Monitoring method and system for prevention and treatment of renal fibrosis

Abstract

The invention discloses a monitoring method and system for prevention and treatment of renal fibrosis. The monitoring method comprises the following steps: collecting biomarker data in a urine sample of a patient in real time through a biosensing detector; transmitting the acquired biomarker data to a data processing center, and establishing a renal fibrosis risk assessment model of the patient; according to the risk assessment result, making a personalized prevention and treatment scheme, and monitoring the execution condition of the prevention and treatment scheme in real time; periodically detecting blood sample indexes of a patient, and combining with the biomarker data to obtain a comprehensive evaluation effect of renal fibrosis prevention and treatment; and feeding back the comprehensive evaluation result to a patient and medical staff, and adjusting the personalized prevention and treatment scheme according to the comprehensive evaluation result. According to the scheme, accurate and dynamic monitoring on prevention and treatment of the renal fibrosis can be achieved, the prevention and treatment effect of the renal fibrosis can be improved, and the life quality and prognosis of a patient are improved.

Description

Technical Field

[0001] The present invention relates to the field of health care monitoring, and in particular to a monitoring method and system for preventing and treating renal fibrosis. Background Art

[0002] Renal fibrosis is a common pathway for various chronic kidney diseases to progress to end-stage renal failure, which is characterized by excessive deposition of extracellular matrix (ECM) and scarring of the renal parenchyma. With factors such as the aging of the population, the increase in underlying diseases, and the impact of an unhealthy lifestyle, the incidence of chronic kidney disease is gradually increasing worldwide. The occurrence and development of renal fibrosis is a complex and progressive process. There may be no obvious symptoms in the early stages, but as the disease progresses, it will gradually lead to serious damage to the structure and function of the kidneys, and may eventually cause renal failure, requiring renal replacement therapy such as dialysis or kidney transplantation, which brings a heavy burden to patients and their families and puts tremendous pressure on medical resources.

[0003] At present, traditional diagnostic methods mainly rely on renal function tests, such as serum creatinine, blood urea nitrogen and other indicators. However, these indicators will only change significantly when renal function has been significantly damaged, and cannot accurately reflect the condition in the early stages of renal fibrosis.

[0004] At the same time, relying solely on renal function indicators cannot fully understand the specific situation of renal fibrosis. Renal fibrosis involves multiple aspects such as the deposition of renal extracellular matrix, inflammatory response, and changes in cytokines. Traditional diagnostic methods often cannot comprehensively evaluate these factors. For example, for key extracellular matrix components in the process of renal fibrosis, such as collagen and fibronectin, and biomarkers such as transforming growth factor-β (TGF-β) that play an important regulatory role in the process of fibrosis, traditional detection methods are difficult to accurately and real-time monitor their changes.

[0005] In addition, traditional renal fibrosis monitoring usually requires patients to go to the hospital for examination, which is not only limited by the hospital's examination equipment and personnel arrangements, but also makes the monitoring frequency cannot be too high. For a progressive disease such as renal fibrosis, failure to understand the dynamic changes of the disease in a timely manner may lead to missing the best time for treatment. For example, patients may have a renal function test once a quarter or half a year, but during this period, the condition of renal fibrosis may have changed and cannot be discovered and treated in time.

[0006] Therefore, the current methods for monitoring renal fibrosis have the following shortcomings:

[0007] (1) There is a delay in diagnosis;

[0008] (2) Lack of comprehensiveness of diagnosis;

[0009] (3) Monitoring frequency and timeliness are limited. Summary of the invention

[0010] In response to the above technical problems, the present invention proposes a monitoring method and system for the prevention and treatment of renal fibrosis, which can realize accurate monitoring, dynamic monitoring, and real-time monitoring of the prevention and treatment of renal fibrosis, help improve the prevention and treatment effect of renal fibrosis, improve the quality of life and prognosis of patients, and avoid serious health losses in patients.

