Liver cancer high-risk group full-life-cycle intelligent management method and system

Abstract

The invention is suitable for the technical field of liver cancer prevention and management, and particularly relates to a full-life-cycle intelligent management method and system for liver cancer high-risk crowds, and the method comprises the steps: receiving personal health data sent by a target user through a first terminal, and understanding the health background of the target user; according to a preset primary liver cancer diagnosis and treatment guide, matching the personal health data of the target user to obtain liver cancer high-risk group definition information of the target user, and determining whether the target user belongs to a liver cancer high-risk group; further formulating a personalized prevention scheme according to the liver cancer high-risk group definition information and the personal health data of the target user; the follow-up visit management is performed on the target user based on the personalized prevention scheme, and the health management state of the whole life cycle of the target user is updated in real time, so that the accuracy of health management is optimized, the enthusiasm of patients to participate in self health management is improved, the operability of intelligent management of liver cancer high-risk groups in the whole life cycle is improved, and the occurrence of disease risks is reduced.

Description

Technical Field

[0001] The present application belongs to the technical field of liver cancer prevention, treatment and management, and in particular, relates to an intelligent management method and system for the entire life cycle of a high-risk population for liver cancer. Background Art

[0002] Lifecycle management of people at high risk of liver cancer is crucial because it aims to reduce the risk of liver cancer or detect it in the early stages of the disease through a series of prevention, monitoring and early intervention measures, thereby improving cure rates and quality of life. Lifecycle management focuses not only on the treatment of the disease, but also on the control of risk factors and the adjustment of personal lifestyles to reduce the possibility of disease onset.

[0003] In the existing technology, the management of people at high risk of liver cancer relies on more traditional data collection and analysis methods. It is difficult to achieve true real-time tracking and updating of user status during follow-up management, which is not enough to promote efficient information exchange between doctors and patients. There is a lack of integrated intelligent tools to process personal health information in a timely and efficient manner, which limits patients' enthusiasm for participating in their own health management and leads to poor results in liver cancer prevention. Summary of the invention

[0004] The embodiments of the present application provide a method and system for intelligent management of the entire life cycle of people at high risk of liver cancer, which can solve the problem in the prior art of lack of integrated intelligent tools to timely and efficiently process personal health information, resulting in limited patients' enthusiasm for participating in their own health management and poor results in liver cancer prevention.

[0005] In a first aspect, the present application provides an intelligent management method for a high-risk group of liver cancer throughout their life cycle, including: Receiving personal health data sent by a target user through a first terminal; Matching the personal health data sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guidelines, classifying the target user, and obtaining liver cancer high-risk group definition information of the target user; Determining a personalized prevention plan for the target user based on the high-risk group definition information for liver cancer and the personal health data of the target user; Follow-up management is performed on the target user based on the personalized prevention plan of the target user to obtain a real-time management status of the target user throughout his or her life cycle.

[0006] The above technical solutions in the embodiments of the present application have at least the following technical effects: The intelligent management method for the entire life cycle of a high-risk group for liver cancer provided in the embodiment of the present application receives the personal health data sent by the target user through the first terminal to understand the health background of the target user. According to the preset primary liver cancer diagnosis and treatment guidelines, the personal health data of the target user is matched to obtain the target user's high-risk group for liver cancer definition information, and determine whether he belongs to the high-risk group for liver cancer. Based on the target user's high-risk group for liver cancer definition information and personal health data, a personalized prevention plan is further formulated to provide the target user with targeted and scientific health management suggestions. Follow-up management of the target user is carried out based on the personalized prevention plan, and the health management status of the target user throughout the life cycle is updated in real time, which optimizes the accuracy of health management, enhances the enthusiasm of patients to participate in their own health management, improves the operability of intelligent management of the entire life cycle of the high-risk group for liver cancer, and reduces the occurrence of disease risks.

[0007] In a second aspect, the present application embodiment provides an intelligent management system for the entire life cycle of a high-risk population for liver cancer, including: A receiving unit, configured to receive personal health data sent by a target user via a first terminal; A definition unit, configured to match the personal health data sent by the first terminal according to a preset primary liver cancer diagnosis and treatment guideline, classify the target user, and obtain liver cancer high-risk group definition information of the target user; A program unit, used for determining a personalized prevention program for the target user according to the high-risk group definition information of liver cancer and the personal health data of the target user; A follow-up unit is used to perform follow-up management on the target user based on the personalized prevention plan of the target user, and obtain a real-time management status of the target user throughout his or her life cycle.

[0008] In the third aspect, an embodiment of the present application provides an intelligent management device for the entire life cycle of people at high risk of liver cancer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method as described in any one of the above aspects when executing the computer program.

[0009] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product is run on an intelligent management device for the entire life cycle of a population at high risk of liver cancer, the intelligent management device for the entire life cycle of a population at high risk of liver cancer executes any of the methods described in the above aspects.

[0010] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions in the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 This is a flow chart of an intelligent management method for the whole life cycle of a high-risk population for liver cancer provided in one embodiment of the present application; Figure 2 This is a schematic diagram of the operation of the intelligent management method for the whole life cycle of a high-risk group of liver cancer provided by an embodiment of the present application; Figure 3 This is a schematic diagram of the structure of an intelligent management system for the entire life cycle of a high-risk population for liver cancer provided in one embodiment of the present application; Figure 4 It is a structural schematic diagram of an intelligent management device for the entire life cycle of a high-risk population for liver cancer provided in one embodiment of the present application. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0017] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0018] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0019] In the existing technology, the management of people at high risk of liver cancer relies on more traditional data collection and analysis methods. It is difficult to achieve true real-time tracking and updating of user status during follow-up management, which is not enough to promote efficient information exchange between doctors and patients. There is a lack of integrated intelligent tools to process personal health information in a timely and efficient manner, which limits patients' enthusiasm for participating in their own health management and leads to poor results in liver cancer prevention.

