Smart medical platform based on big data

Abstract

The present invention is applicable to the field of smart medical care, and provides a smart medical care platform based on big data, the platform including: a data acquisition module, a data analysis module, a referral decision support module, a comprehensive data sharing module, a health management monitoring module, an autonomous participation management module, and a library learning update module. Data analysis and artificial intelligence technologies are used to optimize the referral process of patients to ensure that patients can receive treatment in the most suitable medical institutions. By combining the individual disease development characteristics of patients, the current treatment effects and the real-time data of medical resources in the region, efficient and accurate referral decision support is achieved. The present invention significantly improves the efficiency and accuracy of the medical referral process, while improving the patient's medical experience and treatment results, effectively solving problems such as uneven distribution of medical resources and complex referral processes, and is of great significance to the continuous optimization and development of modern medical systems.

Description

Technical Field

[0001] The present invention belongs to the field of smart medical care, and in particular relates to a smart medical care platform based on big data. Background Art

[0002] Smart healthcare, also often referred to as digital healthcare or intelligent healthcare, refers to a series of activities that use modern information technology means such as digital technology, big data analysis, artificial intelligence and Internet technology to optimize the quality of medical services, improve medical efficiency, reduce medical costs, and improve medical experience. Key components of this field include electronic health records (EHR), telemedicine services, patient health data analysis, personalized medical solutions, intelligent diagnostic systems, disease prediction models, medical robots and wearable medical devices. The goal of smart healthcare is to achieve more personalized, accurate, efficient and convenient medical services through technological innovation.

[0003] In existing technologies, there are inaccuracies in identifying personalized medical needs, difficulties in real-time integration and utilization of medical resource information, inefficiencies in traditional referral decisions, lack of instant updates and dynamic adjustment mechanisms for changes in patient conditions, uneven distribution of medical resources, and complexity in referral processes. These problems have jointly affected the efficiency and quality of medical services, resulting in patients not being able to receive the most appropriate treatment in a timely manner and increasing the burden on the medical system. Summary of the invention

[0004] The purpose of the present invention is to provide a smart medical platform based on big data, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented in this way: a smart medical platform based on big data, the platform comprising:

[0006] The data acquisition module is used to connect with the information system of medical institutions to automatically obtain and manage relevant patient data, including medical records, physiological parameters, genomic data and medical records;

[0007] The data analysis module is used to analyze the automatically acquired data, identify individual differences and characteristics in the patient group, and combine the relevant data of each patient to predict the disease development trend and evaluate the current treatment effect of the patient;

[0008] The referral decision-making support module is used to obtain the availability of medical resources of the current medical institution and medical institutions within the region, and to determine whether the availability of medical resources of the current medical institution can meet the patient's cure needs in combination with the patient's disease development trend prediction and current treatment effect. If it cannot be met, all medical institutions within the region that can meet the patient's cure needs are identified, and a transfer mark is added for the patient;

[0009] A comprehensive data sharing module is used to establish a data sharing platform within the region, and the data sharing platform establishes communication channels with all medical institutions within the region, and the patient-related data with transfer marks are placed in the data sharing platform for encrypted storage;

[0010] The health management monitoring module is used to monitor the patient's physiological parameters and disease development trend data in real time, and set physiological expected thresholds based on the relevant data of each patient, and issue warning information when the monitored parameters and data exceed the physiological expected thresholds;

[0011] The autonomous participation management module is used to receive the patient's application information and the physiological parameters actively uploaded by the patient, and to feed back the doctor's feedback instructions to the patient;

[0012] The library learning and updating module is used to establish a medical reserve library and add labels to each disease in the medical reserve library, storing the relevant data and final treatment plan of each patient in the medical reserve library.

[0013] As a further solution of the present invention, the data acquisition module includes:

[0014] The institution docking unit is used to connect with the information system of each medical institution, establish a communication connection, and automatically obtain and transmit relevant patient data;

[0015] A data management unit is used to store, integrate and manage the collected patient-related data and add a patient name tag to each patient's data;

[0016] The data encryption unit encrypts the data of each patient.

[0017] As a further solution of the present invention, the data analysis module includes:

[0018] A feature selection unit is used to extract individual differences and features from each patient's relevant data, including patient body shape data, genetic characteristics, clinical data, and current disease data;

[0019] Individualized risk assessment unit, which is used to assess the patient's current disease transformation risk and predict the potential path and speed of disease progression based on the patient's clinical data and current disease data;

[0020] The effectiveness evaluation unit is used to analyze all data of patients who have received treatment and evaluate the effectiveness of the treatment plan for the patient.

