the scoring threshold of the clinical significance score, k represents the adjustment coefficient of the clinical significance score formula, which can be 0.5, f represents the specific index variable of the physiological evaluation indicator, g represents the general index variable of the physiological evaluation indicator, and n represents the total number of physiological evaluation indicators.

[0068] It needs to be explained that the formula Represents the modified linear unit function, which is used to filter out the index values ​​that have no positive contribution to the severity of heart failure. It is a quantitative result obtained through comprehensive analysis and calculation based on multiple physiological evaluation indicators, clinical symptoms, patient medical history and other information. For example, the New York Heart Failure Classification is not a single physiological evaluation indicator. f is an identifier for a specific single indicator, while g is a traversal index used for overall operations on all indicators. Through this distinction, the contribution coefficient of each physiological evaluation indicator to the severity of heart failure can be calculated more clearly and accurately.

[0069] The embodiment of the present invention constructs a risk warning mechanism for heart failure patients based on the potential risk status and the graded warning threshold, which can more accurately assess the patient's current risk level and possible risk changes in the future, and help medical staff adjust the treatment plan in a timely manner. The risk warning mechanism refers to a system for monitoring and evaluating changes in the condition of heart failure patients, timely discovering potential risks and issuing corresponding alarms.

[0070] As an embodiment of the present invention, the risk warning mechanism for the heart failure patient is constructed based on the potential risk status and the graded warning threshold, including: setting a risk status scoring mechanism for the heart failure patient based on the potential risk status; determining the risk level of the heart failure patient according to the risk status scoring mechanism; defining the warning signal rule for the risk level based on the graded warning threshold; setting the risk intervention method for the heart failure patient according to the risk level; and constructing the risk warning mechanism for the heart failure patient in combination with the risk status scoring mechanism, the warning signal rule and the risk intervention method.

[0071] Among them, the risk status scoring mechanism refers to a system for quantitatively assessing the potential risk status of heart failure patients, the risk level refers to the score obtained according to the risk status scoring mechanism, which divides patients into different risk groups, the risk intervention method refers to the specific medical intervention measures taken according to the patient's risk level, and the early warning signal rule refers to the corresponding alarm method and standards set for different risk levels based on the graded early warning threshold. For example, when the patient's risk status score reaches the threshold corresponding to the medium risk level, the system will issue a yellow early warning signal, prompting medical staff to pay attention to the patient's condition and adjust the treatment plan in time; if the score reaches the threshold of the high risk level, a red early warning signal will be issued, which means that the situation is urgent and the patient needs to be rescued immediately or more active treatment measures need to be taken.

[0072] Optionally, the risk status scoring mechanism setting for the heart failure patient based on the potential risk status can be determined by a gradient boosting tree model, the warning signal rule definition for the risk level based on the graded warning threshold can be implemented using the Jess rule engine, and the risk intervention method setting for the heart failure patient based on the risk level can be obtained through a remote monitoring device, such as a wearable device.

[0073] The liquid management module 105 is used to create a health management system for the heart failure patient based on the risk warning mechanism, and analyze the liquid management effect of the heart failure patient in combination with the health management system, the disease guidance suggestions and the risk warning mechanism. When the liquid management effect meets the preset effect, the liquid management processing of the heart failure patient is executed to obtain the liquid management result.

[0074] The embodiment of the present invention creates a health management system for heart failure patients based on the risk warning mechanism, and can formulate personalized disease guidance suggestions and risk intervention methods to better meet the needs of different patients, improve treatment effects and patients' quality of life. The health management system refers to a comprehensive framework for comprehensive monitoring, evaluation, intervention and management of health-related factors of heart failure patients.

[0075] As an embodiment of the present invention, the health management system for the heart failure patient is created based on the risk warning mechanism, including: identifying the current health status of the heart failure patient based on the risk warning mechanism; configuring a symptom dynamic monitoring network for the heart failure patient according to the current health status; constructing a self-management mechanism for the heart failure patient based on the symptom dynamic monitoring network; setting a health education portal for the heart failure patient based on the self-management mechanism and the symptom dynamic monitoring network; and creating a health management system for the heart failure patient by combining the symptom dynamic monitoring network, the self-management mechanism and the health education portal.