[0020] To solve the above problems, the embodiments of the present application provide a method and system for intelligent management of a high-risk group for liver cancer throughout their life cycle. In this method, the health background of the target user is understood by receiving the personal health data sent by the target user through the first terminal. According to the preset primary liver cancer diagnosis and treatment guidelines, the personal health data of the target user is matched to obtain the target user's high-risk group for liver cancer definition information, and determine whether he belongs to the high-risk group for liver cancer. Based on the target user's high-risk group for liver cancer definition information and personal health data, a personalized prevention plan is further formulated to provide the target user with targeted and scientific health management suggestions. Follow-up management is performed on the target user based on the personalized prevention plan, and the health management status of the target user throughout his life cycle is updated in real time, which optimizes the accuracy of health management, enhances the enthusiasm of patients to participate in their own health management, improves the operability of intelligent management of the high-risk group for liver cancer throughout their life cycle, and reduces the occurrence of disease risks.

[0021] The intelligent management method for the entire life cycle of people at high risk of liver cancer provided in the embodiment of the present application can be applied to the intelligent management equipment for the entire life cycle of people at high risk of liver cancer. At this time, the intelligent management equipment for the entire life cycle of people at high risk of liver cancer is the executor of the intelligent management method for the entire life cycle of people at high risk of liver cancer provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of the intelligent management equipment for the entire life cycle of people at high risk of liver cancer.

[0022] For example, the intelligent management equipment for the entire life cycle of people at high risk of liver cancer can be a mobile phone, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, desktop computer, smart large screen, smart TV, handheld device with wireless communication function, computing device or other processing device connected to a wireless modem, vehicle-mounted equipment, Internet of Vehicles terminal, computer, laptop computer, communication equipment, computing equipment, etc.

[0023] In order to better understand the intelligent management method for the whole life cycle of people at high risk of liver cancer provided in the embodiment of the present application, the specific implementation process of the intelligent management method for the whole life cycle of people at high risk of liver cancer provided in the embodiment of the present application is exemplarily introduced below.

[0024] Figure 1 A schematic flow chart of the intelligent management method for the whole life cycle of a high-risk population for liver cancer provided in an embodiment of the present application is shown. Figure 2 The operation flow chart of the intelligent management method for the whole life cycle of a high-risk group of liver cancer provided in an embodiment of the present application is shown. The intelligent management method for the whole life cycle of a high-risk group of liver cancer includes: S100, receiving personal health data sent by a target user via a first terminal.

[0025] It is understandable that the health data uploaded and sent by the target user through the first terminal (such as a smart phone, wearable device, etc.) can be received to obtain the personal health data of the target user. Personal health data may include personal health information (such as age, gender, height, weight, family history, etc.) and personal examination data obtained through regular physical examinations (such as blood pressure, blood sugar, blood lipids, liver function, CT images, B-ultrasound images, etc.). The transmission of personal health data can be completed through wireless communication technologies (such as Wi-Fi, Bluetooth, 4G / 5G).

[0026] S200, matching the personal health data sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guidelines, classifying the target users, and obtaining the liver cancer high-risk group definition information of the target users.

[0027] It is understandable that the health data of the target user can be matched with the preset primary liver cancer diagnosis and treatment guidelines, and the liver cancer risk of the target user can be defined according to the preset rules. Algorithms such as rule engines and decision trees can be used to accurately and efficiently match the target user based on factors such as hepatitis history, cirrhosis, and drinking, and to predict whether they are at high risk of liver cancer at an early stage, thereby reducing safety risks.

[0028] In a possible implementation, S200, personal health data sent by the first terminal is matched according to a preset primary liver cancer diagnosis and treatment guideline, target users are classified, and liver cancer high-risk group definition information of the target users is obtained, including: S210, matching the personal health data sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guidelines to obtain a user matching portrait of the target user.

[0029] It can be understood that personal health data (such as lifestyle, family history, disease history, liver function, CT images, B-ultrasound images, etc.) can be matched with liver cancer diagnosis and treatment guidelines to generate a user matching profile of the target user. The user matching profile is a comprehensive assessment tool generated by systematically analyzing the personal health information and personal examination data of the target user, combined with preset standards (such as diagnosis and treatment guidelines, disease prevention strategies, etc.). The user matching profile reflects the user's relative health status and risk level in liver cancer risk. A rule engine or decision tree model can be used to compare the user's personal health data with the matching rules in the guidelines to generate a user matching profile of the target user.

[0030] Optionally, the personal health data includes personal health information and personal examination data; S210, matching the personal health information sent by the first terminal according to a preset primary liver cancer diagnosis and treatment guideline to obtain a user matching portrait of the target user, including: S211, matching the personal health information sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guidelines to obtain first feature matching information of the target user; wherein the first feature matching information is used to reflect the matching status of the personal health information and the primary liver cancer diagnosis and treatment guidelines.

[0031] It can be understood that the personal health information of the target user can be analyzed in detail and matched with the diagnosis and treatment guidelines for primary liver cancer to generate the first feature matching information. The first feature matching information refers to the key information related to primary liver cancer extracted from the personal health information of the target user in the process of building a user matching portrait, and the result obtained by matching these key information with the preset diagnosis and treatment guidelines, which reflects the matching situation and degree of matching between personal health information and the diagnosis and treatment guidelines for primary liver cancer. The generation of the first feature matching information is based on the key features in the personal health information, such as age, gender, body mass index (BMI), eating habits, living habits (smoking, drinking), whether there is a relevant history of hepatitis, cirrhosis, etc. Each health feature will be compared with the diagnosis and treatment standards in the preset diagnosis and treatment guidelines for primary liver cancer, and a weight value will be assigned to each feature. Weighted scoring method or multivariate regression analysis can be used to score according to the feature value and standard rules. By assigning different weights to each feature, the degree of matching between personal health information and the diagnosis and treatment guidelines for primary liver cancer is quantified. The first feature matching information of the target user is a numerical output, which can help doctors or other medical systems evaluate the user's high-risk status for liver cancer.