[0011] The technical solution used in the present invention is: a monitoring method for the prevention and treatment of renal fibrosis, used for a monitoring system for the prevention and treatment of renal fibrosis, comprising the following steps: Step S100: real-time collection of biomarker data in a patient's urine sample through a biosensor detector 1; Step S200: transmitting the collected biomarker data to a data processing center to establish a patient's renal fibrosis risk assessment model; Step S300: formulating a personalized prevention and treatment plan based on the risk assessment results, and monitoring the implementation of the prevention and treatment plan in real time; Step S400: regularly testing the patient's blood sample indicators, combining the biomarker data, to obtain a comprehensive evaluation effect of renal fibrosis prevention and treatment; Step S500: feeding back the comprehensive evaluation results to the patient and medical staff, and adjusting the personalized prevention and treatment plan based on the comprehensive evaluation results.

[0012] Furthermore, the collected biomarker data is transmitted to a data processing center to establish a patient's renal fibrosis risk assessment model, which also includes that the renal fibrosis risk assessment model is obtained by analyzing and processing the biomarker data using a machine learning algorithm at the data processing center.

[0013] Furthermore, it also includes: step S210: preprocessing the data, including removing outliers and standardizing the data; step S220: randomly selecting and running a machine learning algorithm for measurement; step S230: establishing a risk assessment model for renal fibrosis based on the measurement results of the machine learning algorithm.

[0014] Furthermore, the biomarkers include collagen, transforming growth factor β (TGF-β) and fibronectin.

[0015] Furthermore, the blood sample indicators are renal function indicators including serum creatinine and urea nitrogen.

[0016] Furthermore, the personalized prevention and treatment plan includes drug treatment, diet adjustment and exercise intervention.

[0017] Furthermore, the risk assessment model can give corresponding risk assessment results based on the patient's real-time biomarker data; the risk assessment results include low risk, medium risk or high risk.

[0018] Furthermore, it includes a biosensor detector, a smart phone, a cloud data processing center and a medical data center; the biosensor detector is used to obtain biomarker data in the patient's urine sample and send the biomarker data to the smart phone; the smart phone is used to input self-report data, obtain biomarker data of the biosensor detector, and send the self-report data and the biomarker data to the cloud data processing center; the medical data center is used to obtain the patient's blood sample indicators and send the blood sample indicators to the cloud data processing center; the cloud data processing center generates evaluation results and personalized prevention and treatment plans based on the biomarker data, and generates a comprehensive prevention and treatment evaluation effect based on the blood sample indicators; and sends the evaluation results, the personalized prevention and treatment plans and the comprehensive prevention and treatment evaluation effect to the smart phone and the medical data center.

[0019] Furthermore, the biosensor detector also includes a latex shell, a control device, a metal joint, a flexible suction nozzle and a cap container; the latex shell is used for hand-held operation, and urine can be sucked from the cap container by squeezing the latex shell hard; four recessed grip grooves are provided on one side of the latex shell for four-finger gripping of the palm; the control device is arranged on the latex shell, including a switch button, a measurement button and a display panel; the metal joint is arranged on the lower side of the latex shell; the flexible suction nozzle is arranged on the lower side of the metal joint; the cap container is used to hold urine and can be detachably fixed to the metal joint.

[0020] Furthermore, the biosensor detector also includes a first processor unit, a first communication unit, a first analysis unit, a first display unit and a button unit; the smart phone terminal includes a second processor unit, a second communication unit and an input and display unit; the cloud data processing center includes a third processor unit and a third communication unit; the medical data center includes a fourth processor unit, a fourth communication unit and a second analysis unit; the first processor unit is used to perform preliminary calculations and sorting on the relevant signals of the biomarker indicators; the first communication unit is wirelessly connected to the second communication unit to achieve data sharing and interaction; the first display unit is used to display the operation interface and the biomarker indicator data; the button unit is used for the patient to perform button operations on the biosensor detector; the first analysis unit is used to analyze the biomarker data in the patient's urine sample to generate the biomarker indicators.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: by real-time collection of urine biomarker data and regular testing of blood renal function indicators, a risk assessment model is established in combination with a machine learning algorithm, a personalized prevention and treatment plan is formulated for the patient, and the plan is monitored and adjusted in real time, thereby achieving accurate and dynamic monitoring of the prevention and treatment of renal fibrosis, which helps to improve the prevention and treatment effect of renal fibrosis and improve the patient's quality of life and prognosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flow chart of a monitoring method for preventing and treating renal fibrosis.