[0076] Among them, the current health status refers to the overall health status of heart failure patients at a specific time point or period, the symptom dynamic monitoring network refers to a platform that continuously collects and analyzes symptom data of heart failure patients, the self-management mechanism refers to a patient self-monitoring system set up based on the information provided by the symptom dynamic monitoring network and the guidance of the medical team, and the health education portal refers to a portal that provides health education resources to patients through a digital platform (such as mobile applications, websites).

[0077] Optionally, according to the current health status, the configuration of the dynamic symptom monitoring network of the heart failure patient can be implemented using a mobile application, based on the dynamic symptom monitoring network, the construction of the self-management mechanism of the heart failure patient can be obtained through a behavioral intervention algorithm, and according to the self-management mechanism and the dynamic symptom monitoring network, the setting of the health education portal for the heart failure patient can be implemented using a personalized recommendation algorithm.

[0078] Furthermore, the embodiments of the present invention analyze the fluid management effect of the heart failure patients by combining the health management system, the disease guidance suggestions and the risk warning mechanism, and can verify the effectiveness of the fluid management measures for heart failure patients, thereby reducing the risk of acute exacerbation of heart failure caused by fluid retention in patients, and further reducing the number of re-hospitalizations and the occurrence of complications. The fluid management effect refers to the effect achieved after treating heart failure patients through a fluid management plan. The purpose of fluid management is to reduce fluid retention in the body by restricting fluid intake, using diuretics, etc., thereby reducing the burden on the heart and improving cardiac function.

[0079] As an embodiment of the present invention, the combination of the health management system, the disease guidance recommendations and the risk warning mechanism to analyze the fluid management effect of the heart failure patient includes: collecting the physiological data, biomarker data and patient self-reported data of the heart failure patient according to the health management system; extracting the fluid management goals and treatment priorities of the heart failure patient based on the disease guidance recommendations; generating a current fluid status analysis report of the heart failure patient according to the physiological data, the biomarker data, the patient self-reported data and the fluid management goals; setting the risk stratification of the heart failure patient based on the risk warning mechanism; constructing a fluid management priority list for the heart failure patient according to the risk stratification and the treatment priority; creating a personalized fluid management method for the heart failure patient based on the current fluid status analysis report and the fluid management priority list; and analyzing the fluid management effect of the heart failure patient according to the personalized fluid management method.

[0080] The physiological data refers to patient physiological indicator data directly measured by medical devices or sensors, such as blood pressure and heart rate data. The biomarker data refers to heart failure-related biomarker level data obtained through laboratory testing or rapid testing equipment, such as electrolyte levels and troponin levels. The patient self-reported data refers to symptom, lifestyle, and fluid intake data proactively reported by patients. The fluid management goal refers to a fluid management goal established based on the patient's condition and treatment guidelines, such as a weight stabilization goal. The treatment priority refers to the priority of treatment measures determined based on the urgency of the patient's condition and the importance of the treatment goal. The current fluid status analysis report refers to a comprehensive analysis report reflecting the patient's current fluid status, generated based on physiological data, biomarker data, and patient self-reported data. Risk stratification refers to the classification of patients into different risk levels based on preset risk thresholds and patient data. The fluid management priority list refers to a priority list of fluid management measures established based on risk stratification and treatment priority, such as prioritizing diuretic dose adjustment, fluid restriction, or ultrafiltration therapy. The personalized fluid management approach refers to a personalized fluid management plan developed based on the patient's specific circumstances (e.g., fluid status, risk stratification, and treatment priority).