[0032] S212, matching the personal examination data sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guidelines to obtain second feature matching information of the target user; wherein the second feature matching information is used to reflect the matching status of the personal examination data with the primary liver cancer diagnosis and treatment guidelines.

[0033] It can be understood that a second feature matching information reflecting the risk of liver cancer can be generated by matching and analyzing the personal examination data of the target user. According to the personal examination data, including alpha-fetoprotein (AFP) test results and imaging examination results, it is compared with the standards in the primary liver cancer diagnosis and treatment guidelines. The AFP test results are used to evaluate the level of biomarkers for liver cancer, while imaging examinations further understand the health of the liver by evaluating abnormal lesions in the liver, such as tumors or signs of cirrhosis. Combining the AFP data with the imaging data will calculate a matching score that reflects the degree of compliance between the target user's examination results and the high-risk standards defined in the primary liver cancer diagnosis and treatment guidelines. If the AFP value exceeds the normal range or the imaging results show obvious lesions in the liver, a higher matching score will be obtained, indicating that the target user may have a higher risk of liver cancer. The generated second feature matching information includes the comprehensive evaluation results of the AFP data and imaging data. Through the second feature matching information, the risk of liver cancer can be accurately and effectively assessed, so that more accurate health management and prevention work can be carried out to reduce the risk of liver cancer.

[0034] S213: Fusing the first feature matching information and the second feature matching information of the target user to obtain a user matching portrait of the target user.

[0035] It can be understood that the first feature matching information reflects the matching of personal health information with the preset primary liver cancer diagnosis and treatment guidelines, while the second feature matching information reflects the matching of personal examination data with the diagnosis and treatment guidelines. By performing feature fusion on the first feature matching information and the second feature matching information, health behaviors and physiological states (such as living habits, body mass index, etc.) can be combined with biomarkers (such as AFP levels, imaging examination results) to obtain a comprehensive matching result, that is, a user matching portrait. A weighted fusion method can be used to assign different weight values ​​according to the relevance and importance of each feature. Weighted fusion can form a comprehensive score or matching index to reflect the high-risk status of liver cancer of the target user. The technical implementation of feature fusion can include weighted average, linear regression, logistic regression and other algorithms, which can quantify and calculate the final matching result according to the matching degree of the features. For example, if the personal health information and personal examination data of the target user have high scores respectively, the fused user matching portrait will show that the user has a high risk of liver cancer; conversely, if the score is low, it indicates that the user has a low risk of liver cancer. The user matching portrait obtained through fusion can help us understand the patient's health status and potential risks, and also provide more accurate data support for personalized treatment and management plans.

[0036] S220, classifying the target user according to the user matching portrait of the target user, and obtaining the high-risk group definition information of liver cancer for the target user.

[0037] It can be understood that the user matching portrait is a comprehensive health profile that covers various health indicators and test results of the target user, such as age, gender, lifestyle habits (such as drinking, smoking), body mass index (BMI), medical history of hepatitis or cirrhosis, and examination data (such as AFP value, imaging examination results, etc.). The user matching portrait can be regarded as a comprehensive reflection of the health status of the target user. On this basis, a classification threshold can be set based on the specific score of the user matching portrait. The classification threshold is determined based on the high-risk standards, historical data, and medical statistical analysis listed in the primary liver cancer diagnosis and treatment guidelines. For example, the threshold of the user matching portrait is A. If the value of the user matching portrait of the target user is greater than or equal to A, the target user is determined to be a "high-risk group for liver cancer". If the value of the user matching portrait is less than A, the target user is determined to be a "non-high-risk group for liver cancer". Finally, based on a clear, quantified user matching portrait and a standardized evaluation system, an accurate liver cancer risk assessment is provided for the target user.

[0038] S300 determines the personalized prevention plan for the target user based on the target user’s high-risk liver cancer population definition information and personal health data.

[0039] It can be understood that a personalized prevention plan can be tailored for the target user based on the target user's health status, liver cancer risk level and specific health data. The target user's liver cancer high-risk population definition information includes the liver cancer high-risk population, i.e., the first definition information, or the non-liver cancer high-risk population, i.e., the second definition information, which reflects the target user's health risk level. On this basis, a personalized prevention plan can be designed for the target user by analyzing the target user's personal health data to reduce the probability of liver cancer or reduce its risk of progression.

[0040] In a possible implementation, the liver cancer high-risk population definition information includes the first definition information or the second definition information; S300, according to the liver cancer high-risk population definition information and personal health data of the target user, determining a personalized prevention plan for the target user, including: S310, when the high-risk group definition information for liver cancer of the target user is the first definition information, a first user prevention portrait of the target user is obtained according to the personal health data of the target user.