[0023] Figure 2 This is an architecture diagram of a monitoring system for preventing and treating renal fibrosis according to the present invention.

[0024] Figure 3 This is a schematic diagram of a biosensor detector for preventing and treating renal fibrosis according to the present invention.

[0025] Figure 4 This is a schematic diagram of the principle structure of a monitoring system for preventing and treating renal fibrosis according to the present invention.

[0026] Figure 5 This is a timing operation diagram of a monitoring system for preventing and treating renal fibrosis according to the present invention.

[0027] Figure numerals: 1. Biosensor detector; 2. Smart phone terminal; 3. Cloud data processing center; 4. Medical data center; 11. Latex shell; 12. Control device; 13. Metal joint; 14. Flexible nozzle; 15. Cap container. DETAILED DESCRIPTION

[0028] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0029] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] The present invention will be specifically described below in conjunction with the accompanying drawings:

[0031] A monitoring method for preventing and treating renal fibrosis, such as Figure 1 As shown, a monitoring system for preventing and treating renal fibrosis comprises the following steps:

[0032] Step S100: collecting biomarker data in a patient's urine sample in real time through the biosensor detector 1 .

[0033] In this step, the biomarkers include collagen, transforming growth factor β (TGF-β) and fibronectin.

[0034] It can be understood that the biosensor detector 1 has high sensitivity and specificity and can accurately identify target biomarkers in urine. For example, by using a biosensor detector 1 based on the principle of antibody-antigen specific binding, corresponding antibody coatings are designed for collagen, TGF-β and fibronectin, etc. When the biomarkers in urine come into contact with the biosensor detector 1, a specific binding reaction can occur, thereby being detected by the biosensor detector 1.

[0035] It is understandable that the patient discharges urine into the collection device as required, and the biosensor detector 1 starts working immediately. For the detection of collagen, the biosensor detector 1 performs quantitative analysis by identifying the unique structure and chemical properties of collagen. TGF-β and fibronectin are similar. The biosensor detector 1 can capture their presence in urine and accurately measure their content. The collection process is carried out in real time, which means that once a urine sample enters the collection device, the biosensor detector 1 will quickly acquire data in order to timely understand the dynamic changes of these biomarkers in the patient's body.

[0036] Step S200: Transmit the collected biomarker data to a data processing center to establish a patient's renal fibrosis risk assessment model.

[0037] In this step, the renal fibrosis risk assessment model is achieved by analyzing and processing the biomarker data using a machine learning algorithm at the data processing center.

[0038] It can be understood that the renal fibrosis risk assessment model is a big data model used for information storage and algorithm calculation.

[0039] It is understandable that, through the biosensor detector 1, the collected biomarker data is transmitted to the mobile phone in real time through wireless transmission technology (such as Bluetooth, Wi-Fi, etc.), and then transmitted to the data processing center in real time through the mobile phone.

[0040] It is understandable that during the transmission process, if it is necessary to ensure the security and integrity of the data and prevent the data from being tampered with or lost, encryption technology can be used to encrypt the data and establish a stable data transmission channel to ensure that the data can reach the data processing center accurately. For example, encryption and decryption software can be set up on the mobile phone and the data processing center to encrypt and decrypt the biomarker data to be transmitted.