[0021] As a further solution of the present invention, the referral decision support module includes:

[0022] The medical resource analysis unit is used to obtain the medical resource information of all medical institutions in the current medical institution and the area where the patient is located, including the departments, medical equipment, vacant beds and medical level of each medical institution;

[0023] The patient needs assessment unit is used to evaluate the medical level of the current medical institution based on the patient's disease development trend and current treatment effect, analyze the patient's treatment needs and the availability of medical resources of the medical institution, and determine whether the medical institution has the medical resources required to cure the corresponding patient;

[0024] The regional institution screening unit is used to screen out all medical institutions that can meet the patient's treatment needs from other medical institutions within the patient's area according to the patient's treatment needs analysis results when the medical institution does not have the medical resources required to cure the corresponding patient;

[0025] The referral marking unit is used to obtain the medical institution selected by the patient, establish communication with the medical institution, synchronize all the patient's data to the information database of the medical institution, and add a transfer mark for the patient.

[0026] As a further solution of the present invention, the comprehensive data sharing module includes:

[0027] A communication building unit is used to establish a data sharing platform and, based on the coverage of the data sharing platform, establish communication channels with all medical institutions within the coverage area;

[0028] A data sharing permission unit, used to set the access permission of the data sharing platform, wherein the access permission is set to be accessible only to doctors;

[0029] The data upload synchronization unit is used to upload the patient information that needs to be referred to the data sharing platform and encrypt the uploaded data. The encryption key will only be synchronized to the medical institution selected by the patient.

[0030] As a further solution of the present invention, the health management monitoring module includes:

[0031] A real-time monitoring unit is used to connect with the sensor equipment worn by the patient to collect the patient's physiological parameter data and the patient's disease development trend data;

[0032] A standard setting unit, used to set personalized physiological expected thresholds based on each patient's physiological parameter data and current disease status;

[0033] The anomaly detection unit is used to compare and analyze the monitoring data in real time, and compare each acquired monitoring data with the set physiological expected threshold. When any monitoring data exceeds the physiological expected threshold, a warning message is issued.

[0034] As a further solution of the present invention, the autonomous participation management module includes:

[0035] The application information receiving unit is used to interact with the patient, receive the patient's needs, consultation and appointment information, and receive the doctor's reply to the interactive information and feed it back to the patient;

[0036] The parameter autonomous uploading unit is used to receive physiological parameter data actively uploaded by the patient.

[0037] As a further solution of the present invention, the library learning update module includes:

[0038] The library establishment unit is used to build a medical library to store disease-related data and add a label index to each disease in the medical library;

[0039] The patient data storage unit is used to store the patient's information in a tag under the same index according to the patient's disease data, and obtain the treatment plan and treatment effect adopted by the patient this time, and bind and store them in the tag.

[0040] The beneficial effects of the present invention are:

[0041] Use data analysis and artificial intelligence technology to optimize the patient referral process to ensure that patients can receive treatment at the most suitable medical institution. By combining the patient's individual disease development characteristics, current treatment effects and real-time data on regional medical resources, efficient and accurate referral decision support can be achieved.

[0042] Through in-depth analysis of each patient's personalized data and regional medical resources, personalized medical decision support is provided. This personalized analysis can ensure that patients are referred to the medical institutions that best meet their specific needs, thereby greatly improving medical efficiency and patient satisfaction. In addition, the resource information of each medical institution can be obtained in real time, so that referral decisions can be made based on the latest medical resource conditions. This real-time nature ensures the accuracy and timeliness of referral decisions, and effectively alleviates the problem of uneven distribution of medical resources.

[0043] At the same time, it can also dynamically evaluate the patient's treatment needs and the service capabilities of the current medical institutions. This dynamic evaluation mechanism allows the system to instantly adjust referral decisions when the patient's condition changes, ensuring that the patient is always on the best treatment path.

[0044] The present invention significantly improves the efficiency and accuracy of the medical referral process, while improving patients' medical experience and treatment outcomes, effectively solving problems such as uneven distribution of medical resources and complex referral processes, and is of great significance to the continuous optimization and development of the modern medical system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A structural block diagram of a smart medical platform based on big data provided by an embodiment of the present invention;

[0046] Figure 2 A structural block diagram of a data acquisition module provided by an embodiment of the present invention;

[0047] Figure 3 A structural block diagram of a data analysis module provided by an embodiment of the present invention;

[0048] Figure 4 A structural block diagram of a referral decision support module provided in an embodiment of the present invention;

[0049] Figure 5 A structural block diagram of a comprehensive data sharing module provided in an embodiment of the present invention;

[0050] Figure 6 A structural block diagram of a health management monitoring module provided by an embodiment of the present invention;

[0051] Figure 7 A structural block diagram of an autonomous participation management module provided in an embodiment of the present invention;

[0052] Figure 8 This is a structural block diagram of a library learning and updating module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0055] Figure 1 The structure diagram of the smart medical platform based on big data provided by the embodiment of the present invention is as follows: Figure 1As shown, a smart medical platform based on big data includes:

[0056] The data acquisition module 100 is used to connect with the information system of the medical institution to automatically acquire and manage the patient's relevant data, including medical records, physiological parameters, genome data and medical records;

[0057] In the smart medical platform based on big data, the data acquisition module plays a vital role, which realizes the efficient, accurate and safe collection of patient data. By adopting advanced interface technology and communication protocols, it can automatically identify and adapt to the technical requirements of information systems of different medical institutions, realize seamless connection with multiple medical information systems, significantly improve the efficiency and coverage of data collection, and reduce the error rate.