[0081] Optionally, according to the health management system, the biomarker data collection of the heart failure patients can be obtained through laboratory tests, the patient self-reported data collection of the heart failure patients can be implemented using online questionnaires, and based on the risk warning mechanism, the risk stratification setting of the heart failure patients can be determined by the warning threshold in the risk warning mechanism, and based on the current fluid status analysis report and the fluid management priority list, the creation of personalized fluid management methods for heart failure patients can be implemented using personalized treatment algorithms. The specific steps are: using the health management system to monitor patient data in real time, the risk warning mechanism dynamically evaluates the patient's risk status based on the real-time monitored patient data, and the condition guidance suggestions provide adjustment suggestions based on the patient's risk status.

[0082] The embodiment of the present invention obtains a fluid management result by executing the fluid management treatment of the heart failure patient when the fluid management effect meets the preset effect. This can more accurately evaluate the patient's fluid status, formulate and optimize personalized fluid management plans based on the patient's individual characteristics and dynamic risk warnings, improve the targetedness and adaptability of treatment, and reduce the errors and burden of manual intervention, thereby improving management efficiency. In addition, the patient's participation and self-management ability can be enhanced through the health management system, ultimately achieving the goals of improving patient prognosis, reducing the risk of acute heart failure attacks, and improving quality of life. The preset effect refers to a pre-set fluid management goal or standard used to evaluate whether the fluid management treatment has achieved the expected therapeutic effect. The fluid management treatment refers to the process of providing fluid management care to heart failure patients based on the patient's fluid status analysis report, risk stratification, and personalized fluid management plan. The fluid management result refers to the changes in the patient's fluid status and related physiological indicators after the fluid management treatment, such as weight stabilization or decrease, edema reduction, and dyspnea relief.

[0083] Exemplarily, as an embodiment of the present invention, when the liquid management effect meets the preset effect, the liquid management processing of the heart failure patient is executed to obtain the liquid management result, which is divided into two situations: for example, when the liquid management effect meets the preset effect, the goal and direction of liquid management are determined according to the disease guidance suggestions, such as limiting the sodium salt intake level, adjusting the diuretic use plan, and using the risk warning mechanism to monitor the potential risks in the liquid management process in real time. Once an abnormality occurs, timely adjustment is made. With the health management system as the framework, specific liquid management measures are implemented, such as controlling the daily infusion volume and the patient's water intake, so as to execute the liquid management processing of the heart failure patient and obtain the liquid management result; if the liquid management effect does not meet the preset effect, the rationality of the health management system is re-evaluated, and the disease guidance suggestions are adjusted according to the actual situation of the patient, such as further strengthening dietary restrictions or increasing the intensity of the exercise rehabilitation plan, and the graded warning threshold and risk warning mechanism are readjusted to ensure that the changes in the disease can be captured in a timely and accurate manner. Based on the adjusted system and mechanism, the liquid management processing flow is re-planned until the liquid management effect meets the preset requirements.

