Personalized medical decision support system based on big data analysis

Abstract

The invention provides an innovative personalized medical decision support system, and aims to generate highly-customized medical suggestions by comprehensively analyzing real-time physiological data and historical medical records of a patient in combination with latest clinical guidelines and expert experience. The system adopts an advanced data acquisition technology to monitor patient conditions in real time, uses a data warehouse technology to integrate historical medical data, and deeply mines data values through machine learning and deep learning algorithms. Besides, the system has adaptive learning ability, and can continuously optimize the decision model according to new medical data and user feedback, so as to realize more accurate and timely medical decision support. The design of the system not only improves the quality and efficiency of medical services, but also provides more personalized medical experience for patients, and is expected to bring revolutionary change in the medical field.

Description

Technical Field

[0001] The present invention relates to the field of medical health, and in particular to a system that uses big data technology, machine learning algorithms and artificial intelligence to provide personalized medical decision support for patients. Background Art

[0002] In the current healthcare field, medical decision support systems (CDSS) have become an important tool for improving the quality and efficiency of medical services. These systems usually provide doctors with diagnosis and treatment recommendations based on clinical guidelines and expert knowledge. However, traditional CDSS have some limitations, including insufficient consideration of individual differences among patients, inability to respond to changes in patients' physiological status in real time, and lack of ability to quickly integrate emerging medical research results.

[0003] With the development and application of big data technology, a large amount of patient data has been accumulated in the medical field, including electronic health records, laboratory test results, medical imaging data, etc. The in-depth mining and analysis of these data has made personalized medicine possible. However, how to effectively integrate and analyze these data to generate medical advice that meets the individual patient's situation remains a technical challenge.

[0004] The experience and knowledge of medical experts are indispensable resources in medical decision-making. However, expert knowledge often exists in an unstructured form and is difficult to directly apply to automated decision support systems. Therefore, how to effectively transform expert knowledge into a computable model is the key to improving the performance of CDSS.

[0005] In view of the above background, the present invention aims to develop a new personalized medical decision support system, which can make full use of big data technology, machine learning algorithms and artificial intelligence technology, overcome the limitations of existing technologies, and provide patients with more accurate and personalized medical services. Summary of the invention

[0006] The personalized medical decision support system of the present invention is a highly integrated intelligent platform, and its operation process is as follows: the real-time data acquisition module uses advanced monitoring equipment, such as smart watches and heart rate monitors, to capture the patient's key physiological indicators in real time. These indicators include heart rate, blood pressure, blood sugar and body temperature, etc., providing the system with a dynamic and continuous health data stream. Next, after receiving the real-time data, the historical data integration module merges it with the patient's historical medical records. This module performs data cleaning and standardization processing to ensure the accuracy and consistency of the data and build a comprehensive and detailed patient health profile. Subsequently, the clinical guideline adaptation module and the expert knowledge base are updated synchronously to integrate the latest clinical research results and medical experts' advice into the decision-making process. This ensures that the medical advice generated by the system is both scientific and authoritative, and meets current medical practices and regulatory requirements. The core of the system is the data analysis and processing engine, which uses statistics and machine learning techniques to conduct in-depth analysis of the collected data. Through feature extraction and model training, the engine can identify health risks and predict disease development trends. Based on the output of the data analysis engine, the personalized advice generation module is further developed to generate customized medical advice and treatment plans based on the patient's specific circumstances and preferences. These suggestions are intended to provide patients with the most appropriate medical services. To continuously improve the accuracy and applicability of recommendations, the Adaptive Learning Module continuously learns and adjusts the decision-making algorithm based on feedback from medical professionals and patients. This module uses reinforcement learning technology to enable the system to automatically optimize and adapt to new medical data and user needs. User interaction with the system is carried out through the user interaction interface, which provides an intuitive and easy-to-use operation experience. Medical professionals and patients can easily access the system, view recommendations, and enter feedback to participate in the decision-making process. Finally, the Security and Privacy Protection Module ensures that all patient data is securely stored and transmitted throughout the system. By using advanced encryption technology and access control, this module protects patient privacy, complies with data protection regulations, and enhances user trust in the system.

