Post-diagnosis management system and method for hepatopathy patients

Abstract

The invention provides a post-diagnosis management system and method for a hepatopathy patient. The system comprises a data acquisition module used for acquiring multi-dimensional physical sign data and diagnosis and treatment historical data of the patient; the data storage module is used for performing standardized encryption, filing and storage on the multi-dimensional physical sign data and the diagnosis and treatment historical data; the data analysis module is used for analyzing the current hepatopathy condition of the patient based on the stored data to determine the current illness state of the patient, and predicting the disease progress of the patient according to the current illness state of the patient to obtain an illness state prediction result; and the application service module is used for intelligently adjusting the current prognosis treatment scheme of the patient based on the condition prediction result and the treatment guidance of the doctor, and supervising the treatment process of the patient according to the adjustment result. According to the invention, the medical data resources of the patient are integrated, and an intelligent, personalized and all-around post-diagnosis management system for the hepatopathy patient is constructed, so that the prognosis effect of the patient is improved, the medical burden is reduced, and the efficiency and quality of medical services are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of the intersection of artificial intelligence technologies, and particularly relates to a post-diagnosis management system and method for liver disease patients. Background Art

[0002] With the changes in modern lifestyle and the increase in the obese population, the incidence of metabolic associated fatty liver disease has been rising year by year and has become a global public health problem. In terms of the development of medical technology, the diagnostic technology for this disease has been continuously improving, from traditional liver function tests, ultrasound examinations to more accurate magnetic resonance elastography, liver biopsies, etc., but the post-diagnosis management link is relatively weak. Most of the existing medical management software currently focuses on hospital information management systems, electronic medical record systems, etc. These systems are mainly used for the management of the diagnosis and treatment processes within the hospital and have limited functions for the long-term management of patients after diagnosis. Although some health management APPs provide functions such as health records, exercise and diet suggestions, they lack professionalism and personalization for patients and cannot dynamically adjust the management plan according to the changes in the patients' conditions. For example, some general health management APPs only provide fixed diet templates without considering the special nutritional needs and individual differences of patients. In addition, although some telemedicine platforms can achieve remote communication between doctors and patients, they lack effective artificial intelligence algorithm support in the monitoring and prediction of patients' conditions and cannot timely detect the potential risk of deterioration of patients' conditions.

[0003] Traditional post-diagnosis management methods are difficult to meet the needs of patients. After discharge, patients often lack professional guidance and continuous supervision, and are prone to situations such as not taking medicine on time and not following diet and exercise suggestions. Moreover, doctors are difficult to obtain real-time data on the changes in patients' conditions at home and cannot adjust the treatment plan in a timely manner. Summary of the Invention

[0004] The present invention provides a post-diagnosis management system and method for liver disease patients, solves the above problems, integrates the medical data resources of patients, constructs an intelligent, personalized and all-round post-diagnosis management platform for liver disease patients, so as to improve the prognosis effect of patients, reduce the medical burden, and improve the efficiency and quality of medical services.

[0005] The present invention provides a post-diagnosis management system for liver disease patients, including:

[0006] A data acquisition module, configured to acquire multi-dimensional physical sign data and diagnosis and treatment history data of patients;

[0007] A data storage module, configured to perform standardized encrypted archival storage on the multi-dimensional physical sign data and diagnosis and treatment history data;

[0008] A data analysis module, which is used to analyze the current liver disease condition of a patient based on the stored data, determine the patient's current condition, predict the disease progression of the patient according to the patient's current condition, and obtain a disease condition prediction result;

[0009] An application service module, which is used to intelligently adjust the patient's current prognosis treatment plan based on the disease condition prediction result and the doctor's treatment guidance, and supervise the patient's treatment process according to the adjustment result.

[0010] Preferably, in a post-diagnosis management system for liver disease patients, the data collection module includes:

[0011] A patient feedback data collection unit, which is used to collect the self-perceived physical sign data manually input by the patient based on the patient-associated data topology network;

[0012] A self-examination physical sign data collection unit, which is used to collect the self-examination physical sign data of the patient's body detection on wearable devices and home medical detection devices based on the patient-associated data topology network;

[0013] A diagnosis record collection unit, which is used to collect the patient's diagnosis and treatment history data based on the patient-associated data topology network;

[0014] A data transmission unit, which is used to generate multi-dimensional physical sign data based on the self-perceived physical sign data and the self-examination physical sign data, and send the multi-dimensional physical sign data and the diagnosis and treatment history data to the data storage module.

[0015] Preferably, in a post-diagnosis management system for liver disease patients, the data collection module further includes:

[0016] A data collection network establishment unit, which is used to obtain the access rights to the relevant data corresponding to the current patient in the patient's handwritten system, hospital information system, electronic medical record system, and the data storage systems of wearable devices and home medical detection devices;

[0017] Based on the access rights, generate a corresponding local data communication protocol;

[0018] Based on the local data communication protocol, establish communication channels between the data collection module and the patient's handwritten system, hospital information system, electronic medical record system, and the data storage systems of wearable devices and home medical detection devices respectively, and generate a patient-associated data topology network.

[0019] Preferably, in a post-diagnosis management system for liver disease patients, the data storage module includes:

[0020] A data standardization unit, which is used to synchronize the data format standardization of the multi-dimensional physical sign data and the diagnosis and treatment history data based on the preset templates of various multi-dimensional physical sign data and the diagnosis and treatment history data, and obtain a patient's stage storage form;

[0021] An organizing storage unit is used to encrypt and archive the patient's phased storage form into the corresponding patient management data storage directory based on the time axis order.

[0022] Preferably, in a post - diagnosis management system for liver disease patients, the data standardization unit includes:

[0023] A stage division sub - unit is used to obtain the patient's prognosis treatment plan and determine the optimal post - diagnosis management analysis time interval for the patient based on the treatment course corresponding to the prognosis treatment plan.

[0024] A template generation unit is used to determine the collection frequency corresponding to various multi - dimensional physical sign data based on the data categories corresponding to various multi - dimensional physical sign data and in combination with the patient's current condition.