[0041] It should be noted that the machine learning algorithm is used on the biomarker data. After the data processing center receives the biomarker data, it uses the machine learning algorithm to analyze it. The specific steps include the following:

[0042] Step S210: preprocessing the data, including removing outliers and standardizing the data.

[0043] Step S220: Randomly select and run a machine learning algorithm for measurement.

[0044] Machine learning algorithms include support vector machines (SVM) and random forests.

[0045] It is understandable that, taking the random forest algorithm as an example, it constructs multiple decision trees to perform classification and regression analysis on biomarker data to predict the risk level of renal fibrosis in patients.

[0046] It is understandable that randomly selecting an algorithm can combine the measurement results of the two algorithms and help improve the accuracy and reliability of clinical diagnostic results.

[0047] Step S230: Establish a risk assessment model for renal fibrosis based on the measurement results of the machine learning algorithm.

[0048] It should be noted that the risk assessment model can give corresponding risk assessment results based on the patient's real-time biomarker data.

[0049] Risk assessment results include low risk, medium risk, or high risk.

[0050] It is understandable that the algorithm will continuously learn and adjust model parameters, and be trained based on a large amount of existing biomarker data and clinical diagnosis results of patients with renal fibrosis to improve the accuracy and reliability of the model.

[0051] Step S300: formulate a personalized prevention and control plan based on the risk assessment results, and monitor the implementation of the prevention and control plan in real time.

[0052] In this step, personalized prevention and treatment plans include drug treatment, dietary adjustment and exercise intervention.

[0053] It is understandable that a personalized prevention and treatment plan should be developed, including:

[0054] If the risk assessment result is low risk, patients are advised to undergo regular health checks and dietary adjustments. Dietary adjustments include increasing the intake of foods rich in antioxidants such as vitamins C and E to protect kidney cells from oxidative damage.

[0055] If the risk assessment result is medium risk, the patient is recommended to adjust diet, take medication and exercise therapy. Medication therapy includes drugs with antioxidant and anti-inflammatory effects. Exercise therapy includes moderate aerobic exercise and strength training every week. Through exercise therapy, the overall health of the body and the blood circulation of the kidneys can be enhanced.

[0056] If the risk assessment result is high risk, a strict and comprehensive prevention and treatment plan will be formulated for the patient, which includes the use of targeted anti-fibrosis drugs, strict control of protein and salt intake in the diet, and arrangement of professional rehabilitation exercises.

[0057] It is understandable that real-time monitoring of the implementation of the prevention and control plan includes:

[0058] Through wearable devices or self-reporting by patients, we can understand in real time whether patients are taking medication, adjusting diet and exercising according to the prevention and treatment plan. For example, wearable devices can monitor the patient's exercise steps, exercise intensity and exercise time to ensure that the patient meets the prescribed exercise requirements. For drug treatment, devices such as smart medicine boxes are used to remind patients to take medicine on time and record the patient's medication status. For dietary adjustments, patients can record their daily dietary intake through mobile phone applications, and medical staff can remotely monitor and give guidance to ensure that patients strictly follow the dietary plan. If it is found that the patient does not follow the prevention and treatment plan, communicate with the patient in a timely manner to understand the reasons and take appropriate measures to make adjustments.

[0059] Step S400: Regularly test the patient's blood sample indicators and combine them with biomarker data to obtain a comprehensive evaluation effect of the prevention and treatment of renal fibrosis.

[0060] In this step, the blood sample indicators are renal function indicators including serum creatinine and urea nitrogen.

[0061] It is understandable that regarding the collection and testing of blood samples, professional medical staff can be arranged regularly to collect blood samples from patients, generally on a weekly or monthly basis, depending on the patient's risk level and the progression of the disease. The collected blood samples are sent to the laboratory for testing, and the serum creatinine and urea nitrogen levels are accurately measured by biochemical analysis methods. Elevated serum creatinine levels may indicate impaired glomerular filtration function, and elevated urea nitrogen levels reflect abnormal renal excretion function. Analyze the changing trends of these renal function indicators during the prevention and treatment process. For example, if the serum creatinine level gradually decreases over a period of time, it means that kidney function may have improved and prevention and control measures may have played a positive role.