[0058] Utilizing the latest data management technology, we not only store and integrate the collected data, but also clean, standardize and classify the data to establish a comprehensive and structured data file for each patient, ensuring the high quality and easy-to-analyze nature of the data, and providing an accurate and reliable data foundation for subsequent data analysis and decision support.

[0059] At the same time, the latest encryption technology is used to provide strong protection measures for each patient's data, ensuring the security of data during transmission or storage, providing solid protection for patient privacy, and enhancing the trust of patients and medical institutions in the platform. In addition, the design of the data acquisition module takes into account the changes in future technology and medical needs, and has good scalability and flexibility, allowing the platform to adapt to the rapid development needs of the medical industry.

[0060] This series of design advances and technological advantages not only provides a solid data foundation for the smart medical platform, but also makes significant contributions to improving the efficiency, accuracy and safety of the entire medical system.

[0061] The data analysis module 200 is used to analyze the automatically acquired data, identify individual differences and characteristics in the patient group, and combine the relevant data of each patient to predict the disease development trend and evaluate the current treatment effect of the patient;

[0062] The main task is to deeply explore and analyze medical data to provide personalized disease prediction and treatment effect evaluation.

[0063] Using advanced data mining technology, we extract individual differences and features that are important for disease prediction and treatment from massive amounts of patient data. These features include but are not limited to the patient's body shape data, genetic characteristics, clinical data, and current disease status. By identifying these key features, the unit can provide accurate basic data for subsequent risk assessment and treatment effect evaluation, greatly improving the accuracy of prediction and the possibility of personalized treatment.

[0064] These characteristic data are then further analyzed, combined with the patient's clinical data and current disease status, and a complex algorithm model is used to evaluate the patient's current risk of disease change and predict the disease's progression path and speed. The use of this unit not only helps doctors and patients understand the potential development trend of the disease, but also provides a scientific basis for formulating more accurate treatment plans.

[0065] This module will also track and analyze patients who have received treatment, and evaluate the effect of the current treatment plan on the patient by analyzing all the data before and after the treatment. This link is the key to continuously improving the treatment method and improving the efficiency of treatment. Through real-time evaluation of the treatment effect, the medical team can adjust the treatment plan in time and improve the success rate of treatment.

[0066] Through efficient feature selection, the data analysis module can accurately identify patient characteristics that have an important impact on disease prediction and treatment, providing strong support for personalized medicine. Secondly, individualized risk assessment makes medical decisions more accurate and can provide patients with more personalized treatment recommendations, greatly improving treatment effects and patient satisfaction. Finally, the application of the effect evaluation unit ensures the continuous optimization and updating of treatment plans and promotes the continuous improvement of the quality of medical services. These advances and advantages not only improve the efficiency and effectiveness of medical services, but also provide patients with safer, more effective and personalized medical services, demonstrating the huge potential of smart medical platforms in the field of modern medicine.

[0067] The referral auxiliary decision module 300 is used to obtain the availability of medical resources of the current medical institution and medical institutions within the region, and to judge whether the availability of medical resources of the current medical institution can meet the patient's cure needs in combination with the prediction of the patient's disease development trend and the current treatment effect. If it cannot be met, all medical institutions within the region that can meet the patient's cure needs are identified, and a transfer mark is added for the patient;

[0068] The referral decision support module is one of the core components of the big data-based smart medical platform. It aims to optimize the patient referral process by using data analysis and artificial intelligence technology to ensure that patients can receive treatment in the most suitable medical institution. This module combines the individual disease development characteristics of the patient, the current treatment effect and the real-time data of medical resources in the region to achieve efficient and accurate referral decision support.

[0069] The referral decision support module provides personalized medical decision support by deeply analyzing each patient's personalized data (including disease development trends, treatment effect feedback, etc.) and regional medical resources. This personalized analysis can ensure that patients are referred to the medical institutions that best meet their specific needs, thereby greatly improving medical efficiency and patient satisfaction.

[0070] The module can obtain the resource information of each medical institution in real time (such as department capacity, equipment utilization rate, number of empty beds, etc.), so that referral decisions can be made based on the latest medical resource status. This real-time nature ensures the accuracy and timeliness of referral decisions and effectively alleviates the problem of uneven distribution of medical resources.

[0071] It can also dynamically evaluate the patient's treatment needs and the service capabilities of current medical institutions. This dynamic evaluation mechanism allows the system to instantly adjust referral decisions when the patient's condition changes, ensuring that the patient is always on the best treatment path.

[0072] Subsequently, the modules jointly optimized the referral process, from patient needs assessment to the final selection of medical institutions, with the entire process being highly automated and efficient. The automatic synchronization of patient data reduced information loss and delays in the referral process, while the transfer mark added to the patient ensured smooth communication and data sharing between medical institutions.