[0025] Based on the optimal post - diagnosis management analysis time interval, various multi - dimensional physical sign data types, and the collection frequency corresponding to various multi - dimensional physical sign data, a preset template for the patient's post - diagnosis management data is generated.

[0026] An intelligent filling sub - unit is used to identify the type of the received data, extract key data according to the identification result, and fill the extraction result into the corresponding position of the preset template.

[0027] Preferably, in a post - diagnosis management system for liver disease patients, the data storage module further includes:

[0028] An intelligent reminder unit is used to determine the latest collection time of each multi - dimensional physical sign data based on the collection frequency corresponding to various multi - dimensional physical sign data. If any multi - dimensional physical sign data arrives at the corresponding latest collection time and the corresponding type of multi - dimensional physical sign data is received, a reminder notice is sent to the patient - associated mobile terminal.

[0029] Preferably, in a post - diagnosis management system for liver disease patients, the data analysis module includes:

[0030] A first data analysis unit is used to obtain the stored data corresponding to the preset effective time, obtain the diagnostic changes of the patient's liver disease based on the diagnostic conclusions corresponding to the diagnosis and treatment history data in the stored data, determine the change trend of the patient's liver disease condition and the patient's latest diagnosed condition.

[0031] According to the patient's latest diagnosed condition, in combination with the standard value range corresponding to multiple physiological indexes corresponding to the patient's current age stage, predict the personal reference value range of the patient's current multiple physiological indexes.

[0032] A data intelligent tagging unit, which is used to compare the new physiological index data with its corresponding personal value range when the data storage module receives new physiological index data. When the new physiological index data is within its corresponding personal value range, a normal tag is added to the new physiological index data;

[0033] Otherwise, an abnormal tag is added to the new physiological index data;

[0034] A second data analysis unit, which is used to determine the abnormal frequency of each physiological index of the patient within a preset effective time based on the tags carried by various physiological indexes in the stored data;

[0035] According to the error rate between the abnormal physiological index data included in each type of physiological index and its corresponding personal value range, the average error rate of each physiological index is obtained respectively;

[0036] Based on the abnormal frequency and average error rate of each physiological index, the index abnormal rate of each physiological index is determined respectively;

[0037] A disease prediction unit, which is used to determine the weight allocation order of physiological indexes based on the latest diagnosed disease condition and in combination with the change speed of physiological indexes corresponding to different liver disease conditions, and obtain a descending allocation sequence;

[0038] According to the number of physiological indexes self-checked by the patient, a corresponding weight array is obtained in a preset database, and based on the allocation sequence and the weight array, the weights corresponding to each physiological index are allocated;

[0039] Based on the weight allocation result and the index abnormal rate of each physiological index, the monitoring index abnormal rate of the patient is obtained;

[0040] When the monitoring index abnormal rate is less than the lower limit of the preset abnormal floating interval, it is determined that the patient's condition has improved;

[0041] When the monitoring index abnormal rate is greater than the upper limit of the preset abnormal floating interval, it is determined that the patient's condition has deteriorated;

[0042] When the monitoring index abnormal rate is within the preset abnormal floating interval, it is determined that the patient's condition is stable.

[0043] Preferably, in a post-diagnosis management system for liver disease patients, an application service module includes:

[0044] A result sending unit, which is used to synchronously send the patient's abnormal physiological index data or disease prediction result to the patient's attending physician;

[0045] The first scheme adjustment analysis unit is used to evaluate the degree of the patient's condition deterioration based on the deviation rate between the abnormal rate of the monitoring indicators and the upper limit of the preset abnormal floating range when the patient's condition deteriorates. When the degree of the patient's condition deterioration is greater than the preset warning value, a medical warning is sent to the patient;

[0046] Otherwise, obtain the patient's self-perceived physical sign data within the preset effective time, determine the patient's medication situation and diet and exercise record situation, and based on the current prognosis treatment plan, judge whether the patient's condition deterioration is of the type of insufficient self-supervision;

[0047] If so, based on the degree of the patient's deterioration and the symptom description in the patient's self-perceived physical sign data, combined with the development process of the liver disease currently suffered by the patient, predict the current disease stage of the patient;

[0048] If not, send a medical warning to the patient;

[0049] The second scheme adjustment analysis unit is used to compare the symptom description in the patient's self-perceived physical sign data of the current effective time interval with the corresponding described symptoms of the previous effective time interval when the patient's condition improves, and obtain the alleviated symptoms and eliminated symptoms of the patient;

[0050] Based on the alleviated symptoms and eliminated symptoms of the patient, combined with the development process of the liver disease currently suffered by the patient, predict the current disease stage of the patient;

[0051] The intelligent adjustment confirmation unit is used to adjust the patient's medication dosage and diet and exercise requirements according to the current disease stage and the changes in the patient's physiological indicators, obtain a new prognosis treatment plan, and send it to the patient's attending physician for confirmation.

[0052] Preferably, in a post-diagnosis management system for liver disease patients, the intelligent adjustment confirmation unit includes:

[0053] The treatment comparison sub-unit is used to obtain the initial diagnosis and treatment history data of multiple liver disease patients, classify the initial diagnosis and treatment records based on the type of liver disease of the patients, obtain multiple groups of diagnosis record data, and cluster the groups of diagnosis record data according to the severity of the patients' conditions to obtain multiple data clusters;

[0054] Compare the various physiological indicators of the patients corresponding to the different initial diagnosis and treatment history data within the data cluster with their corresponding standard value ranges respectively, and obtain the index deviations corresponding to each physiological indicator;

[0055] Vectorize the physiological indicator data of the patients corresponding to the different initial diagnosis and treatment history data based on the index deviations to obtain an index deviation vector;

[0056] Compare the index deviations corresponding to the initial diagnosis and treatment history data within the same data cluster to obtain the similarity between different initial diagnosis and treatment history data;

[0057] Take any index deviation vector within the data cluster as the target vector, and cluster it according to a preset clustering interval based on the similarity between the target vector and the remaining index deviation vectors within the data cluster to obtain the vector cluster corresponding to the target vector;