[0007] Preferably, the operation process of the real-time data acquisition module is as follows: in the device pairing and initialization stage, the real-time data acquisition module is paired with the smart device worn by the patient through wireless communication technology. This process ensures the secure connection between devices and the accuracy of data transmission, laying the foundation for subsequent data synchronization. In the wireless communication establishment stage, after the pairing is successful, a stable data transmission channel is established between the module and the wearable device. This step is crucial because it ensures that the patient's physiological index data can be transmitted to the system in real time and continuously. Entering the real-time data acquisition and transmission stage, the wearable device captures the patient's physiological indicators, such as heart rate, blood pressure, etc., in real time according to the preset monitoring frequency, and sends these data to the real-time data acquisition module through wireless communication technology. In the data preprocessing stage, after the module receives the data, it immediately performs necessary preprocessing, including data format conversion, timestamp marking and outlier detection, to ensure the quality and availability of the data. In the data synchronization to the central database stage, the preprocessed data is securely synchronized to the central database of the system. This process involves data encryption, which ensures the protection of patient privacy during transmission. In the data integration and subsequent analysis stage, the data synchronized to the database is received by the historical data integration module and integrated with the patient's historical medical records, providing a comprehensive data foundation for the data analysis and processing engine. In the adaptive adjustment and optimization stage, the system dynamically adjusts the frequency and parameters of data collection through the adaptive learning module based on data analysis results and user feedback to optimize monitoring effects and data quality. In the continuous monitoring and dynamic update stage, the real-time data acquisition module continues to run, maintaining continuous monitoring of patients' physiological indicators, ensuring real-time data updates, providing the system with the latest patient status information, and supporting dynamic medical decision-making processes.

[0008] Preferably, the historical data integration module ensures the accuracy and consistency of the data through a series of refined processes. The module first receives patient historical data from multiple medical information systems, and then performs data deduplication through advanced algorithms, identifies and eliminates duplicate records, and ensures the uniqueness of the data. Next, the module performs error detection, verifies the accuracy of the data using predefined rules and logic, removes or marks outliers and logical errors, and ensures the reliability of the data. Subsequently, the module standardizes the data, converts data from different sources and formats into a unified format and unit, such as unifying millimeters of mercury (mmHg) and kilopascals (kPa) of blood pressure into international standard units, and adjusts the data structure of different medical institutions to conform to the data model within the system. In addition, the historical data integration module is also responsible for the continuous updating and maintenance of data to reflect the patient's latest medical condition, while strictly complying with data security and privacy protection regulations throughout the process to ensure the confidentiality and compliance of patient information.

[0009] Preferably, the clinical guideline adaptation module ensures that the system is always in sync with the latest medical research results through a carefully designed process. The module uses web crawler technology to regularly retrieve the latest clinical guidelines and research papers from authoritative medical databases and journals, and identifies updates related to existing medical practices through set keywords and algorithm filtering. The module uses natural language processing technology to extract key information and convert it into actionable guidelines and parameters, which are updated to the system. In addition, the module also integrates an expert verification mechanism, inviting medical experts to review and verify the updated content to ensure its scientificity and applicability.

[0010] Preferably, the expert knowledge base module is designed for medical experts and provides an intuitive online interface that allows experts to update and maintain knowledge items at least once a quarter. The module verifies the identity of experts through a secure login protocol to ensure that only authorized medical professionals can access the update function. Experts can submit the latest medical insights, treatment plans and research results through a user-friendly interface, with each submission accompanied by detailed references and practical cases. The system's built-in review mechanism ensures that each new knowledge item is peer-reviewed by at least 3 other experts in the field before it is officially released, and the review process is completed within an average of 15 working days. Through this rigorous peer review and quality control process, the expert knowledge base can continuously integrate the latest professional medical knowledge and provide the system with reliable and cutting-edge decision support information. The update history and version control function of the knowledge base make the change process of each knowledge item traceable and transparent, enhancing the trust and reliability of the system.

[0011] The preferred data analysis and processing engine is a carefully designed multi-submodule system dedicated to deeply mining and analyzing patient data to provide accurate medical insights. The engine's operating process first starts with the feature extraction submodule, which uses cluster analysis and association rule mining techniques to accurately identify key physiological indicators and disease patterns with an accuracy rate of more than 90%. Next, the model training submodule intervenes, using a series of advanced algorithms, including support vector machines (SVMs), random forests, and deep neural networks, to conduct in-depth learning of these key features and build a prediction model with an accuracy of more than 95% on the training set, ensuring the high predictive performance of the model. The validation submodule uses the K-fold cross-validation method (K value is set to 10) to carefully test and validate each model to evaluate its generalization ability on unknown data, ensuring that the error rate of the validation process does not exceed 5%. This continuous analysis and validation process not only ensures the scientificity and effectiveness of the model, but also enables the data analysis and processing engine to output highly reliable and personalized medical decision support information through continuous optimization and adjustment.