[0062] It should be noted that the comprehensive evaluation of the prevention and treatment effects of renal fibrosis is to combine the renal function indicators of blood tests with the urine biomarker data for comprehensive evaluation.

[0063] If the levels of biomarkers of collagen, TGF-β and fibronectin in urine decrease, and renal function indicators such as serum creatinine and urea nitrogen also tend to be normal, it means that the prevention and treatment of renal fibrosis is better and the structure and function of the kidney have been improved.

[0064] If the biomarker levels continue to rise and the renal function indicators do not improve significantly or even worsen, it is necessary to re-evaluate the effectiveness of the prevention and treatment plan and consider whether the treatment measures need to be adjusted. For example, it may be necessary to adjust the drug dosage, change the type of drug, or further strengthen diet and exercise interventions.

[0065] Step S500: Feedback the comprehensive evaluation results to the patient and medical staff, and adjust the personalized prevention and treatment plan according to the comprehensive evaluation results.

[0066] It is understandable that the comprehensive evaluation results can be fed back to patients and medical staff in an intuitive and easy-to-understand way. For patients, mobile applications can be used to display the progress of their renal fibrosis prevention and treatment in the form of charts, text descriptions, etc., so that patients can clearly understand their physical condition and treatment effects. For medical staff, detailed evaluation data and analysis reports can be provided through platforms such as the hospital's electronic medical record system so that medical staff can fully understand the changes in the patient's condition.

[0067] It should be noted that the adjustment of personalized prevention and treatment plans mainly includes:

[0068] If the personalized prevention and treatment is effective, the current prevention and treatment plan can be maintained, but the patient's condition must also be closely monitored to prevent recurrence of the disease.

[0069] If personalized prevention and control is not effective, the prevention and control plan needs to be adjusted in time.

[0070] It is understandable that if the patient's urine biomarker levels are found to be elevated, and renal function indicators such as serum creatinine and urea nitrogen also have a deteriorating trend, it may be necessary to further examine the patient's etiology, adjust the drug treatment plan, add new treatment methods, or strengthen health education and lifestyle intervention for patients to improve the prevention and treatment of renal fibrosis. At the same time, after adjusting the plan, monitor and evaluate again according to the above steps to form a dynamic closed-loop management process and continuously optimize the prevention and treatment strategy of renal fibrosis.

[0071] A monitoring system for the prevention and treatment of renal fibrosis, such as Figure 2 As shown, it includes a biosensor detector 1, a smart phone terminal 2, a cloud data processing center 3 and a medical data center 4.

[0072] The biosensor detector 1 is used to obtain biomarker data in a urine sample of a patient and send the biomarker data to a smart phone terminal 2 .

[0073] The smart phone terminal 2 is used to input self-reported data, obtain biomarker data from the biosensor detector 1, and send the self-reported data and biomarker data to the cloud data processing center 3.

[0074] The medical data center 4 is used to obtain the patient's blood sample indicators and send the blood sample indicators to the cloud data processing center 3.

[0075] The cloud data processing center 3 generates evaluation results and personalized prevention and treatment plans based on the biomarker data, and generates comprehensive prevention and treatment evaluation effects based on the blood sample indicators; and sends the evaluation results, personalized prevention and treatment plans, and comprehensive prevention and treatment evaluation effects to the smartphone 2 and the medical data center 4.

[0076] Biomarkers included collagen, transforming growth factor β (TGF-β), and fibronectin.

[0077] Blood samples were collected for renal function including serum creatinine and urea nitrogen.

[0078] It is understandable that, through the biosensor detector 1, the collected biomarker data is transmitted to the mobile phone in real time through wireless transmission technology (such as Bluetooth, Wi-Fi, etc.), and then transmitted to the cloud data processing center in real time through the mobile phone.