[0041] It can be understood that when the definition information of the high-risk group for liver cancer is the first definition information, it means that the health status of the target user has reached a certain degree of high-risk standard for liver cancer. The high-risk standard for liver cancer may include the user's health indicators (such as high AFP value, abnormal liver imaging, history of chronic hepatitis, etc.), indicating that the target has a high risk of liver cancer, so special attention should be paid to health management and intervention measures. Through in-depth analysis of the personal health data of the target user, including but not limited to age, gender, weight, eating habits, smoking, chronic liver disease, etc., and based on existing medical examination results (such as liver function tests, AFP values, imaging examination results, etc.), a first user prevention portrait that comprehensively reflects the liver cancer risk of the target user can be constructed. According to the matching of personal health data with the primary liver cancer diagnosis and treatment guidelines, the matching results of each health indicator and the indicator standard can be extracted to generate the "matching status" of each indicator, that is, whether the health indicator meets the high-risk standard, and gradually build the first user prevention portrait. The matching results of various health indicators can be organized into a comprehensive image to reflect the user's liver cancer risk. The matching status of each indicator can be directly reflected in the prevention portrait, indicating the risk status of each health indicator under the high-risk standard of liver cancer. For example, the first user prevention portrait of the target user may include: [Age: 45 years old (high-risk age group); Gender: Male (males are at higher risk); BMI: 28 (overweight, increases risk of fatty liver and liver cancer); Lifestyle habits: drinking twice a day and smoking 10 cigarettes a week (drinking and smoking are risk factors for liver cancer); History of chronic liver disease: history of chronic hepatitis (chronic hepatitis is an important cause of liver cancer); AFP value: 150 ng / mL (higher than normal, indicating an increased risk of liver cancer); Imaging examination results: mild fatty liver (potential liver disease, increased risk of liver cancer); Liver function: AST = 45 U / L, ALT = 50 U / L (abnormal liver function, indicating liver damage);] The first user prevention portrait extracts specific health data to display the target user's liver cancer risk profile, highlighting their high-risk indicators for liver cancer risk, which helps to develop personalized intervention plans.

[0042] S320, determining a first personalized prevention plan for the target user based on the first user prevention portrait of the target user and the preset primary liver cancer prevention and treatment guidelines.

[0043] It can be understood that the first user prevention portrait of the target user can be compared with the preset primary liver cancer prevention and treatment guidelines, and specific preventive measures can be formulated according to certain predetermined rules to determine the first personalized prevention plan for the target user. The first personalized prevention plan is a set of prevention measures for people at high risk of liver cancer customized for the target user based on the personal health data of the target user. The predetermined rule is to establish a mapping relationship between the sample personal health data and the preset liver cancer prevention and treatment guidelines in advance. According to the first user prevention portrait and the predetermined rules, each health indicator of the target user's liver cancer risk can be mapped one by one, and a personalized prevention plan, that is, the first personalized prevention plan, can be provided, which helps to reduce the risk of liver cancer. For example, based on the health data provided by the first user's prevention portrait, a personalized liver cancer prevention plan can be formulated: Dietary recommendations: control weight, improve diet structure, reduce high-fat and high-sugar foods, increase intake of vegetables and fruits, and avoid excessive drinking; Regular check-ups: Check AFP values ​​every three months and liver imaging tests (such as ultrasound, CT scans, etc.) every six months Lifestyle intervention: Suggest quitting smoking and reducing drinking, especially reducing the number of drinks per week Chronic hepatitis management: Antiviral therapy (if hepatitis B or C) and monitoring of liver function and liver transaminase levels every six months In a possible implementation, S300 determines a personalized prevention plan for the target user based on the target user's high-risk liver cancer population definition information and personal health data, including: S330, when the high-risk group definition information for liver cancer of the target user is the second definition information, a second user prevention portrait of the target user is obtained according to the personal health data of the target user.

[0044] It can be understood that when the target user's high-risk liver cancer population definition information is the second definition information, it means that the target user's health status does not meet the standards of the high-risk liver cancer population and is at a lower liver cancer risk level. Potential risks can be assessed and health management can be performed based on their personal health data. A second user prevention portrait is constructed based on the user's personal health data (such as weight, eating habits, family history, whether there is chronic liver disease, etc.) and examination results (such as AFP value, imaging results, liver function, etc.). The second user prevention portrait reflects the health status of the user in a low-risk group, but still requires certain attention. Through personal health data, users can be provided with a moderate health management plan to prevent the occurrence of liver cancer.

[0045] S340, determining a second personalized prevention plan for the target user based on the second user prevention portrait of the target user and the preset primary liver cancer prevention and treatment guidelines.

[0046] It can be understood that the second user prevention portrait of the target user can be compared with the preset primary liver cancer prevention and treatment guidelines, and specific preventive measures can be formulated according to certain predetermined rules to determine the second personalized prevention plan for the target user. The second personalized prevention plan is a set of prevention measures for non-liver cancer high-risk groups customized for the target user based on the personal health data of the target user. The predetermined rule is to establish a mapping relationship between the sample personal health data and the preset liver cancer prevention and treatment guidelines in advance. According to the second user prevention portrait and the predetermined rules, each health indicator of the target user's liver cancer risk can be mapped one by one, and a personalized prevention plan, that is, the second personalized prevention plan, can be provided, which helps to reduce the potential risk of liver cancer.

[0047] S400: Follow-up management is performed on the target user based on the personalized prevention plan of the target user to obtain the real-time management status of the target user throughout his / her life cycle.

[0048] It is understandable that the target user can be followed up and managed through the personalized prevention plan of the target user, the real-time management status of the target user throughout the life cycle can be obtained and updated, the user's health status can be continuously monitored, the prevention plan can be dynamically adjusted, and health risks can be effectively managed based on real-time data. The real-time management status of the whole life cycle reflects the health dynamics of the target user throughout the health management process, including the implementation of health behaviors, regular physical examination results, changes in health risks, etc. The real-time status of the whole life cycle can provide data support for subsequent health management decisions, and can promptly detect potential health problems or risks and make timely responses.

[0049] In a possible implementation, S400 performs follow-up management on the target user based on the personalized prevention plan of the target user to obtain the real-time management status of the target user throughout the life cycle, including: S410, sending a personalized prevention plan for a target user to a first terminal, and receiving user selection information sent by the target user through the first terminal.

[0050] It is understandable that the formulated personalized prevention plan can be sent to the target user of the first terminal (e.g., mobile phone, health management platform, etc.). The personalized prevention plan can include specific guidance on diet, exercise, health check-up, etc. The target user can choose according to his or her own situation, make adjustments or confirm the specific implementation plan of the prevention plan. By accepting the selection information returned by the target user through the first terminal, including whether to accept certain measures, whether certain measures need to be adjusted, etc., the target user can understand and make appropriate choices according to his or her actual situation, so that the subsequent steps can be accurately promoted.