[0058] Obtain the vector clusters corresponding to all index deviation vectors, compare the treatment plans within multiple initial diagnosis and treatment records corresponding to the same vector cluster to obtain the characteristics of plan formulation;

[0059] Compare the characteristics of plan formulation with the characteristics of plan formulation corresponding to the irrelevant vector clusters corresponding to the vector cluster to determine the first influence of the physiological index difference on the patient's treatment plan under different conditions of the same type of liver disease in the patient;

[0060] The plan adjustment subunit is used to determine the best treatment plan adjustment strategy for the patient's current condition under the current physiological index based on the current disease stage and the change of the patient's physiological index, combined with the first influence;

[0061] Adjust the patient's drug dosage and diet and exercise requirements based on the best treatment plan adjustment strategy to obtain a new prognosis treatment plan, and send it to the patient's attending physician for confirmation.

[0062] The present invention provides a method for post-diagnosis management of liver disease patients, including:

[0063] Collect the multi-dimensional physical sign data and diagnosis and treatment history data of the patient;

[0064] Standardize, encrypt and archive the multi-dimensional physical sign data and diagnosis and treatment history data for storage;

[0065] Analyze the patient's current liver disease condition based on the stored data to determine the patient's current condition, and predict the patient's disease progression according to the patient's current condition to obtain a disease prediction result;

[0066] Intelligently adjust the patient's current post-diagnosis treatment based on the disease prediction result and the doctor's treatment guidance, and supervise the patient's quality process according to the adjustment result.

[0067] Compared with the prior art, the present invention has at least the following beneficial effects:

[0068] The present invention collects multi-dimensional vital sign data and medical history data of patients through a data collection module, and sends them to a data storage module for standardized encryption and archiving storage of the multi-dimensional vital sign data and medical history data, realizing the integration of patients' medical data resources. Then, through a data analysis module, it analyzes the current liver disease condition of the patients based on the stored data, determines the current condition of the patients, predicts the disease progression of the patients according to the current condition of the patients, and obtains a disease prediction result, realizing the remote monitoring of the patients' conditions by doctors and providing a reliable basis for the timely adjustment of the treatment plan for the patients. And through an application service module, based on the disease prediction result and the doctor's treatment guidance, it intelligently adjusts the current prognosis treatment plan for the patients, realizes personalized services, ensures that the current treatment plan used by the patients has a high degree of matching with the current physical condition and current disease condition of the patients, provides a continuous possibility for the patients' recovery or improvement of the disease condition, and supervises the treatment process of the patients according to the adjustment result to ensure the effective implementation of the prognosis treatment plan after the patients are discharged, effectively improving the diagnosis and treatment effect.

[0069] Other features and advantages of the present invention will be described in the following specification, and, in part, will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structure specifically pointed out in this application document.

[0070] The technical solution of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0071] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0072] Figure 1 It is a structural diagram of a post-diagnosis management system for liver disease patients according to the present invention;

[0073] Figure 2 It is a structural diagram of a data collection module of a post-diagnosis management system for liver disease patients according to the present invention;

[0074] Figure 3 It is a structural diagram of a data storage module of a post-diagnosis management system for liver disease patients according to the present invention;

[0075] Figure 4 It is a structural diagram of a data analysis module of a post-diagnosis management system for liver disease patients according to the present invention;

[0076] Figure 5 It is a structural diagram of an application service module of a post-diagnosis management system for liver disease patients according to the present invention;

[0077] Figure 6This is a flowchart of a post - diagnosis management method for liver disease patients of the present invention. Detailed implementation manners

[0078] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for the purpose of illustrating and explaining the present invention, and are not used to limit the present invention.

[0079] Embodiment 1:

[0080] The present invention provides a post - diagnosis management system for liver disease patients, as Figure 1 shown, including:

[0081] A data acquisition module, which is used to acquire multi - dimensional physical sign data and medical treatment history data of patients;

[0082] A data storage module, which is used to perform standardized encrypted archiving storage on multi - dimensional physical sign data and medical treatment history data;

[0083] A data analysis module, which is used to analyze the current liver disease condition of patients based on the stored data, determine the current condition of patients, predict the disease progression of patients according to the current condition of patients, and obtain a disease condition prediction result;

[0084] An application service module, which is used to intelligently adjust the current prognosis treatment plan of patients based on the disease condition prediction result and the doctor's treatment guidance, and supervise the treatment process of patients according to the adjustment result.

[0085] In this embodiment, the multi - dimensional physical sign data includes self - perceived physical sign data and self - detected physical sign data. Among them, the self - perceived physical sign data is manually input data by patients, including but not limited to data such as diet records, exercise conditions, symptom feedback, actual drug taking conditions at home, etc.; the self - detected physical sign data is physical sign data obtained by users through wearable devices and home medical detection devices, including but not limited to physical sign data such as heart rate, blood pressure, blood sugar, weight, etc.

[0086] The medical treatment history data includes but not limited to data such as inspection reports, diagnosis results, treatment records, etc.

[0087] Advantages of the above technical solution: The present invention collects multi-dimensional physical sign data and medical history data of patients through a data collection module, and sends them to a data storage module for standardized encrypted archiving storage of the multi-dimensional physical sign data and medical history data, realizing the integration of patients' medical data resources. Then, through a data analysis module, based on the stored data, the current liver disease condition of the patient is analyzed to determine the patient's current condition. According to the patient's current condition, the disease progression of the patient is predicted to obtain a disease prediction result, realizing the remote monitoring of the patient's condition by doctors and providing a reliable basis for the timely adjustment of the patient's treatment plan. And through an application service module, based on the disease prediction result and the doctor's treatment guidance, the current prognosis treatment plan of the patient is intelligently adjusted to realize personalized service, ensuring that the current treatment plan used by the patient has a high degree of matching with the patient's current physical condition and current disease condition, providing continuous help for the patient's recovery or improvement of the condition, and supervising the patient's treatment process according to the adjustment result to ensure the effective implementation of the prognosis treatment plan after the patient is discharged, effectively improving the diagnosis and treatment effect.