[0073] At the same time, during the entire referral process, encryption measures are taken for the patient's personal information and medical data during transmission and storage to ensure patient privacy and data security. This data protection measure provides patients with a safer medical experience.

[0074] The referral decision-making support module significantly improves the efficiency and accuracy of the medical referral process through highly integrated data analysis and intelligent algorithms, while improving patients' medical experience and treatment outcomes. The implementation of this module can effectively solve problems such as uneven distribution of medical resources and complex referral processes, and is of great significance to the continuous optimization and development of the modern medical system.

[0075] The comprehensive data sharing module 400 is used to establish a data sharing platform within the region, and the data sharing platform establishes communication channels with all medical institutions within the region, and stores the patient-related data with the transfer mark in the data sharing platform in an encrypted manner;

[0076] The comprehensive data sharing module plays the role of a data hub in the smart medical platform. By building a safe and efficient data sharing platform, it promotes the transparency and circulation of medical information, thereby improving the response speed and treatment quality of the entire medical service system.

[0077] The data sharing module is responsible for establishing a stable communication channel between various medical institutions in the region. This communication framework not only needs to achieve real-time data transmission, but also needs to ensure the security and stability of the transmission process. The significant advancement of this design is that it can integrate multiple communication technologies and protocols to ensure seamless connection between different medical institutions, laying a solid foundation for subsequent data sharing and access.

[0078] Protect patient privacy and data security by setting strict access rights. In this module, access rights are strictly limited to doctors only. This measure can effectively prevent the leakage of sensitive patient information, protect the privacy of patients, and prevent data abuse or unauthorized access while meeting medical needs.

[0079] This module ensures the secure synchronization of patient information required for referral between medical institutions. Not only is the patient data uploaded to the sharing platform encrypted, but the encryption key is also synchronized only to the medical institution selected by the patient. This approach ensures the security of data transmission while ensuring the continuity and efficiency of medical services, because only authorized medical institutions can decrypt and access this information, thereby providing timely medical services to patients.

[0080] In general, the design concept of the integrated data sharing module is centered on the safety and privacy of patients, while ensuring the high efficiency and accuracy of medical information flow. This module improves the reliability and safety of medical services, reduces the error rate of information transmission, speeds up the response time of medical services, and greatly improves the medical experience and treatment effect of patients. In this way, the smart medical platform can achieve the optimal allocation of medical resources, promote cooperation between medical institutions in the region, and thus improve the efficiency of the entire medical system.

[0081] The health management monitoring module 500 is used to monitor the patient's physiological parameters and disease development trend data in real time, and to set physiological expected thresholds according to the relevant data of each patient, and to issue a warning message when the monitored parameters and data exceed the physiological expected thresholds;

[0082] The main function of this module is to monitor the patient's health status in real time, promptly detect potential health problems and prevent the deterioration of the disease, and enhance the early warning and response capabilities of the entire medical system.

[0083] The module is connected to various medical sensor devices worn by patients, such as heart rate monitors, blood glucose meters, blood pressure monitors, etc. These devices can continuously collect the patient's physiological parameters, such as heart rate, blood glucose level, blood pressure, etc., and transmit the data to the smart medical platform in real time. The significant progress of this unit lies in its high degree of integration and scalability. It can be compatible with a variety of sensors and devices to achieve simultaneous monitoring of multiple physiological parameters.

[0084] Based on the patient's personal historical health data and the disease trend analysis results provided by the big data analysis module, personalized physiological expectation thresholds are customized for each patient. These thresholds are not static, but dynamically adjusted according to changes in the patient's condition to ensure the accuracy and timeliness of the monitoring results. The advantage of this unit is that it can achieve refined management and adjust health management strategies based on individual differences.

[0085] Finally, this module continuously compares the collected data with the customized thresholds. Once any physiological parameter exceeds the threshold, the unit immediately analyzes the severity of the threshold and issues appropriate warning information according to the set protocol. The significant advancement of this unit lies in its intelligent anomaly detection algorithm, which can identify abnormal changes that really need attention based on the patient's disease type and clinical manifestations.

[0086] The autonomous participation management module 600 is used to receive the patient's application information and the physiological parameters actively uploaded by the patient, and feed back the doctor's feedback instructions to the patient;

[0087] This module not only enhances the patient's sense of active participation, but also improves the accessibility and personalization of medical services. This module enables patients to conveniently submit their needs, consultations, and appointment requests. Natural language processing technology will be used to understand and classify patients' requests so that they can be efficiently directed to the appropriate medical service providers. In addition, it is used to receive the doctor's response and pass the feedback information to the patient in an easy-to-understand form.

[0088] It also allows patients to upload their physiological parameter data via smartphones, wearable devices or home medical devices. It not only supports multiple data formats and upload methods, but also enables preliminary analysis of the uploaded data to quickly identify potential health problems.