[0084] The embodiment of the present invention can identify the site of fluid retention and help evaluate the severity of the disease in heart failure patients by collecting the biomarkers of the heart failure patients and measuring the impedance values ​​of different parts of the heart failure patients; further, the embodiment of the present invention can accurately determine fluid retention by identifying the body fluid status of the heart failure patients based on the biomarkers and constructing a real-time monitoring network for the body fluid status, and help monitor the disease progression of heart failure patients and prevent serious events such as acute heart failure attacks; the embodiment of the present invention can formulate personalized rehabilitation care plans for patients and prevent complications by analyzing the evolution trend of the retention status of the fluid retention site according to the impedance changes; further, the embodiment of the present invention can accurately determine the fluid retention of the heart failure patients based on the biomarkers and build a real-time monitoring network for the body fluid status. Analyzing the severity of heart failure in the heart failure patient based on the changing rules of the body fluid state and the evolution trend of the retention condition can guide the doctor's treatment decision, optimize the patient's nursing advice, and help identify the potential risk status of the heart failure patient, so as to more accurately grasp the development of the disease and provide a basis for timely intervention; the embodiment of the present invention generates disease guidance suggestions for the heart failure patient based on the severity of the heart failure, which can not only comprehensively consider the patient's overall condition, but also closely follow the development of the disease and adjust the treatment plan in time; further, the embodiment of the present invention defines the graded warning threshold of the heart failure patient based on the severity of the heart failure, which can provide timely and accurate risk prompts for the clinic according to the real-time changes of the disease, based on the The potential risk status and the graded warning threshold are used to construct a risk warning mechanism for the heart failure patient, which can more accurately assess the patient's current risk level and possible risk changes in the future, and help medical staff adjust the treatment plan in a timely manner; the embodiment of the present invention creates a health management system for the heart failure patient based on the risk warning mechanism, and can formulate personalized disease guidance suggestions and risk intervention methods, so as to better meet the needs of different patients and improve the treatment effect and the patient's quality of life; further, the embodiment of the present invention analyzes the fluid management effect of the heart failure patient by combining the health management system, the disease guidance suggestions and the risk warning mechanism, and can verify the effectiveness of the fluid management measures for heart failure patients. The invention can improve the patient's fluid status, thereby reducing the risk of acute exacerbation of heart failure caused by fluid retention, and further reducing the number of re-hospitalizations and the occurrence of complications; finally, the embodiment of the present invention can more accurately assess the patient's fluid status by executing the fluid management process of the heart failure patient when the fluid management effect meets the preset effect, and obtain the fluid management result. Based on the patient's individual characteristics and dynamic risk warning, a personalized fluid management plan can be formulated and optimized to improve the pertinence and adaptability of the treatment. At the same time, it can reduce the error and burden of manual intervention and improve management efficiency. In addition, the patient's participation and self-management ability can be enhanced through the health management system, ultimately achieving the goal of improving patient prognosis, reducing the risk of acute heart failure attacks, and improving the quality of life. Therefore, the pertinence and effectiveness of fluid management care for heart failure patients are improved.

[0085] like Figure 2 FIG. 1 is a flow chart of a non-invasive fluid management nursing method for heart failure patients provided by one embodiment of the present invention. In this embodiment, the non-invasive fluid management nursing method for heart failure patients includes: Acquiring a heart failure patient to be cared for, collecting biomarkers of the heart failure patient, and measuring impedance values ​​of different parts of the heart failure patient, and identifying the fluid retention site of the heart failure patient according to the impedance values ​​of the different parts; Based on the biomarkers, identifying the body fluid status of the heart failure patient, extracting evaluation indicators of the body fluid status, constructing a real-time monitoring network for the body fluid status according to the evaluation indicators, and analyzing the changing patterns of the body fluid status of the heart failure patient based on the real-time monitoring network; Identifying impedance changes of the impedance values ​​at different locations, analyzing the retention status evolution trend of the fluid retention location based on the impedance changes, analyzing the severity of heart failure in the heart failure patient based on the fluid status change pattern and the retention status evolution trend, and identifying the potential risk status of the heart failure patient; Generate guidance suggestions for the heart failure patient based on the severity of the heart failure, define graded warning thresholds for the heart failure patient, and establish a risk warning mechanism for the heart failure patient based on the potential risk state and the graded warning thresholds; Based on the risk warning mechanism, a health management system for the heart failure patient is created. Combined with the health management system, the disease guidance suggestions and the risk warning mechanism, the fluid management effect of the heart failure patient is analyzed. When the fluid management effect meets the preset effect, the fluid management treatment of the heart failure patient is executed to obtain the fluid management result.

[0086] In the several embodiments provided by the present invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and actual implementation may employ other division methods.