[0088] Embodiment 2:

[0089] Based on Embodiment 1, the data collection module, as Figure 2 shown, includes:

[0090] A patient feedback data collection unit for collecting self-perceived physical sign data manually input by the patient based on the patient-associated data topology network;

[0091] A self-check physical sign data collection unit for collecting self-check physical sign data obtained by the patient's physical examination on wearable devices and home medical testing devices based on the patient-associated data topology network;

[0092] A diagnosis record collection unit for collecting the medical history data of the patient based on the patient-associated data topology network;

[0093] A data transmission unit for generating multi-dimensional physical sign data based on the self-perceived physical sign data and the self-check physical sign data, and sending the multi-dimensional physical sign data and the medical history data to the data storage module.

[0094] Advantages of the above technical solution: By means of the patient feedback data acquisition unit, the self-examination physical sign data acquisition unit, and the diagnosis record acquisition unit, the present invention respectively acquires the self-perceived physical sign data, the self-examination physical sign data, and the medical history data of the patient through the patient-associated data topology network. Then, based on the self-perceived physical sign data and the self-examination physical sign data, the data transmission unit generates multi-dimensional physical sign data, and sends the multi-dimensional physical sign data and the medical history data to the data storage module, realizing the integration of the patient's medical data resources. This is not only beneficial for doctors to comprehensively understand the patient's condition, but also can improve the patient's prognosis, and provides a basis for supervising and adjusting the implementation of the patient's post-treatment plan.

[0095] Embodiment 3:

[0096] Based on Embodiment 2, the data acquisition module further includes:

[0097] The data acquisition network establishment unit is used to obtain the access rights to the relevant data corresponding to the current patient in the data storage systems of the patient's handwritten note system, the hospital information system, the electronic medical record system, and the wearable device and the home medical detection device;

[0098] Based on the access rights, generate a corresponding local data communication protocol;

[0099] Based on the local data communication protocol, respectively establish communication channels between the data acquisition module and the data storage systems of the patient's handwritten note system, the hospital information system, the electronic medical record system, and the wearable device and the home medical detection device, and generate a patient-associated data topology network.

[0100] Advantages of the above technical solution: The present invention obtains the access rights to the relevant data corresponding to the current patient in the data storage systems of the patient's handwritten note system, the hospital information system, the electronic medical record system, and the wearable device and the home medical detection device through the data acquisition network establishment unit; based on the access rights, generate a corresponding local data communication protocol; based on the local data communication protocol, respectively establish communication channels between the data acquisition module and the data storage systems of the patient's handwritten note system, the hospital information system, the electronic medical record system, and the wearable device and the home medical detection device, and generate a patient-associated data topology network. When the system acquires the patient's relevant data, due to the limitation of the local data communication protocol, it can only access the data with access rights related to the current patient, ensuring the legality of obtaining the patient's medical data resources while also ensuring the data security of the accessed device or system.

[0101] Embodiment 4:

[0102] Based on Embodiment 1, as Figure 3 shown, the data storage module includes:

[0103] A data standardization unit, which is used to synchronize the data format standardization of multi-dimensional physical sign data and diagnosis and treatment history data based on a preset template of various multi-dimensional physical sign data and diagnosis and treatment history data, and obtain a patient's phased storage form;

[0104] An arrangement and storage unit, which is used to insert the patient's phased storage form into the corresponding patient management data storage directory for encrypted archival storage based on the time axis sequence;

[0105] An intelligent reminder unit, which is used to determine the latest collection time of each type of multi-dimensional physical sign data based on the collection frequency corresponding to various multi-dimensional physical sign data. If any type of multi-dimensional physical sign data arrives at the corresponding latest collection time and the corresponding type of multi-dimensional physical sign data is received, a reminder notice is sent to the mobile terminal associated with the patient.

[0106] Beneficial effects of the above technical solution: In the present invention, the data standardization unit standardizes the collected data into a patient's phased storage form, which not only ensures the simplicity of the data in the system but also realizes the automatic integration of the data. While making the system intelligent, it also reduces the storage pressure of the system. The arrangement and storage unit inserts the patient's phased storage form into the corresponding patient management data storage directory for encrypted archival storage based on the time axis sequence, which not only realizes orderly storage but also ensures the confidentiality and security of the patient's medical data. And the intelligent reminder unit determines the latest collection time of each type of multi-dimensional physical sign data based on the collection frequency corresponding to various multi-dimensional physical sign data. If any type of multi-dimensional physical sign data arrives at the corresponding latest collection time and the corresponding type of multi-dimensional physical sign data is received, a reminder notice is sent to the mobile terminal associated with the patient, ensuring that the patient can complete it on time according to the prognosis treatment plan given by the doctor, providing a guarantee for the effective implementation of the prognosis treatment plan, effectively improving the efficiency and quality of medical services, and also reducing the medical burden to a certain extent.

[0107] Example 5:

[0108] Based on Example 4, the data standardization unit includes:

[0109] A stage division sub-unit, which is used to obtain the prognosis treatment plan of the patient and determine the optimal post-diagnosis management analysis time interval of the patient based on the treatment course corresponding to the prognosis treatment plan;

[0110] A template generation unit, which is used to determine the collection frequency corresponding to various multi-dimensional physical sign data respectively based on the data categories corresponding to various multi-dimensional physical sign data and in combination with the patient's current condition;

[0111] Based on the optimal post-diagnosis management analysis time interval, various multi-dimensional physical sign data types, and the collection frequency corresponding to various multi-dimensional physical sign data, a preset template of the patient's post-diagnosis management data is generated;

[0112] An intelligent filling subunit, configured to perform type recognition on the received data, extract key data from the received data according to the recognition result, and fill the extraction result into the corresponding position of a preset template.

[0113] In this embodiment, the key data refers to the key points in different collected data. For example, the key points of the patient's diet data are the types of food the patient eats and the corresponding consumption amounts; the key points of the patient's medication data are the types of drugs the patient takes, the dosage, and the administration time.