[0089] The library learning and updating module 700 is used to establish a medical reserve library, add labels to each disease in the medical reserve library, and store the relevant data and final treatment plan of each patient in the medical reserve library.

[0090] Through precise data collection, storage, analysis and update mechanisms, it not only promotes the accumulation and utilization of medical knowledge, but also significantly improves the accuracy of medical decision-making and the effectiveness of treatment plans. This module will establish and maintain a comprehensive medical reserve library for storing relevant data on various diseases. This library not only includes standard disease information such as symptoms, treatment methods and prognosis, but is also subdivided into different label indexes, such as disease stage, patient population (age, gender, etc.), genetic information, etc., to facilitate more detailed data analysis and application.

[0091] In this module, each patient's disease data, treatment plan and effect are systematically stored under the corresponding disease label. This includes not only basic diagnosis and treatment information, but also patient feedback information, follow-up data and long-term tracking results, thus providing a rich and multi-dimensional data resource for the medical reserve library.

[0092] Figure 2 The structural block diagram of the data acquisition module provided by the embodiment of the present invention is as follows: Figure 2 As shown, the data acquisition module includes:

[0093] The institution docking unit 110 is used to dock with the information system of each medical institution, establish a communication connection, and automatically obtain and transmit relevant data of the patient;

[0094] The data management unit 120 is used to store, integrate and manage the collected patient-related data and add a patient name tag to the data of each patient;

[0095] The data encryption unit 130 encrypts the data of each patient.

[0096] Figure 3 The structural block diagram of the data analysis module provided by the embodiment of the present invention is as follows: Figure 3 As shown, the data analysis module includes:

[0097] A feature selection unit 210 is used to extract individual differences and features from the relevant data of each patient, including the patient's body shape data, genetic characteristics, clinical data and current disease data;

[0098] Individualized risk assessment unit 220, for assessing the patient's current disease transformation risk and predicting the potential path and speed of disease progression through the patient's clinical data and current disease data;

[0099] The effect evaluation unit 230 is used to analyze all the data of the patients who have received treatment and evaluate the effectiveness of the treatment plan for the patients.

[0100] As a further solution of the present invention, in the feature selection unit, extracting the individual differences and features in the relevant data of each patient, including the patient's body shape data, genetic characteristics, clinical data and current disease data, specifically comprises the following steps:

[0101] Acquire an original patient data set, the original patient data set includes individual difference data and feature data, perform standardization on the original patient data set to obtain a standardized patient data set, the standardized patient data set includes standardized individual difference data and standardized feature data;

[0102] ANOVA F value was used to analyze the F value scores between the standardized individual difference data and the standardized feature data, and the individual difference data with the first several high F value scores and the corresponding feature data were selected in descending order to form the first data set;

[0103] The mutual information method is used to analyze the nonlinear relationship between the standardized individual difference data and the standardized feature data to calculate the information gain value, and the individual difference data with the highest information gain value and the corresponding feature data are selected in descending order to form the second data set;

[0104] The principal component analysis method is used to analyze the principal component sharing degree of the standardized individual difference data, and the first several individual difference data with high principal component sharing degree and the corresponding characteristic data are selected in descending order to form the third data set;

[0105] The number of times the individual difference data and characteristic data are selected in the first data set, the second data set, and the third data set is counted, and the number of times selected is used as a measurement indicator to obtain individual differences and characteristics in the patient-related data.

[0106] As a further solution of the present invention, in the individualized risk assessment unit, the step of assessing the patient's current disease transformation risk and predicting the potential path and speed of disease progression through the patient's clinical data and current disease data specifically includes the following steps:

[0107] The clinical data of the patient includes several clinical characteristics, and a first interaction term is created according to the interaction relationship between different clinical characteristics;

[0108] Based on the Cox model, a multivariate model was constructed with several clinical characteristics and the first interaction term as multiple variables;

[0109] The multivariate model was incrementally fitted using stepwise regression operations to construct the optimal multivariate model;

[0110] The coefficient of each variable was estimated by the maximum likelihood estimation method to fit the optimal multivariate model. Based on the fitting results, the contribution of each variable to the baseline risk was calculated to obtain the contribution of clinical characteristics to the overall risk.

[0111] extracting genomic data from the genetic features, and determining genetic markers associated with the target disease from the genomic data;

[0112] Perform a single-factor statistical test on each genetic marker, calculate the significant p-value of each genetic marker, evaluate the impact of the genetic marker on the disease, and obtain several original p-values;

[0113] Bonferroni correction was used to adjust several original p values ​​to obtain several significant p values;

[0114] Based on Fisher's method, several significant p values ​​were combined to evaluate the genetic risk score of the overall genetic markers and obtain the overall contribution of genetic characteristics to disease risk;

[0115] Obtain current and historical disease data;

[0116] A time series analysis model is constructed using a dynamic Bayesian network, and the time series analysis model is trained using historical disease data to obtain a trained time series analysis model;

[0117] Input current disease data into the trained time series analysis model to obtain future risk status;

[0118] Based on the future risk status, the risk level at different time points is calculated by weighted average to obtain the comprehensive disease risk, and the potential path and speed of disease progression are generated based on the risk level at different time points;

[0119] The contribution of clinical characteristics to the overall risk, the overall contribution of genetic characteristics to disease risk, and the comprehensive disease risk are combined in a weighted combination to obtain a comprehensive risk assessment result and the current disease change risk.