[0087] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A non-invasive fluid management and nursing system for heart failure patients, characterized by: The non-invasive fluid management nursing system for heart failure patients comprises: a data acquisition module, a body fluid status monitoring module, a risk status identification module, a risk control module and a fluid management module; The data acquisition module is used to obtain a heart failure patient to be cared for, collect biomarkers of the heart failure patient, and measure impedance values ​​of different parts of the heart failure patient, and identify the fluid retention site of the heart failure patient based on the impedance values ​​of the different parts; The body fluid status monitoring module is configured to identify the body fluid status of the heart failure patient based on the biomarkers, extract evaluation indicators of the body fluid status, construct a real-time monitoring network for the body fluid status based on the evaluation indicators, and analyze the changing patterns of the body fluid status of the heart failure patient based on the real-time monitoring network; The risk status identification module is configured to identify impedance changes of the impedance values ​​at different locations, analyze the retention status evolution trend of the fluid retention location based on the impedance changes, analyze the severity of heart failure in the heart failure patient based on the fluid status change pattern and the retention status evolution trend, and identify the potential risk status of the heart failure patient; The risk control module is configured to generate condition guidance suggestions for the heart failure patient based on the severity of the heart failure, define graded warning thresholds for the heart failure patient, and establish a risk warning mechanism for the heart failure patient based on the potential risk status and the graded warning thresholds; The liquid management module is used to create a health management system for the heart failure patient based on the risk warning mechanism, and analyze the liquid management effect of the heart failure patient in combination with the health management system, the disease guidance suggestions and the risk warning mechanism. When the liquid management effect meets the preset effect, the liquid management processing of the heart failure patient is executed to obtain the liquid management result.

2. A non-invasive fluid management and nursing system for heart failure patients as claimed in claim 1, characterized in that: The step of identifying the fluid retention site of the heart failure patient according to the impedance values ​​of the different sites includes: Identifying the body parts corresponding to the impedance values ​​of the different parts; measuring the segment lengths of the body parts, and calculating the body fluid volume of the body parts based on the segment lengths and the impedance values ​​of the different parts; determining a baseline value of body fluid distribution in the body part according to the body fluid volume; identifying a body fluid distribution deviation in the body region based on the body fluid distribution baseline value and the body fluid volume; Based on the fluid distribution deviation, the site of fluid retention in the heart failure patient is identified.

3. A non-invasive fluid management and nursing system for heart failure patients as claimed in claim 1, characterized in that: The step of constructing a real-time monitoring network for the body fluid status according to the evaluation index comprises: Performing outlier removal processing on the evaluation index to obtain a standard evaluation index; Extracting key evaluation indicators of the body fluid status from the standard evaluation indicators; Constructing a state monitoring network for the body fluid state according to the key evaluation indicators; Based on the state monitoring network, a dynamic feedback mechanism of the body fluid state is set; Outputting the real-time monitoring results of the body fluid status according to the dynamic feedback mechanism, and creating a data visualization interface for the real-time monitoring results; By combining the state monitoring network, the dynamic feedback mechanism and the data visualization interface, a real-time monitoring network for the body fluid state is constructed.

4. A non-invasive fluid management and nursing system for heart failure patients as claimed in claim 1, characterized in that: The analyzing, based on the real-time monitoring network, the changing pattern of the body fluid status of the heart failure patient includes: Based on the real-time monitoring network, multidimensional physiological parameters of the heart failure patient are obtained, wherein the multidimensional physiological parameters include cardiac parameters, body fluid parameters, and blood oxygen and blood pressure parameters; Identifying parameter change trends of the multidimensional physiological parameters; extracting the body fluid status characteristics of the heart failure patient according to the parameter change trend; Calculating the correlation between the multidimensional physiological parameter and the body fluid status characteristic based on the parameter change trend; extracting key influencing factors of the body fluid status characteristics from the multidimensional physiological parameters according to the correlation degree; Based on the key influencing factors, the changing patterns of the body fluid status of the heart failure patients are analyzed.

5. The non-invasive fluid management and nursing system for heart failure patients according to claim 1, characterized in that: Analyzing the evolution trend of the fluid retention condition at the fluid retention site based on the impedance change includes: collecting impedance change data of the fluid retention area according to the impedance change; generating a spatial distribution map of the fluid retention site based on the impedance change data; identifying the retention region corresponding to the fluid retention site according to the spatial distribution map, and extracting the regional area and regional distribution characteristics of the retention region; Analyzing depth information of the spatial distribution map, and identifying volume parameters of the retention area based on the depth information; The evolution trend of the retention condition of the fluid retention area is analyzed based on the area of ​​the region, the regional distribution characteristics and the volume parameter.