[0079] like Figure 3 As shown, the biosensor detector 1 further includes a latex housing 11 , a control device 12 , a metal joint 13 , a flexible suction nozzle 14 and a cap container 15 .

[0080] The latex shell 11 is used for hand-held operation. Urine can be sucked from the cap container 15 by squeezing the latex shell 11 forcefully.

[0081] One side of the latex shell 11 is provided with four recessed gripping grooves for gripping with four fingers of the palm.

[0082] The control device 12 is arranged on the latex housing 11, and includes a switch button, a measurement button and a display panel.

[0083] It can be understood that the biosensor detector 1 can be turned on or off by pressing the switch button, and the biomarker parameters of urine can be measured by pressing the measurement button, and the biomarker parameters can be displayed through the display panel.

[0084] The metal joint 13 is provided on the lower side of the latex housing 11 .

[0085] A flexible suction nozzle 14 is provided on the lower side of the metal joint 13 .

[0086] The cap container 15 is used to hold urine and can be detachably fixed to the metal joint 13 .

[0087] It can be understood that the patient holds his / her urine through the cap container 15, and operates the squeezing latex shell 11 to make the flexible suction nozzle 14 suck the patient's urine in the cap container 15 to the control device 12, and the control device 12 analyzes the biomarker components of the urine sample.

[0088] like Figure 4 As shown, the biosensor detector 1 further includes a first processor unit, a first communication unit, a first analysis unit, a first display unit and a key unit.

[0089] The smart phone terminal 2 includes a second processor unit, a second communication unit and an input and display unit.

[0090] The cloud data processing center 3 includes a third processor unit and a third communication unit.

[0091] The medical data center 4 includes a fourth processor unit, a fourth communication unit and a second analysis unit.

[0092] The first processor unit is used to perform preliminary calculations and sorting on the relevant signals of the biomarker indicators.

[0093] The first communication unit is wirelessly connected to the second communication unit to realize data sharing and interaction.

[0094] The first display unit is used to display the operation interface and biomarker index data.

[0095] The key unit is used for the patient to perform key operations on the biosensor detector 1 .

[0096] The first analysis unit is used to analyze the biomarker data in the patient's urine sample to generate a biomarker index.

[0097] It can be understood that through specific algorithms and scientific models, the content, type and other information of biomarkers related to renal fibrosis in urine samples can be accurately identified, and biomarker indicators can be generated, which are of great significance for evaluating the condition of renal fibrosis in patients.

[0098] The second processor unit is used for processing the data from the input and display unit and the tasks related to the second communication unit.

[0099] It is understandable that the self-reported data input by the patient through the input and display unit is collated and preliminarily processed to meet the requirements of transmission and further analysis, while coordinating the communication between the second communication unit and other devices to ensure smooth and stable data transmission.

[0100] The second communication unit is used to receive the biomarker data from the biosensor detector 11, and send the sorted patient self-report data and the received biomarker data to the cloud data processing center 3 to realize the flow of data in the entire monitoring system.

[0101] The input and display unit is used for patients to input self-reported data and to display system feedback information to patients.

[0102] It is understandable that patients enter self-reported data, such as their own symptoms, daily diet, medication, and other information that may be related to renal fibrosis.

[0103] It is understandable that the information fed back by the monitoring system, such as the preliminary interpretation of the test results, health advice, etc., is displayed to the patient, and it is also convenient for the patient to review and confirm when entering the self-reported data.

[0104] The third processor unit is used to receive and process a large amount of data from the smart phone terminal 2 for in-depth processing and analysis.

[0105] It is understandable that by combining big data and advanced algorithms, a comprehensive analysis of biomarker data and patient self-reported data can be conducted to explore the potential patterns and trends therein in order to more accurately assess the status and development trend of renal fibrosis in patients.

[0106] The third communication unit is used for data interaction with the smart phone terminal 2 and the medical data center 4.