[0051] S420: Determine a personalized prevention plan for the target user according to the user selection information sent by the target user through the first terminal.

[0052] It is understood that after receiving the user selection information of the target user, the personalized prevention plan can be adjusted and confirmed based on the user's feedback to form a final personalized prevention plan. Dynamically generating appropriate plans based on user selections may involve adjusting health behaviors, modifying health check cycles, or other intervention measures. For example, if the user chooses to reduce the number of certain high-risk behaviors (such as quitting smoking, reducing drinking) and modifying the check cycle from 3 months to 6 months. The personalized prevention plan should reflect the practical feasibility of the prevention needs of the target user and be flexible in actual implementation.

[0053] S430, performing follow-up management on the target user according to the personalized prevention plan of the target user, and obtaining the real-time management status of the target user throughout the life cycle.

[0054] It can be understood that according to the personalized prevention plan of the target user and through follow-up management, reminders of due check-ups can be sent regularly, the health status, behavioral changes and implementation of preventive measures of the target user can be tracked regularly to obtain feedback data, and the real-time management status of the target user's entire life cycle can be generated based on the feedback data.

[0055] Optionally, S430, follow-up management is performed on the target user according to the personalized prevention plan of the target user to obtain the real-time management status of the target user throughout the life cycle, including: S431, determining the health behavior cycle and health checkup cycle of the target user according to the personalized prevention plan of the target user.

[0056] It can be understood that the health behavior cycle refers to the time arrangement and frequency of health behaviors (such as exercise, diet adjustment, smoking cessation, alcohol cessation, etc.) set for the target user. The health check cycle refers to the time interval for health checks (such as liver function, AFP examination, imaging examination, etc.) set for the target user. The corresponding information can be extracted from the personalized prevention plan of the target user to set the health behavior cycle and health check cycle for the target user.

[0057] S432: Send a health behavior reminder to the first terminal based on the health behavior cycle of the target user, and receive health behavior feedback information sent by the target user through the first terminal.

[0058] It can be understood that based on the target user's health behavior cycle (such as regular exercise, diet adjustment, smoking cessation and alcohol cessation, etc.), a health behavior reminder can be sent to the target user's first terminal (such as a smart phone, a health management device) according to a predetermined cycle (such as weekly, monthly). The content of the reminder may include the health behavior that the user should perform, such as regular exercise, healthy diet advice, reducing high-risk behaviors (such as smoking cessation, alcohol cessation), etc. The health behavior reminder is consistent with the personalized prevention plan needs of the target user. After receiving the health behavior reminder, the target user can provide feedback on the first terminal, such as confirming whether the health behavior has been completed, or providing status information about the health behavior (such as "exercise completed" or "failed to stick to the diet plan today"). Feedback information on whether the health behavior sent by the target user through the first terminal is completed, that is, health behavior feedback information, and the user's health behavior record is updated according to the health behavior feedback information to track whether the user performs the health behavior as planned.

[0059] S433: Send a health check reminder to the first terminal based on the health check cycle of the target user, and receive health check feedback information sent by the target user through the first terminal.

[0060] It can be understood that according to the health check cycle of the target user (such as every three months, every six months, etc.), a health check reminder can be sent to the first terminal at a predetermined time point. The health check reminder can include examination items that are about to expire, such as AFP blood test, liver imaging examination, etc. According to the user's personalized health management plan, the examination items related to their liver cancer risk are pushed. For example, for users at high risk of liver cancer, the reminder content can particularly emphasize regular AFP testing and liver imaging examinations. The health check reminder can include information such as the specific time, location, and related preparations for the examination to ensure that the user can perform the examination on time and effectively. After the target user completes the health check, the first terminal can provide relevant health check feedback information, such as uploading a test report and providing examination data (such as AFP value, imaging report, etc.). The health check feedback information needs to include the latest alpha-fetoprotein data and imaging examination data of the target user. The health check results or reports provided by the target user through the first terminal can be received and analyzed to evaluate the changes in the health status of the target user. According to the health check feedback information, the results of the health check can be automatically recorded, and data support can be provided for the next step of health management (such as adjusting health behavior and examination cycle).

[0061] S434, obtaining the real-time management status of the target user throughout his / her life cycle based on the health checkup feedback information and health behavior feedback information of the target user. It can be understood that the feedback information of health check-ups and health behaviors can be combined to form a real-time management status of the entire life cycle, indicating the current health status of the target user, the implementation of the prevention plan, and his / her progress in overall health management. The real-time management status of the entire life cycle is presented as a dynamic health data file, which can reflect whether the target user has achieved the expected health management goals and whether it is necessary to adjust health behaviors, health check-up cycles or other management measures.

[0062] Exemplarily, the health check feedback information includes alpha-fetoprotein data and imaging examination data; S434, according to the health check feedback information and health behavior feedback information of the target user, obtain the real-time management status of the target user's entire life cycle, including: S4341, performing first data identification on the alpha-fetoprotein data to obtain a first data classification result of the target user.

[0063] It is understandable that the target user's alpha-fetoprotein (AFP) data can be compared and analyzed to identify the key features of the data and graded according to the alpha-fetoprotein standard in the primary liver cancer diagnosis and treatment guidelines. As an important biomarker for liver cancer, the concentration change of AFP is crucial for the early detection of liver cancer. The value of AFP can be divided into different risk levels according to different ranges: Normal range: AFP values ​​are within the normal range, indicating that there is no abnormality in the liver.

[0064] Increased range: A significantly increased AFP value may indicate liver disease (such as liver cancer or hepatitis).