6. A non-invasive fluid management and nursing system for heart failure patients as claimed in claim 1, characterized in that: The identifying of the potential risk status of the heart failure patient based on the changing pattern of the body fluid status and the evolution trend of the retention status includes: extracting body fluid state elements and retention status indicators corresponding to the body fluid state change law and the retention status evolution trend; calculating the strength of association between the fluid status elements and the retention status indicators; Combining the association strength, the body fluid status element and the retention status indicator, constructing a body fluid dynamic association network of the heart failure patient; Identifying node factors in the body fluid dynamic association network and calculating centrality indices of the node factors; determining key nodes of the body fluid dynamic association network according to the centrality index, and extracting key association paths of the key nodes in the body fluid dynamic association network; Based on the key association paths and the key nodes, the potential risk status of the heart failure patient is identified.

7. The non-invasive fluid management and nursing system for heart failure patients according to claim 1, characterized in that: Defining the graded warning thresholds for the heart failure patients according to the severity of the heart failure includes: Identifying physiological evaluation indicators corresponding to the severity of the heart failure; Calculating the contribution coefficient of the physiological evaluation index to the severity of the heart failure; extracting the early warning indicator of the heart failure patient from the physiological evaluation indicator according to the contribution coefficient; Analyze the abnormal range of the early warning indicator under the severity of the heart failure; Based on the abnormal range and the severity of the heart failure, setting a grading judgment baseline for the early warning indicator; A graded warning threshold for the heart failure patient is defined based on the warning indicator and the graded judgment baseline.

8. The non-invasive fluid management and nursing system for heart failure patients according to claim 1, characterized in that: The risk warning mechanism for the heart failure patient is constructed based on the potential risk state and the graded warning threshold, including: Based on the potential risk status, setting a risk status scoring mechanism for the heart failure patient; determining the risk level of the heart failure patient according to the risk status scoring mechanism; Based on the graded warning threshold, define the warning signal rules for the risk level; According to the risk level, setting a risk intervention method for the heart failure patient; By combining the risk status scoring mechanism, the early warning signal rules and the risk intervention method, a risk early warning mechanism for heart failure patients is constructed.

9. The non-invasive fluid management and nursing system for heart failure patients according to claim 1, characterized in that: The health management system for the heart failure patient is established based on the risk warning mechanism, including: Based on the risk warning mechanism, identifying the current health status of the heart failure patient; configuring a dynamic symptom monitoring network for the heart failure patient according to the current health condition; Building a self-management mechanism for the heart failure patient based on the dynamic symptom monitoring network; Setting a health education portal for the heart failure patient based on the self-management mechanism and the symptom dynamic monitoring network; By combining the dynamic symptom monitoring network, the self-management mechanism and the health education portal, a health management system for the heart failure patients is created.

10. The non-invasive fluid management and nursing system for heart failure patients according to claim 1, characterized in that: The analysis of the fluid management effect of the heart failure patient in combination with the health management system, the disease guidance and the risk warning mechanism includes: collecting physiological data, biomarker data, and patient self-reported data from the heart failure patient according to the health management system; Extracting the fluid management goals and treatment priorities for the heart failure patient based on the disease guidance recommendations; generating a current fluid status analysis report of the heart failure patient based on the physiological data, the biomarker data, the patient self-reported data, and the fluid management goal; Based on the risk warning mechanism, setting risk stratification for the heart failure patient; constructing a fluid management priority list for the heart failure patient based on the risk stratification and the treatment priority; creating a personalized fluid management approach for the heart failure patient based on the current fluid status analysis report and the fluid management priority list; Analyze the effect of fluid management on the heart failure patient based on the personalized fluid management method.

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