[0107] It is understandable that the data uploaded by the smart phone 2 is received and the processed result data is sent to the medical data center 4 so that medical staff can obtain comprehensive and accurate patient information and provide more powerful support for diagnosis and treatment.

[0108] The fourth processor unit is used to make a more comprehensive assessment of the patient's renal fibrosis condition.

[0109] The fourth communication unit maintains communication with the cloud data processing center 3 to ensure timely receipt of data.

[0110] It is understandable that the fourth communication unit may also communicate with other systems or devices within the hospital to achieve data sharing and integration, thereby improving the efficiency and quality of medical services.

[0111] The second analysis unit is used to analyze the blood data in the patient's blood sample and generate a blood sample index.

[0112] It can be understood that, unlike the biosensor detector 1 which analyzes biomarker data in urine samples, it generates blood sample indicators by analyzing blood data. These indicators reflect the patient's physical condition from another perspective, especially the physiological changes related to renal fibrosis, and provide medical staff with a more comprehensive diagnostic basis.

[0113] like Figure 5 As shown, the operating sequence of the renal fibrosis prevention and treatment monitoring system of the present invention is shown, which specifically includes:

[0114] At time T1, biomarker data in the patient's urine sample is collected in real time by the biosensor detector 1.

[0115] Biomarkers included collagen, transforming growth factor β (TGF-β), and fibronectin.

[0116] At time T2, the collected biomarker data are transmitted to the data processing center to establish a renal fibrosis risk assessment model for the patient.

[0117] The renal fibrosis risk assessment model is achieved by analyzing and processing biomarker data using machine learning algorithms at the data processing center.

[0118] At time T3, a personalized prevention and control plan is formulated based on the risk assessment results, and the implementation of the prevention and control plan is monitored in real time.

[0119] Personalized prevention and treatment plans include medication, dietary adjustments, and exercise interventions.

[0120] At T4, the patient's blood sample indicators are regularly tested and combined with urine biomarker data to obtain a comprehensive evaluation of the prevention and treatment of renal fibrosis.

[0121] Blood samples were collected for renal function including serum creatinine and urea nitrogen.

[0122] At T5, the comprehensive evaluation results will be fed back to patients and medical staff, and personalized prevention and treatment plans will be adjusted based on the comprehensive evaluation results.

[0123] Adjustment of personalized prevention and treatment plans mainly includes:

[0124] If the personalized prevention and treatment is effective, the current prevention and treatment plan can be maintained, but the patient's condition must also be closely monitored to prevent recurrence of the disease.

[0125] If personalized prevention and control is not effective, the prevention and control plan needs to be adjusted in time.

[0126] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0127] In an embodiment of the present invention, a computer device is provided, including a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor.

[0128] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or the corresponding software can be implemented through a hardware platform.

[0129] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the devices in the implementation scenario can be distributed in the devices of the implementation scenario according to the description of the implementation scenario, or can be changed accordingly and located in one or more devices different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0130] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A monitoring method for the prevention and treatment of renal fibrosis, used in a monitoring system for the prevention and treatment of renal fibrosis, characterized in that: The following steps are involved: Step S100: collecting biomarker data in a patient's urine sample in real time through a biosensor detector (1); Step S200: transmitting the collected biomarker data to a data processing center to establish a patient's renal fibrosis risk assessment model; Step S300: Formulate a personalized prevention and control plan based on the risk assessment results, and monitor the implementation of the prevention and control plan in real time; Step S400: Regularly testing the patient's blood sample indicators, and combining the biomarker data to obtain a comprehensive evaluation effect of the prevention and treatment of renal fibrosis; Step S500: Feedback the comprehensive evaluation results to the patient and medical staff, and adjust the personalized prevention and treatment plan according to the comprehensive evaluation results.