[0065] By performing standardized analysis on the AFP data, an alpha-fetoprotein data classification result is obtained, namely the first data classification, which reflects the liver cancer risk level corresponding to the AFP data.

[0066] S4342, performing second data identification on the health behavior feedback information to obtain a second data classification result of the target user.

[0067] It can be understood that the health behavior feedback information includes the target user's implementation of health behaviors, such as whether they maintain a proper diet, whether they exercise regularly, whether they smoke, whether they drink, etc. The health behavior feedback information can be statistically analyzed to evaluate the health behavior performance of the target user, and can be divided into different levels (such as good health behavior, need to improve or poor). For example: Good: The user actively engages in healthy behaviors, such as regular exercise and a healthy diet.

[0068] Moderate: The user has some problems with health behaviors, but has made some improvements.

[0069] Poor: The user fails to adhere to healthy behavior recommendations and may have high-risk lifestyle habits (e.g., smoking, excessive drinking).

[0070] Through the identification and analysis of health behavior feedback information, a data classification result of the behavior feedback can be obtained, that is, a second data classification result, which reflects the health behavior risk of the target user.

[0071] S4343, use a preset primary liver cancer image classification model to process the image examination data to obtain image classification results of the target user's image examination data; wherein the primary liver cancer image classification model uses sample image examination data as expected input and image classification results corresponding to the sample image examination data as expected output, and trains an initial machine learning model to obtain the model; the sample image examination data includes at least one of B-ultrasound images and CT images.

[0072] It can be understood that clearly labeled B-ultrasound images and CT image samples such as B-ultrasound, CT, and MRI images can be obtained from liver cancer patients, and the corresponding categories (for example: liver cancer, liver cysts, normal, etc.) can be labeled. The sample data can contain normal, suspicious lesions, and clear liver cancer grading and other labeled information. Deep learning technology (such as convolutional neural network, CNN) can be used to train the model with sample image examination data as the expected input and the image classification results corresponding to the image examination data as the expected output. The classification accuracy can be improved and the overfitting risk can be reduced by adjusting hyperparameters (such as learning rate, batch size, etc.) to obtain a primary liver cancer image classification model. The image examination data of the target user can be input into the primary liver cancer image classification model, and the primary liver cancer image classification model automatically extracts image features (such as morphological features of the liver area, grayscale change patterns, etc.), and classifies the images based on the features, and outputs the image classification results corresponding to the image examination data of the target user. The image classification results can include multiple categories (such as normal images, suspected lesion images, early liver cancer images, late liver cancer images, etc.). The image classification results can provide a basis for subsequent analysis, improve diagnostic efficiency, and reduce manual errors.

[0073] S4344, based on the first data classification result, the second data classification result and the image classification result, obtain a user follow-up portrait of the target user.

[0074] It can be understood that the user follow-up portrait is a personalized health risk description and life status management file generated for the target user by analyzing multi-dimensional health data. The user follow-up portrait integrates the target user's health data classification results, behavioral feedback information and imaging examination results, and comprehensively reflects the user's health status and potential risks. The first data classification results, second data classification results and imaging classification results of the target user can be integrated and processed through a specific algorithm to extract health risk characteristics and generate a user follow-up portrait. User follow-up portraits can be produced by introducing rule mapping, statistical modeling and other methods, providing a scientific basis for early detection and intervention of diseases.

[0075] Exemplarily, the user follow-up portrait may be, for example: [First data classification results: Raise range Second data classification results: good Image classification results: Normal image S4345, based on the user follow-up portrait of the target user, obtain the real-time management status of the target user throughout his / her life cycle. It can be understood that the integrated user follow-up portrait can be used to comprehensively evaluate the current health status of the target user and provide real-time dynamic feedback for their subsequent health management. Based on the content of the user follow-up portrait, a real-time comprehensive description of the life cycle of a specific target user is generated, that is, the real-time management status of the entire life cycle. The real-time management status of the entire life cycle reflects the current health status, and the recommendations and key points for future health management (such as the cycle of the next check-up, health behaviors that need to be improved, changes in risk factors, etc.). The determination of the real-time management status of the entire life cycle is a continuously updated process, which will be continuously adjusted over time as user behavior and health indicators change. The real-time management status of the entire life cycle can provide personalized health management services to ensure that users can obtain timely health guidance and reduce potential health risks throughout the life cycle of liver cancer prevention and diagnosis and treatment.

[0076] Exemplarily, at S4345, according to the user follow-up portrait of the target user, the real-time management status of the target user throughout the life cycle is obtained, including: S43451, obtaining the first follow-up opinion of the target user based on the user follow-up portrait of the target user.

[0077] It is understandable that the user follow-up portrait of the target user can be compared with a standardized preset health management framework, which may include medical guidelines and historical data models. By comparing the user follow-up portrait with the standardized preset health management framework, a follow-up opinion, i.e., a first follow-up opinion, can be generated based on the user follow-up portrait of the target user. The first follow-up opinion may include specific health management suggestions for the user's personalized health status, such as whether further examinations, adjustments to lifestyle habits, or additional health intervention measures are needed.

[0078] S43452, sending the target user's personal health data, user follow-up portrait, and first follow-up opinion to the second terminal, and receiving the second follow-up opinion from the second terminal.

[0079] It can be understood that the health data of the target user, the user follow-up portrait and the first follow-up opinion can be transmitted to the second terminal, and the second follow-up opinion fed back by the second terminal can be received. The personal health data of the target user (such as alpha-fetoprotein level, imaging examination results, health behavior data, etc.) and the user follow-up portrait (overview of health status after comprehensive analysis) can be transmitted to the second terminal through an encrypted communication protocol. The user of the second terminal can be a doctor, a health manager, or a doctor. The user of the second terminal can further analyze the health status of the target user through the second terminal. After receiving the health information of the target user, the user of the second terminal (such as a doctor or a health expert) can make a more professional evaluation and suggestion based on the health data, user follow-up portrait and the first follow-up opinion of the target user, that is, the second follow-up opinion. The second follow-up opinion can include further adjustments to health intervention measures, whether to conduct new examinations or treatment suggestions, etc., so that health data and suggestions flow between different terminals, providing efficient interactive support and enhancing user participation and enthusiasm.