[0120] As a further solution of the present invention, in the effect evaluation unit, analyzing all the data of the patients who have received treatment and evaluating the effectiveness of the treatment plan for the patients specifically includes the following steps:

[0121] All data of the patients who have received treatment include patient body shape data, genetic characteristics and clinical data, and the patient body shape data, genetic characteristics and clinical data are standardized to obtain standardized patient body shape data, standardized genetic characteristics and standardized clinical data;

[0122] The standardized patient body shape data is transformed nonlinearly using the fully connected layer to obtain body shape feature representation;

[0123] The fully connected layer is used to perform nonlinear transformation on the standardized genetic features to obtain the genetic feature representation;

[0124] The standardized clinical data is input into the long short-term memory network layer, and the long short-term memory network layer is used to capture the dynamic change trend of the patient's status over time to obtain the clinical feature representation;

[0125] The body shape feature representation, genetic feature representation and clinical feature representation are fused by splicing to obtain a comprehensive feature vector;

[0126] The comprehensive feature vector is subjected to self-attention technique to obtain the attention output;

[0127] Pass the attention output through the output layer to get the prediction result;

[0128] Obtain the known efficacy of the treatment plan, and perform weighted calculation on the known efficacy of the treatment plan and the predicted results according to the weights to obtain a comprehensive treatment effect index;

[0129] A comparison threshold is set and the comprehensive treatment effect index is compared with the comparison threshold. If the comprehensive treatment effect index exceeds the comparison threshold, the treatment plan is considered effective; otherwise, the treatment plan is considered ineffective.

[0130] Figure 4 The structure block diagram of the referral auxiliary decision module provided by the embodiment of the present invention is as follows: Figure 4 As shown, the referral decision support module includes:

[0131] The medical resource analysis unit 310 is used to obtain the medical resource information of all medical institutions in the current medical institution and the area where the patient is located, including the departments, medical equipment, vacant beds and medical level of each medical institution;

[0132] The patient demand assessment unit 320 is used to assess the medical level of the current medical institution in combination with the patient's disease development trend and current treatment effect, analyze the patient's treatment needs and the availability of medical resources of the medical institution, and determine whether the medical institution has the medical resources required to cure the corresponding patient;

[0133] The regional institution screening unit 330 is used to screen out all medical institutions that can meet the patient's treatment needs from other medical institutions within the patient's area according to the patient's treatment needs analysis result when the medical institution does not have the medical resources required to cure the corresponding patient;

[0134] The referral marking unit 340 is used to obtain the medical institution selected by the patient, establish communication with the medical institution, synchronize all the patient's data to the information database of the medical institution, and add a transfer mark for the patient.

[0135] Figure 5 The structural block diagram of the comprehensive data sharing module provided by the embodiment of the present invention is as follows: Figure 5 As shown, the comprehensive data sharing module includes:

[0136] The communication establishment unit 410 is used to establish a data sharing platform and, according to the coverage of the data sharing platform, establish communication channels with all medical institutions within the coverage area;

[0137] A data sharing permission unit 420 is used to set the access permission of the data sharing platform, wherein the access permission is set to be accessible only to doctors;

[0138] The data upload synchronization unit 430 is used to upload the patient information that needs to be referred to the data sharing platform, and encrypt the uploaded data, and the encryption key will only be synchronized to the medical institution selected by the patient.

[0139] Figure 6 The structural block diagram of the health management monitoring module provided by the embodiment of the present invention is as follows: Figure 6 As shown, the health management monitoring module includes:

[0140] A real-time monitoring unit 510 is used to connect with the sensor device worn by the patient to collect the patient's physiological parameter data and the patient's disease development trend data;

[0141] The standard setting unit 520 is used to set a personalized physiological expected threshold value according to the physiological parameter data and current disease state of each patient;

[0142] The abnormality detection unit 530 is used to compare and analyze the monitoring data in real time, and compare each acquired monitoring data with the set physiological expectation threshold. When any monitoring data exceeds the physiological expectation threshold, a warning message is issued.

[0143] Figure 7 The structure diagram of the autonomous participation management module provided by the embodiment of the present invention is as follows: Figure 7 As shown, the autonomous participation management module includes:

[0144] The application information receiving unit 610 is used to interact with the patient, receive the patient's requirements, consultation and appointment information, and receive the doctor's reply to the interactive information and feed it back to the patient;

[0145] The parameter autonomous uploading unit 620 is used to receive physiological parameter data actively uploaded by the patient.