[0114] In this embodiment, the optimal post-diagnosis management analysis time interval refers to the default time interval for analyzing the patient's multi-dimensional physical sign data and medical history data, and the user can also adjust it according to their own needs.

[0115] In this embodiment, the preset template includes various multi-dimensional physical sign data, and corresponding data filling cells are generated according to the number of acquisitions within the optimal post-diagnosis management analysis time interval based on the acquisition frequency corresponding to the multi-dimensional physical sign data.

[0116] Beneficial effects of the above technical solution: The present invention obtains the patient's prognosis treatment plan through the stage division subunit, determines the optimal post-diagnosis management analysis time interval based on the treatment course corresponding to the prognosis treatment plan, and supervises the patient's prognosis treatment in a staged manner, providing a basis for the staged analysis of the patient's condition, facilitating the adjustment of the prognosis treatment plan according to the patient's actual condition, and being conducive to improving the adaptability of the prognosis treatment plan to the patient's actual condition and physical condition; then, through the template generation unit, based on the data categories corresponding to various multi-dimensional physical sign data and in combination with the patient's current condition, the acquisition frequencies corresponding to various multi-dimensional physical sign data are respectively determined, providing a basis for the supervision and reminder of the patient's self-perceived physical sign data such as medication, diet, and exercise. Then, based on the optimal post-diagnosis management analysis time interval, various multi-dimensional physical sign data types, and the acquisition frequencies corresponding to various multi-dimensional physical sign data, a preset template for the patient's post-diagnosis management data is generated, obtaining a staged data storage template, simplifying the data acquisition and integration process, and realizing the automatic integration of the patient's staged prognosis medical data; finally, through the intelligent filling subunit, type recognition is performed on the received data, key data is extracted from the received data according to the recognition result, and the extraction result is filled into the corresponding position of the preset template, realizing the automatic acquisition and intelligent integration of the patient's medical data.

[0117] Embodiment 6:

[0118] Based on Embodiment 1, the data analysis module, as Figure 4 shown, includes:

[0119] The first data analysis unit is used to obtain the stored data corresponding to the preset valid time, and based on the diagnosis conclusions corresponding to the diagnosis and treatment history data in the stored data, obtain the diagnosis changes of the patient's liver disease, determine the change trend of the patient's liver disease condition and the patient's latest diagnosed condition;

[0120] According to the patient's latest diagnosed condition, combined with the standard value ranges corresponding to multiple physiological indicators in the patient's current age stage, predict the current multiple physiological indicators of the patient and the personal reference value ranges;

[0121] The data intelligent marking unit is used to compare the new physiological index data with its corresponding personal value range when the data storage module receives the new physiological index data. When the new physiological index data is within its corresponding personal value range, add a normal label to the new physiological index data;

[0122] Otherwise, add an abnormal label to the new physiological index data;

[0123] The second data analysis unit is used to determine the abnormal frequency of each physiological index of the patient within the preset valid time based on the labels carried by the multiple physiological indicators in the stored data;

[0124] According to the error rates between the abnormal physiological index data included in each type of physiological index and their corresponding personal value ranges, respectively obtain the average error rate of each physiological index;

[0125] Based on the abnormal frequency and average error rate of each physiological index, respectively determine the index abnormal rate of each physiological index;

[0126] The disease prediction unit is used to determine the weight allocation order of the physiological indicators based on the latest diagnosed condition, combined with the change speeds of the physiological indicators corresponding to different liver disease conditions, and obtain the allocation sequence from large to small;

[0127] According to the number of physiological indicators self-checked by the patient, obtain the corresponding weight array in the preset database, and based on the allocation sequence and the weight array, allocate the weights corresponding to each physiological indicator;

[0128] Based on the weight allocation result and the index abnormal rate of each physiological indicator, obtain the monitoring index abnormal rate of the patient;

[0129] When the monitoring index abnormal rate is less than the lower limit of the preset abnormal floating interval, it is determined that the patient's condition has improved;

[0130] When the monitoring index abnormal rate is greater than the upper limit of the preset abnormal floating interval, it is determined that the patient's condition has deteriorated;

[0131] When the monitoring index abnormal rate is within the preset abnormal floating interval, it is determined that the patient's condition is stable.

[0132] In this embodiment, the preset database contains various distribution ratios of physiological index data. The distribution ratios are different for different numbers of self-checked physiological indexes. For example, when there are three types of physical sign data, the weights included in the weight array are 0.3, 0.4, and 0.3. When there are four types of physical sign data, the weights included in the weight array are 0.2, 0.2, 0.3, and 0.1.

[0133] Beneficial effects of the above technical solution: According to the latest diagnosed condition of the patient, the first data analysis unit of the present invention combines the standard value ranges corresponding to multiple physiological indexes corresponding to the patient's current age stage to predict the current multiple physiological indexes of the patient and the personal reference value ranges. Then, the data intelligent marking unit automatically detects and marks each collected physiological index data to determine the abnormal physiology of the patient, providing a basis for subsequent disease prediction. Through data analysis, combined with the abnormal conditions of the physiological index data of the patient at the current stage, the index abnormality rate of each physiological index is determined, and according to the index abnormality rate, combined with the weight distribution structure, the monitoring index abnormality rate of the patient is obtained, realizing the intelligent judgment of the patient's current condition, providing a reference for the doctor to timely track the patient's condition. Among them, based on the latest diagnosed condition, combined with the change speed of the physiological indexes corresponding to different diseases of liver disease, the weight distribution order of the physiological indexes is determined to obtain a descending distribution sequence; according to the number of self-checked physiological indexes of the patient, the corresponding weight array is obtained in the preset database, and based on the distribution sequence and the weight array, the weights corresponding to each physiological index are distributed, so that the finally obtained monitoring index abnormality rate is more in line with the actual physical condition of the patient, improving the accuracy of disease condition judgment.