2. The method for monitoring the prevention and treatment of renal fibrosis according to claim 1, characterized in that: The collected biomarker data is transmitted to a data processing center to establish a patient's renal fibrosis risk assessment model, and further includes: The renal fibrosis risk assessment model is achieved by analyzing and processing the biomarker data using a machine learning algorithm at the data processing center; The machine learning algorithm includes any one of a support vector machine (SVM) and a random forest.

3. A monitoring method for preventing and treating renal fibrosis according to claim 2, characterized in that: The following steps are also included: Step S210: preprocessing the data, including removing outliers and standardizing the data; Step S220: randomly selecting and running a machine learning algorithm for measurement; Step S230: Establish a renal fibrosis risk assessment model based on the calculation results of the machine learning algorithm.

4. The method for monitoring the prevention and treatment of renal fibrosis according to claim 1, characterized in that: The biomarkers include collagen, transforming growth factor β (TGF-β) and fibronectin.

5. The method for monitoring the prevention and treatment of renal fibrosis according to claim 1, characterized in that: The blood sample indices are renal function indices including serum creatinine and urea nitrogen.

6. A monitoring method for preventing and treating renal fibrosis according to claim 1, characterized in that: The personalized prevention and treatment plan includes drug treatment, diet adjustment and exercise intervention.

7. The method for monitoring the prevention and treatment of renal fibrosis according to claim 3, characterized in that: The risk assessment model can provide corresponding risk assessment results based on the patient's real-time biomarker data; The risk assessment results include low risk, medium risk or high risk.

8. A monitoring system for preventing and treating renal fibrosis, characterized in that: It includes a biosensor detector (1), a smartphone (2), a cloud data processing center (3) and a medical data center (4); The biosensor detector (1) is used to obtain biomarker data in a patient's urine sample and send the biomarker data to the smart phone (2); The smartphone (2) is used to input self-reported data, obtain biomarker data from the biosensor detector (1), and send the self-reported data and the biomarker data to the cloud data processing center (3); The medical care data center (4) is used to obtain blood sample indicators of patients and send the blood sample indicators to the cloud data processing center (3); The cloud data processing center (3) generates an evaluation result and a personalized prevention and treatment plan based on the biomarker data, and generates a comprehensive prevention and treatment evaluation effect based on the blood sample index; and sends the evaluation result, the personalized prevention and treatment plan and the comprehensive prevention and treatment evaluation effect to the smart phone terminal (2) and the medical data center (4).

9. A monitoring system for preventing and treating renal fibrosis according to claim 8, characterized in that: The biosensor detector (1) also includes a latex housing (11), a control device (12), a metal joint (13), a flexible suction nozzle (14) and a cap container (15); The latex shell (11) is used for hand-held operation, and urine can be sucked from the cap container (15) by squeezing the latex shell (11) forcefully; One side of the latex shell (11) is provided with four recessed gripping grooves for gripping with four fingers of the palm; The control device (12) is arranged on the latex housing (11), and comprises a switch button, a measurement button and a display panel; The metal joint (13) is arranged on the lower side of the latex shell (11); The flexible suction nozzle (14) is arranged on the lower side of the metal joint (13); The cap container (15) is used to hold urine and can be detachably fixed to the metal joint (13).

10. A monitoring system for preventing and treating renal fibrosis according to claim 8, characterized in that: The biosensor detector (1) further comprises a first processor unit, a first communication unit, a first analysis unit, a first display unit and a key unit; The smart phone terminal (2) comprises a second processor unit, a second communication unit and an input and display unit; The cloud data processing center (3) comprises a third processor unit and a third communication unit; The medical care data center (4) comprises a fourth processor unit, a fourth communication unit and a second analysis unit; The first processor unit is used to perform preliminary calculations and sorting on the relevant signals of the biomarker index; The first communication unit is wirelessly connected to the second communication unit to achieve data sharing and interaction; The first display unit is used to display an operation interface and the biomarker index data; The key unit is used for the patient to perform key operations on the biosensor detector (1); The first analysis unit is used to analyze the biomarker data in the patient's urine sample to generate a biomarker index.

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