[0146] Figure 8 The structural block diagram of the library learning update module provided by the embodiment of the present invention is as follows: Figure 8 As shown, the library learning update module includes:

[0147] A library building unit 710 is used to build a medical library to store disease-related data and add a label index to each disease in the medical library;

[0148] The patient data storage unit 720 is used to store the patient's information in a tag under the same index according to the patient's disease data, and obtain the treatment plan and treatment effect adopted by the patient this time, and bind and store them in the tag.

[0149] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0150] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0151] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart medical platform based on big data, characterized in that: The platform includes: The data acquisition module is used to connect with the information system of medical institutions to automatically obtain and manage relevant patient data, including medical records, physiological parameters, genomic data and medical records; The data analysis module is used to analyze the automatically acquired data, identify individual differences and characteristics in the patient group, and combine the relevant data of each patient to predict the disease development trend and evaluate the current treatment effect of the patient; The referral decision-making support module is used to obtain the availability of medical resources of the current medical institution and medical institutions within the region, and to determine whether the availability of medical resources of the current medical institution can meet the patient's cure needs in combination with the patient's disease development trend prediction and current treatment effect. If it cannot be met, all medical institutions within the region that can meet the patient's cure needs are identified, and a transfer mark is added for the patient; A comprehensive data sharing module is used to establish a data sharing platform within the region, and the data sharing platform establishes communication channels with all medical institutions within the region, and stores the patient-related data with transfer marks in the data sharing platform in encrypted form; The health management monitoring module is used to monitor the patient's physiological parameters and disease development trend data in real time, and set physiological expected thresholds based on the relevant data of each patient, and issue warning information when the monitored parameters and data exceed the physiological expected thresholds; The autonomous participation management module is used to receive the patient's application information and the physiological parameters actively uploaded by the patient, and to feed back the doctor's feedback instructions to the patient; The library learning and updating module is used to establish a medical reserve library and add labels to each disease in the medical reserve library, storing the relevant data of each patient and the final treatment plan in the medical reserve library; The data analysis module includes: A feature selection unit is used to extract individual differences and features from each patient's relevant data, including patient body shape data, genetic characteristics, clinical data, and current disease data; Individualized risk assessment unit, which is used to assess the patient's current disease transformation risk and predict the potential path and speed of disease progression based on the patient's clinical data and current disease data; The effectiveness evaluation unit is used to analyze all the data of the patients who have received treatment and evaluate the effectiveness of the treatment plan for the patients; In the feature selection unit, the extraction of individual differences and features in the relevant data of each patient, including the patient's body shape data, genetic characteristics, clinical data and current disease data, specifically includes the following steps: Acquire an original patient data set, the original patient data set includes individual difference data and feature data, perform standardization on the original patient data set to obtain a standardized patient data set, the standardized patient data set includes standardized individual difference data and standardized feature data; ANOVA F value was used to analyze the F value scores between the standardized individual difference data and the standardized feature data, and the individual difference data with the first several high F value scores and the corresponding feature data were selected in descending order to form the first data set; The mutual information method is used to analyze the nonlinear relationship between the standardized individual difference data and the standardized feature data to calculate the information gain value, and the individual difference data with the highest information gain value and the corresponding feature data are selected in descending order to form the second data set; The principal component analysis method is used to analyze the principal component sharing degree of the standardized individual difference data, and the first several individual difference data with high principal component sharing degree and the corresponding characteristic data are selected in descending order to form the third data set; Counting the number of times the individual difference data and characteristic data are selected in the first data set, the second data set, and the third data set, using the number of times selected as a measurement indicator, to obtain individual differences and characteristics in the patient-related data; In the individualized risk assessment unit, the step of assessing the patient's current disease transformation risk and predicting the potential path and speed of disease progression through the patient's clinical data and current disease data specifically includes the following steps: The clinical data of the patient includes several clinical characteristics, and a first interaction term is created according to the interaction relationship between different clinical characteristics; Based on the Cox model, a multivariate model was constructed with several clinical characteristics and the first interaction term as multiple variables; The multivariate model was incrementally fitted using stepwise regression operations to construct the optimal multivariate model; The coefficient of each variable was estimated by the maximum likelihood estimation method to fit the optimal multivariate model. Based on the fitting results, the contribution of each variable to the baseline risk was calculated to obtain the contribution of clinical characteristics to the overall risk. extracting genomic data from the genetic features, and determining genetic markers associated with the target disease from the genomic data; Perform a single-factor statistical test on each genetic marker, calculate the significant p-value of each genetic marker, evaluate the impact of the genetic marker on the disease, and obtain several original p-values; Bonferroni correction was used to adjust several original p values ​​to obtain several significant p values; Based on Fisher's method, several significant p values ​​were combined to evaluate the genetic risk score of the overall genetic markers and obtain the overall contribution of genetic characteristics to disease risk; Obtain current and historical disease data; A time series analysis model is constructed using a dynamic Bayesian network, and the time series analysis model is trained using historical disease data to obtain a trained time series analysis model; Input current disease data into the trained time series analysis model to obtain future risk status; Based on the future risk status, the risk level at different time points is calculated by weighted average to obtain the comprehensive disease risk, and the potential path and speed of disease progression are generated based on the risk level at different time points; The contribution of clinical characteristics to the overall risk, the overall contribution of genetic characteristics to the disease risk, and the comprehensive disease risk are combined in a weighted combination to obtain a comprehensive risk assessment result and the current disease change risk; In the effect evaluation unit, analyzing all the data of the patients who have received treatment and evaluating the effectiveness of the treatment plan for the patients specifically includes the following steps: All data of the patients who have received treatment include patient body shape data, genetic characteristics and clinical data, and the patient body shape data, genetic characteristics and clinical data are standardized to obtain standardized patient body shape data, standardized genetic characteristics and standardized clinical data; The standardized patient body shape data is transformed nonlinearly using the fully connected layer to obtain body shape feature representation; The fully connected layer is used to perform nonlinear transformation on the standardized genetic features to obtain the genetic feature representation; The standardized clinical data is input into the long short-term memory network layer, and the long short-term memory network layer is used to capture the dynamic change trend of the patient's status over time to obtain the clinical feature representation; The body shape feature representation, genetic feature representation and clinical feature representation are fused by splicing to obtain a comprehensive feature vector; The comprehensive feature vector is subjected to self-attention technique to obtain the attention output; Pass the attention output through the output layer to get the prediction result; Obtain the known efficacy of the treatment plan, and perform weighted calculation on the known efficacy of the treatment plan and the predicted results according to the weights to obtain a comprehensive treatment effect index; A comparison threshold is set and the comprehensive treatment effect index is compared with the comparison threshold. If the comprehensive treatment effect index exceeds the comparison threshold, the treatment plan is considered effective; otherwise, the treatment plan is considered ineffective.