[0134] Example 7:

[0135] On the basis of Example 1, the application service module, as Figure 5 shown, includes:

[0136] A result sending unit, configured to synchronously send the patient's abnormal physiological index data or the disease prediction result to the patient's attending physician;

[0137] A first solution adjustment and analysis unit, configured to, when the patient's condition deteriorates, evaluate the degree of deterioration of the patient's condition based on the deviation rate between the monitoring index abnormality rate and the upper limit of the preset abnormal floating interval. When the degree of deterioration of the patient's condition is greater than the preset warning value, send a medical warning to the patient;

[0138] Otherwise, obtain the self-perceived physical sign data of the patient within the preset effective time, determine the patient's medication situation and diet and exercise record situation, and based on the current prognosis treatment plan, judge whether the deterioration of the patient's condition is of the type of insufficient self-supervision;

[0139] If so, based on the degree of deterioration of the patient and the symptom descriptions in the patient's self-perceived physical sign data, combined with the development process of the liver disease currently suffered by the patient, predict the current disease stage of the patient;

[0140] If not, send a medical warning to the patient;

[0141] The second plan adjustment analysis unit is used to, when the patient's condition improves, compare the symptom descriptions in the self-perceived physical sign data of the patient's current effective time interval with the described symptoms corresponding to the previous effective time interval to obtain the alleviated symptoms and eliminated symptoms of the patient;

[0142] Based on the alleviated symptoms and eliminated symptoms of the patient, combined with the development process of the liver disease currently suffered by the patient, predict the current disease stage of the patient;

[0143] The intelligent adjustment confirmation unit is used to adjust the patient's drug dosage and diet and exercise requirements according to the current disease stage and the changes in the patient's physiological indicators to obtain a new prognosis treatment plan and send it to the patient's attending physician for confirmation.

[0144] In this embodiment, the alleviated symptoms refer to the symptoms that are alleviated under the current condition of the patient; the eliminated symptoms refer to the symptoms that were originally present but are not or do not appear under the current condition of the patient.

[0145] The beneficial effects of the above technical solutions: The present invention synchronously sends the abnormal physiological index data or the disease prediction result of the patient to the patient's attending physician through the result sending unit, ensuring that the patient's attending physician can timely understand the patient's condition, providing a basis for the physician's remote guidance. At the same time, through the first plan adjustment analysis unit, the severity of the condition of the patient with deteriorated condition is evaluated. When the degree of deterioration of the patient's condition is greater than the preset warning value, a medical warning is sent to the patient to urge the patient to seek medical treatment in time to avoid the continuous deterioration of the condition. And when the degree of deterioration of the patient's condition is less than or equal to the preset warning value, a preliminary judgment is made on the cause of the patient's deterioration, providing a basis for the adjustment of the plan and the urging of seeking medical treatment. And through the second plan adjustment analysis unit, the condition of the patient with improved condition is analyzed. Finally, the intelligent adjustment confirmation unit adjusts the patient's drug dosage and diet and exercise requirements according to the current disease stage and the changes in the patient's physiological indicators to obtain a new prognosis treatment plan and send it to the patient's attending physician for confirmation to achieve personalized service, ensuring that the current treatment plan used by the patient has a high degree of matching with the patient's current physical condition and current disease, providing continuous help for the patient's recovery or the improvement of the condition.

[0146] Example 8:

[0147] On the basis of Example 7, the intelligent adjustment confirmation unit includes:

[0148] A treatment comparison subunit, configured to obtain the initial diagnosis and treatment history data of multiple liver disease patients, classify the initial diagnosis and treatment records based on the types of liver diseases of the patients to obtain multiple groups of diagnostic record data, and cluster the groups of diagnostic record data according to the severity of the patients' conditions to obtain multiple data clusters;

[0149] Compare the various physiological indicators of the patients corresponding to different initial diagnosis and treatment history data within the data cluster with their corresponding standard value ranges respectively to obtain the index deviations corresponding to each physiological indicator;

[0150] Vectorize the physiological indicator data of the patients corresponding to different initial diagnosis and treatment history data based on the index deviations to obtain index deviation vectors;

[0151] Compare the index deviations corresponding to each initial diagnosis and treatment history data within the same data cluster to obtain the similarities between different initial diagnosis and treatment history data;

[0152] Take any index deviation vector within the data cluster as a target vector, and cluster it according to a preset clustering interval based on the similarity between the target vector and the remaining index deviation vectors within the data cluster to obtain a vector cluster corresponding to the target vector;

[0153] Obtain the vector clusters corresponding to all index deviation vectors, compare the treatment plans within multiple initial diagnosis and treatment records corresponding to the same vector cluster to obtain the characteristics of plan formulation;

[0154] Compare the characteristics of plan formulation with the characteristics of plan formulation corresponding to the irrelevant vector clusters corresponding to the vector cluster to determine the first influence of the differences in physiological indicators of the patients under different conditions of the same type of liver disease on the treatment plan of the patients;

[0155] A plan adjustment subunit, configured to determine the best treatment plan adjustment strategy for the current condition of the patient under the current physiological indicators based on the current disease stage and the changes in the patient's physiological indicators, in combination with the first influence;

[0156] Adjust the drug dosage and diet and exercise requirements of the patient based on the best treatment plan adjustment strategy to obtain a new prognosis treatment plan, and send it to the patient's attending physician for confirmation.

[0157] Beneficial effects of the above technical solution: By comparing the prognostic treatment plans of a large number of liver disease patients, the present invention determines the influence of different physiological index data on the prognostic treatment plan of patients under the same condition of the same liver disease. Finally, based on the combination of the above influence, the current disease stage of all patients and the changes in the physiological indexes of the patients, the optimal treatment plan adjustment strategy for the current condition of the patients under the current physiological indexes is determined, realizing the need for personalized customization of the prognostic treatment plan according to the patient's physical condition and disease, providing a reference for doctors to adjust the treatment plan of patients, and being beneficial to improving the treatment quality and efficiency of patients. Based on the optimal treatment plan adjustment strategy, the drug dosage and diet and exercise requirements of patients are adjusted to obtain a new prognostic treatment plan, which is sent to the attending doctor of the patient for confirmation, fully ensuring the feasibility of the adjusted treatment plan.