[0080] S43453: According to the second follow-up opinion of the second terminal, obtain the real-time management status of the target user throughout the life cycle and send the second follow-up opinion and the real-time management status of the target user throughout the life cycle to the first terminal.

[0081] It can be understood that after receiving the second follow-up opinion of the second terminal, the real-time management status of the target user's entire life cycle can be adjusted and updated. The second follow-up opinion is based on the user analysis of the second terminal, and may include suggestions for modification of the target user's health management plan, further examination requirements, or other lifestyle adjustment suggestions. According to the second follow-up opinion, the health status of the target user can be re-evaluated through the text extraction algorithm, and the updated real-time management status of the entire life cycle can be obtained by integrating multiple dimensions such as alpha-fetoprotein data, health behavior feedback, and image classification results. The real-time management status of the entire life cycle refers to a comprehensive description of the health status, risk level, management progress, and related suggestions of the target user that is dynamically evaluated and recorded based on real-time data and information feedback during the entire health management process of the target user. When the new full life cycle management status is generated, it can be sent to the first terminal together with the second follow-up opinion to show the latest health management information to the target user, so as to make corresponding adjustments or implement subsequent management strategies based on real-time data, optimize the accuracy of health management, enhance the enthusiasm of patients to participate in their own health management, and improve the operability of intelligent management of the entire life cycle of high-risk groups for liver cancer, thereby reducing the occurrence of disease risks.

[0082] Corresponding to the intelligent management method for the entire life cycle of people at high risk of liver cancer in the above embodiment, the embodiment of the present application also provides an intelligent management system for the entire life cycle of people at high risk of liver cancer. Each unit of the system can implement each step of the intelligent management method for the entire life cycle of people at high risk of liver cancer. Figure 3 A structural block diagram of an intelligent management system for the entire life cycle of a high-risk population for liver cancer provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0083] Reference Figure 3 The intelligent management system for the whole life cycle of high-risk population for liver cancer includes: A receiving unit, configured to receive personal health data sent by a target user via a first terminal; A definition unit, configured to match the personal health data sent by the first terminal according to a preset primary liver cancer diagnosis and treatment guideline, classify the target user, and obtain liver cancer high-risk group definition information of the target user; A program unit, used for determining a personalized prevention program for the target user according to the high-risk group definition information of liver cancer and the personal health data of the target user; A follow-up unit is used to perform follow-up management on the target user based on the personalized prevention plan of the target user, and obtain a real-time management status of the target user throughout his or her life cycle.

[0084] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0085] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit module can exist physically separately, or two or more unit modules can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0086] The present application also provides an intelligent management device for the whole life cycle of people at high risk of liver cancer. Figure 4 This is a schematic diagram of the structure of an intelligent management device for the whole life cycle of a high-risk group of liver cancer provided in one embodiment of the present application. Figure 4 As shown, the intelligent management device 6 for the whole life cycle of high-risk liver cancer population in this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown), at least one memory 61 ( Figure 4 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the intelligent management device 6 for the whole life cycle of people at high risk of liver cancer implements the steps in any of the above-mentioned embodiments of the intelligent management method for the whole life cycle of people at high risk of liver cancer, or implements the functions of each unit in the above-mentioned system embodiments.

[0087] Exemplarily, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program 62 in the intelligent management device 6 for the whole life cycle of a high-risk population for liver cancer.

[0088] The intelligent management device 6 for the whole life cycle of the high-risk population of liver cancer can be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The intelligent management device for the whole life cycle of the high-risk population of liver cancer can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 4 It is merely an example of the intelligent management device 6 for the whole life cycle of people at high risk of liver cancer, and does not constitute a limitation of the intelligent management device 6 for the whole life cycle of people at high risk of liver cancer. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, for example, it may also include input and output devices, network access devices, buses, etc.

[0089] The processor 60 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0090] In some embodiments, the memory 61 may be an internal storage unit of the intelligent management device 6 for the whole life cycle of a high-risk group of liver cancer, such as a hard disk or memory of the intelligent management device 6 for the whole life cycle of a high-risk group of liver cancer. In other embodiments, the memory 61 may also be an external storage device of the intelligent management device 6 for the whole life cycle of a high-risk group of liver cancer, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the intelligent management device 6 for the whole life cycle of a high-risk group of liver cancer. Further, the memory 61 may also include both an internal storage unit and an external storage device of the intelligent management device 6 for the whole life cycle of a high-risk group of liver cancer. The memory 61 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0091] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0092] An embodiment of the present application provides a computer program product. When the computer program product runs on an intelligent management device for the whole life cycle of a population at high risk of liver cancer, the intelligent management device for the whole life cycle of a population at high risk of liver cancer implements the steps in any of the above-mentioned method embodiments.

[0093] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the intelligent management device for the whole life cycle of people at high risk of liver cancer, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0094] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0095] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0096] In the embodiments provided in the present application, it should be understood that the disclosed intelligent management system for the entire life cycle of a high-risk group of liver cancer / the intelligent management device and method for the entire life cycle of a high-risk group of liver cancer can be implemented in other ways. For example, the above-described intelligent management system for the entire life cycle of a high-risk group of liver cancer / the intelligent management device for the entire life cycle of a high-risk group of liver cancer are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for intelligent management of the whole life cycle of a high-risk population for liver cancer, characterized in that: include: Receiving personal health data sent by a target user through a first terminal; Matching the personal health data sent by the first terminal according to a preset primary liver cancer diagnosis and treatment guideline, classifying the target user, and obtaining liver cancer high-risk group definition information of the target user; Determining a personalized prevention plan for the target user based on the high-risk group definition information for liver cancer and the personal health data of the target user; Follow-up management is performed on the target user based on the personalized prevention plan of the target user to obtain a real-time management status of the target user throughout his or her life cycle.

2. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 1, characterized in that: The personal health data sent by the first terminal is matched according to the preset primary liver cancer diagnosis and treatment guideline, the target user is classified, and the liver cancer high-risk group definition information of the target user is obtained, including: Matching the personal health data sent by the first terminal according to a preset primary liver cancer diagnosis and treatment guideline to obtain a user matching portrait of the target user; The target user is classified according to the user matching portrait of the target user to obtain the high-risk group definition information of liver cancer for the target user.

3. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 2, characterized in that: The personal health data includes personal health information and personal examination data; Matching the personal health information sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guideline to obtain a user matching profile of the target user includes: Matching the personal health information sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guidelines to obtain first feature matching information of the target user; wherein the first feature matching information is used to reflect the matching status of the personal health information and the primary liver cancer diagnosis and treatment guidelines; Matching the personal examination data sent by the first terminal according to the preset primary liver cancer diagnosis and treatment guidelines to obtain second feature matching information of the target user; wherein the second feature matching information is used to reflect the matching status of the personal examination data and the primary liver cancer diagnosis and treatment guidelines; The first feature matching information and the second feature matching information of the target user are feature-fused to obtain a user matching profile of the target user.

4. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 1, characterized in that: The liver cancer high-risk population definition information includes first definition information or second definition information; Determining a personalized prevention plan for the target user based on the high-risk liver cancer population definition information and the personal health data of the target user includes: When the liver cancer high-risk group definition information of the target user is the first definition information, obtaining a first user prevention portrait of the target user according to the personal health data of the target user; A first personalized prevention plan for the target user is determined based on the first user prevention portrait of the target user and the preset primary liver cancer prevention and treatment guidelines.

5. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 4, characterized in that: Determining a personalized prevention plan for the target user based on the high-risk liver cancer population definition information and the personal health data of the target user includes: When the liver cancer high-risk group definition information of the target user is the second definition information, obtaining a second user prevention portrait of the target user according to the personal health data of the target user; A second personalized prevention plan for the target user is determined based on the second user prevention portrait of the target user and the preset primary liver cancer prevention and treatment guidelines.

6. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 1, characterized in that: The follow-up management of the target user based on the personalized prevention plan of the target user to obtain the real-time management status of the target user throughout the life cycle includes: Sending the personalized prevention plan of the target user to the first terminal, and receiving user selection information sent by the target user through the first terminal; Determining a personalized prevention plan for the target user according to the user selection information sent by the target user through the first terminal; Follow-up management is performed on the target user according to the personalized prevention plan of the target user to obtain a real-time management status of the target user throughout his or her life cycle.

7. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 6, characterized in that: The following step of performing follow-up management on the target user according to the personalized prevention plan of the target user to obtain the real-time management status of the target user throughout the life cycle includes: Determining a health behavior cycle and a health checkup cycle of the target user according to the personalized prevention plan of the target user; Based on the health behavior cycle of the target user, sending a health behavior reminder to the first terminal, and receiving health behavior feedback information sent by the target user through the first terminal; Based on the health check cycle of the target user, sending a health check reminder to the first terminal, and receiving health check feedback information sent by the target user through the first terminal; The real-time management status of the target user throughout his / her life cycle is obtained based on the health check feedback information and the health behavior feedback information of the target.

8. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 7, characterized in that: The health check feedback information includes alpha-fetoprotein data and imaging examination data; The step of obtaining the real-time management status of the target user throughout his / her life cycle according to the health checkup feedback information and the health behavior feedback information of the target user includes: Performing first data identification on the alpha-fetoprotein data to obtain a first data classification result of the target user; Performing second data identification on the health behavior feedback information to obtain a second data classification result of the target user; The image examination data is processed using a preset primary liver cancer image classification model to obtain an image classification result of the image examination data of the target user; wherein the primary liver cancer image classification model uses sample image examination data as expected input and image classification results corresponding to the sample image examination data as expected output, and trains an initial machine learning model to obtain a model; the sample image examination data includes at least one of B-ultrasound images and CT images; Based on the first data classification result, the second data classification result and the image classification result, obtaining a user follow-up portrait of the target user; According to the user follow-up portrait of the target user, the real-time management status of the target user throughout its life cycle is obtained.

9. The intelligent management method for the whole life cycle of a high-risk population for liver cancer according to claim 8, characterized in that: The step of obtaining the real-time management status of the target user throughout the life cycle according to the user follow-up portrait of the target user includes: Obtaining a first follow-up opinion of the target user according to the user follow-up portrait of the target user; Sending the personal health data of the target user, the user follow-up portrait, and the first follow-up opinion to a second terminal, and receiving a second follow-up opinion from the second terminal; According to the second follow-up opinion of the second terminal, the real-time management status of the target user throughout the life cycle is obtained, and the second follow-up opinion and the real-time management status of the target user throughout the life cycle are sent to the first terminal.

10. An intelligent management system for the entire life cycle of people at high risk of liver cancer, characterized in that: include: A receiving unit, configured to receive personal health data sent by a target user via a first terminal; A definition unit, configured to match the personal health data sent by the first terminal according to a preset primary liver cancer diagnosis and treatment guideline, classify the target user, and obtain liver cancer high-risk group definition information of the target user; A program unit, used for determining a personalized prevention program for the target user according to the high-risk group definition information of liver cancer and the personal health data of the target user; A follow-up unit is used to perform follow-up management on the target user based on the personalized prevention plan of the target user, and obtain a real-time management status of the target user throughout his or her life cycle.

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