2. The big data-based smart medical platform according to claim 1, characterized in that: The data acquisition module comprises: The institution docking unit is used to connect with the information system of each medical institution, establish a communication connection, and automatically obtain and transmit relevant patient data; A data management unit is used to store, integrate and manage the collected patient-related data and add a patient name tag to each patient's data; The data encryption unit encrypts the data of each patient.

3. The big data-based smart medical platform according to claim 1, characterized in that: The referral auxiliary decision module includes: The medical resource analysis unit is used to obtain the medical resource information of all medical institutions in the current medical institution and the area where the patient is located, including the departments, medical equipment, vacant beds and medical level of each medical institution; The patient needs assessment unit is used to evaluate the medical level of the current medical institution based on the patient's disease development trend and current treatment effect, analyze the patient's treatment needs and the availability of medical resources of the medical institution, and determine whether the medical institution has the medical resources required to cure the corresponding patient; The regional institution screening unit is used to screen out all medical institutions that can meet the patient's treatment needs from other medical institutions within the patient's area according to the patient's treatment needs analysis results when the medical institution does not have the medical resources required to cure the corresponding patient; The referral marking unit is used to obtain the medical institution selected by the patient, establish communication with the medical institution, synchronize all the patient's data to the information database of the medical institution, and add a transfer mark for the patient.

4. The big data-based smart medical platform according to claim 3, characterized in that: The comprehensive data sharing module includes: A communication building unit is used to establish a data sharing platform and, based on the coverage of the data sharing platform, establish communication channels with all medical institutions within the coverage area; A data sharing permission unit, used to set the access permission of the data sharing platform, wherein the access permission is set to be accessible only to doctors; The data upload synchronization unit is used to upload the patient information that needs to be referred to the data sharing platform and encrypt the uploaded data. The encryption key will only be synchronized to the medical institution selected by the patient.

5. The big data-based smart medical platform according to claim 4, characterized in that: The health management monitoring module includes: A real-time monitoring unit is used to connect with the sensor equipment worn by the patient to collect the patient's physiological parameter data and the patient's disease development trend data; A standard setting unit, used to set personalized physiological expected thresholds based on each patient's physiological parameter data and current disease status; The anomaly detection unit is used to compare and analyze the monitoring data in real time, and compare each acquired monitoring data with the set physiological expected threshold. When any monitoring data exceeds the physiological expected threshold, a warning message is issued.

6. The big data-based smart medical platform according to claim 5, characterized in that: The autonomous participation management module includes: The application information receiving unit is used to interact with the patient, receive the patient's needs, consultation and appointment information, and receive the doctor's reply to the interactive information and feed it back to the patient; Parameter autonomous uploading unit, used to receive physiological parameter data actively uploaded by patients; The library learning update module includes: The library establishment unit is used to build a medical library to store disease-related data and add a label index to each disease in the medical library; The patient data storage unit is used to store the patient's information in a tag under the same index according to the patient's disease data, and obtain the treatment plan and treatment effect adopted by the patient this time, and bind and store them in the tag.

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