[0158] Example 9:

[0159] The present invention provides a method for post-diagnosis management of liver disease patients, as Figure 6 shown, including:

[0160] Step 1: Collect multi-dimensional physical sign data and medical history data of patients;

[0161] Step 2: Standardize, encrypt, and archive the multi-dimensional physical sign data and medical history data for storage;

[0162] Step 3: Analyze the current liver disease condition of the patient based on the stored data, determine the current condition of the patient, and predict the disease progression of the patient according to the current condition of the patient to obtain a disease prediction result;

[0163] Step 4: Intelligently adjust the current post-diagnosis treatment of the patient based on the disease prediction result and the doctor's treatment guidance, and supervise the quality process of the patient according to the adjustment result.

[0164] Advantages of the above technical solution: The present invention collects multi-dimensional vital sign data and medical history data of patients through a data collection module, and sends them to a data storage module for standardized encrypted archival storage of the multi-dimensional vital sign data and medical history data, realizing the integration of patients' medical data resources. Then, through a data analysis module, based on the stored data, the current liver disease condition of the patient is analyzed to determine the patient's current condition. According to the patient's current condition, the disease progression of the patient is predicted to obtain a disease prediction result, realizing remote monitoring of the patient's condition by doctors and providing a reliable basis for timely adjustment of the patient's treatment plan. And through an application service module, based on the disease prediction result and doctors' treatment guidance, the current prognosis treatment plan of the patient is intelligently adjusted to realize personalized services, ensuring that the current treatment plan used by the patient has a high degree of matching with the patient's current physical condition and current condition, providing continuous help for the patient's recovery or improvement of the condition, and supervising the patient's treatment process according to the adjustment result to ensure the effective implementation of the prognosis treatment plan after the patient is discharged, effectively improving the diagnosis and treatment effect.

[0165] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A post-diagnosis management system for patients with liver disease, characterized in that: include: Data collection module, used to collect multi-dimensional vital sign data and diagnosis and treatment history data of patients; Data storage module, used for standardized encrypted archiving and storage of multi-dimensional vital sign data and diagnosis and treatment history data; A data analysis module is used to analyze the patient's current liver disease condition based on the stored data, determine the patient's current condition, predict the patient's disease progression based on the patient's current condition, and obtain a condition prediction result; The application service module is used to intelligently adjust the patient's current prognosis treatment plan based on the disease prediction results and the doctor's treatment guidance, and supervise the patient's treatment process based on the adjustment results.

2. A post-diagnosis management system for patients with liver disease according to claim 1, characterized in that: Data acquisition module, including: A patient feedback data collection unit, used for collecting the self-sensed vital sign data manually input by the patient based on the patient-related data topology network; A self-examination vital sign data collection unit, used to collect the self-examination vital sign data of the patient performing body examinations on wearable devices and home medical testing devices based on a patient-related data topology network; A diagnosis record collection unit, used to collect the patient's diagnosis and treatment history data based on the patient-related data topology network; The data transmission unit is used to generate multi-dimensional physical sign data based on the self-sensed physical sign data and the self-examination physical sign data, and send the multi-dimensional physical sign data and the diagnosis and treatment history data to the data storage module.

3. A post-diagnosis management system for patients with liver disease according to claim 1, characterized in that: The data acquisition module also includes: A data collection network establishment unit is used to obtain access rights to relevant data corresponding to the current patient in the patient's handwriting system, hospital information system, electronic medical record system, and data storage systems of wearable devices and home medical testing equipment; Based on the access rights, generating a corresponding local data communication protocol; Based on the local data communication protocol, communication channels are established between the data acquisition module and the patient's handwriting system, hospital information system, electronic medical record system, and data storage systems of wearable devices and home medical detection equipment to generate a patient-related data topology network.

4. A post-diagnosis management system for patients with liver disease according to claim 1, characterized in that: Data storage module, including: A data standardization unit is used to standardize the synchronous data format of multi-dimensional vital sign data and diagnosis and treatment history data based on preset templates of various multi-dimensional vital sign data and diagnosis and treatment history data, and obtain a patient stage storage table; The storage arrangement unit is used to insert the patient phase storage table into the corresponding patient management data storage directory for encrypted archiving storage based on the timeline sequence.

5. A post-diagnosis management system for patients with liver disease according to claim 4, characterized in that: Data standardization unit, including: The stage division subunit is used to obtain the patient's prognosis and treatment plan, and determine the patient's optimal post-diagnosis management analysis time interval based on the treatment course corresponding to the prognosis and treatment plan; A template generation unit, for determining the collection frequencies corresponding to various multi-dimensional vital sign data based on the data categories corresponding to the various multi-dimensional vital sign data and in combination with the patient's current condition; Generate a preset template for patient post-diagnosis management data based on the optimal post-diagnosis management analysis time interval, various multi-dimensional vital sign data types, and the corresponding collection frequency of various multi-dimensional vital sign data; The intelligent filling subunit is used to identify the type of received data, extract key data from the received data based on the identification result, and fill the extracted results into the corresponding position of the preset template.

6. A post-diagnosis management system for patients with liver disease according to claim 5, characterized in that: The data storage module also includes: The intelligent reminder unit is used to determine the latest collection time of each type of multi-dimensional vital sign data based on the collection frequency corresponding to various multi-dimensional vital sign data. If any type of multi-dimensional vital sign data reaches the corresponding latest collection time and receives the corresponding type of multi-dimensional vital sign data, a reminder notification is sent to the patient's associated mobile terminal.

7. A post-diagnosis management system for patients with liver disease according to claim 5, characterized in that: Data analysis modules, including: A first data analysis unit is used to obtain corresponding stored data within a preset effective time, obtain the diagnosis change of the patient's liver disease based on the diagnosis conclusion corresponding to the diagnosis and treatment history data in the stored data, and determine the change trend of the patient's liver disease and the patient's latest diagnosed condition; According to the patient's latest diagnosis, combined with the standard value ranges of multiple physiological indicators corresponding to the patient's current age stage, the patient's current multiple physiological indicators and personal reference value ranges are predicted; A data intelligent labeling unit, used for comparing the new physiological indicator data with its corresponding personal value range when the data storage module receives the new physiological indicator data, and adding a normal label to the new physiological indicator data when the new physiological indicator data is within its corresponding personal value range; Otherwise, adding an abnormal label to the new physiological indicator data; A second data analysis unit, for determining the abnormal frequency of each physiological indicator of the patient within a preset effective time based on the carried tags of the multiple physiological indicators in the stored data; According to the error rate of abnormal physiological index data contained in various physiological indexes and their corresponding personal value ranges, the average error rate of each physiological index is obtained respectively; Based on the abnormal frequency and average error rate of each physiological indicator, the indicator abnormality rate of each physiological indicator is determined respectively; The condition prediction unit is used to determine the weight distribution order of the physiological indicators based on the latest diagnosed condition and the change speed of the physiological indicators corresponding to different liver disease symptoms, and obtain a distribution sequence from large to small; According to the number of physiological indicators of the patient's self-examination, a corresponding weight array is obtained in a preset database, and based on the allocation sequence and the weight array, the weight corresponding to each physiological indicator is allocated; Based on the weight distribution result and the abnormal rate of each physiological index, the abnormal rate of the patient's monitoring index is obtained; When the abnormal rate of the monitoring indicator is less than the lower limit of the preset abnormal floating range, it is determined that the patient's condition has improved; When the abnormal rate of the monitoring indicator is greater than the upper limit of the preset abnormal floating range, it is determined that the patient's condition has worsened; When the abnormal rate of the monitoring indicator is within the preset abnormal floating range, it is determined that the patient's condition is stable.

8. A post-diagnosis management system for patients with liver disease according to claim 1, characterized in that: Application service modules include: A result sending unit is used to synchronously send the patient's abnormal physiological index data or disease prediction results to the patient's attending physician; The first scheme adjusts the analysis unit, which is used to evaluate the degree of deterioration of the patient's condition based on the deviation rate between the abnormal rate of the monitoring indicator and the upper limit of the preset abnormal floating interval when the patient's condition deteriorates, and send a medical warning to the patient when the degree of deterioration of the patient's condition is greater than the preset warning value; Otherwise, obtain the patient's self-perceived physical sign data within the preset effective time, determine the patient's medication status and diet and exercise records, and based on the current prognosis and treatment plan, judge whether the patient's condition deterioration is a type of inadequate self-monitoring; If yes, then based on the patient's deterioration and the symptom description in the patient's self-perceived physical sign data, combined with the progression of the patient's current liver disease, the patient's current disease stage is predicted; If not, a medical warning is sent to the patient; The second scheme adjusts the analysis unit, which is used to compare the symptom description in the patient's self-perceived physical sign data of the current effective time interval with the symptom description corresponding to the previous effective time interval when the patient's condition improves, to obtain the patient's symptom relief and symptom elimination; Predict the patient's current stage of illness based on the patient's symptom alleviation and elimination, combined with the patient's current liver disease progression; The intelligent adjustment confirmation unit is used to adjust the patient's medication dosage and diet and exercise requirements according to the current stage of the disease and changes in the patient's physiological indicators, obtain a new prognosis and treatment plan, and send it to the patient's attending physician for confirmation.

9. A post-diagnosis management system for patients with liver disease according to claim 8, characterized in that: Intelligent adjustment confirmation unit, including: The treatment comparison subunit is used to obtain the initial diagnosis and treatment history data of multiple liver disease patients, classify the initial diagnosis and treatment records based on the type of liver disease of the patients, obtain multiple diagnosis record data groups, and cluster the diagnosis record data groups according to the severity of the patients' conditions to obtain multiple data clusters; Compare various physiological indicators of patients corresponding to different initial diagnosis and treatment history data in the data cluster with their corresponding standard value ranges to obtain the indicator deviation corresponding to each physiological indicator; Based on the indicator deviation, the physiological indicator data of the patient corresponding to different initial diagnosis and treatment history data are vectorized to obtain an indicator deviation vector; Compare the indicator deviations corresponding to each initial diagnosis and treatment history data in the same data cluster to obtain the similarity between different initial diagnosis and treatment history data; Taking any indicator deviation vector in the data cluster as the target vector, clustering is performed according to a preset clustering interval based on the similarity between the target vector and the remaining indicator deviation vectors in the data cluster to obtain a vector cluster corresponding to the target vector; Obtaining vector clusters corresponding to all indicator deviation vectors, comparing treatment plans in multiple initial diagnosis and treatment records corresponding to the same vector cluster, and obtaining plan formulation features; Comparing the plan formulation features with the plan formulation features corresponding to the unrelated vector clusters corresponding to the vector clusters, respectively, to determine the first impact of the difference in physiological indicators of patients with different conditions of the same type of liver disease on the patient's treatment plan; A treatment plan adjustment subunit, configured to determine the best treatment plan adjustment strategy for the patient's current condition under the current physiological indicators based on the current stage of the disease and changes in the patient's physiological indicators in combination with the first influence; Based on the optimal treatment adjustment strategy, the patient's medication dosage and diet and exercise requirements are adjusted to obtain a new prognosis treatment plan, which is sent to the patient's attending physician for confirmation.

10. A method for post-diagnosis management of patients with liver disease, characterized in that: include: Collect multi-dimensional physical sign data and diagnosis and treatment history data of patients; Standardized encrypted archiving and storage of multi-dimensional vital sign data and diagnosis and treatment history data; Analyze the patient's current liver disease condition based on the stored data to determine the patient's current condition, predict the patient's disease progression based on the patient's current condition, and obtain a condition prediction result; Based on the disease prediction results and the doctor's treatment guidance, the patient's current post-diagnosis treatment is intelligently adjusted, and the patient's quality process is supervised based on the adjustment results.

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