[0012] The preferred personalized advice generation module follows a strict process to ensure that accurate medical advice is generated for each patient. The module first analyzes the correlation between real-time physiological data and historical medical records through a feature matching algorithm with an accuracy rate of more than 95%, integrating basic information about the patient, such as age, gender, and past medical history. Then, the module uses decision trees and logistic regression models to calculate personalized treatment plans based on the latest clinical guidelines and the verification opinions of at least three experts. At the same time, the module also considers the patient's personal preferences, such as drug allergies or preferences for treatment methods, and ensures that these factors are fully reflected in the advice through the patient preference analysis tool. Finally, with a personalized matching rate of more than 90%, the module outputs medical advice that comprehensively considers the patient's specific situation and preferences, and presents it to medical professionals and patients in a concise and clear manner through the user interface for further discussion and decision-making. In addition, the module also optimizes itself based on user feedback, and each suggestion is evaluated by a scoring system to ensure the practicality and effectiveness of the advice, achieving at least 90% user satisfaction.

[0013] The preferred adaptive learning module uses reinforcement learning technology to continuously improve the accuracy of medical advice. The module first collects feedback data from the user interface, including the proportion of doctors adopting advice, the patient's satisfaction score for the advice (1 to 10 points), and the percentage of improvement in the patient's recovery speed after treatment. The module has efficient data processing capabilities and can process at least 5,000 feedback messages per second and adjust model parameters in real time. The module uses a reward function to quantify the effect of each suggestion. The reward function is as follows: Reward score = 0.5X adoption ratio + 0.3X (satisfaction score - 5) + 0.2X recovery improvement percentage. The reward function comprehensively considers the frequency of suggestion adoption, patient satisfaction and treatment effect, and each indicator is given different weights according to its importance. If a suggestion is adopted by doctors in most cases, patients give high scores, and the patient's recovery speed is significantly improved, then this suggestion will receive a higher reward score. Such suggestions will be considered and adopted more frequently in future decision-making processes. In order to ensure that the system can quickly adapt to new medical data and user needs, the adaptive learning module is updated at least once every 24 hours. Through this mechanism, the module aims to achieve a feedback response rate of at least 90% and increase the accuracy of recommendations by at least 5%, thereby significantly improving the accuracy and effectiveness of personalized medical recommendations.

[0014] Preferably, the user interaction interface module is specially designed with multilingual support to ensure that medical professionals and patients from different language backgrounds can use the system without obstacles. The interface uses advanced localization technology and can automatically switch to the corresponding language version according to the language setting of the user's device or personal preference. It currently supports more than 10 languages ​​including Chinese, English, Spanish, etc. After the user logs in to the system, the interface will immediately identify and apply the user's preferred language to ensure that all prompts, medical advice and operation instructions are presented in the language familiar to the user. The interface design focuses on simplicity and intuitiveness, using clear layouts and icons so that users of different languages ​​can quickly understand the various functions. For users with language barriers or reading difficulties, the system also provides a voice reading function that can convert text into voice output to further optimize the user experience. The user interaction interface also integrates a real-time feedback mechanism, allowing users to evaluate and feedback on medical advice directly on the interface. These feedbacks will be recorded by the system and used for continuous optimization of the adaptive learning module. During system upgrades or maintenance, the user interaction interface will inform relevant information in the language selected by the user through push notifications to ensure that users remain aware of the system status.

[0015] The detailed steps and processes of the personalized medical decision-making method of the system are as follows: S1. Real-time data collection: Through the real-time data collection module, the system collects the patient's physiological index data at an interval of no more than 5 seconds, including but not limited to heart rate, blood pressure, blood sugar and body temperature, to ensure the real-time and continuity of the data.

[0016] S2. Historical data integration: The historical data integration module imports and processes the patient's medical records for at least the past year, including outpatient records, hospitalization records, laboratory test results, and imaging reports, to build a comprehensive patient health record.

[0017] S3. Clinical guideline synchronization: The clinical guideline adaptation module automatically synchronizes with the authoritative medical database at least once a quarter through the API interface to obtain the latest clinical guidelines and medical research results.

[0018] S4. Expert knowledge retrieval: The expert knowledge base provides at least 1,000 peer-reviewed expert experiences and suggestions. The system retrieves the opinions of at least 3 experts in related fields based on the patient's condition.

[0019] S5. Data Analysis and Processing: The data analysis and processing engine uses feature extraction technology to identify more than 500 key features and uses machine learning models with an accuracy of more than 95% for training and validation.

[0020] S6. Personalized advice generation: The personalized advice generation module generates at least three preliminary medical advice plans based on the patient's specific situation. Each plan includes detailed treatment steps and expected results.

[0021] S7. Adaptive learning and suggestion optimization: The adaptive learning module uses a reinforcement learning algorithm based on at least 10 user feedbacks to adjust and optimize suggestions in real time with an accuracy of 98%.

[0022] S8. User interaction and feedback collection: The user interaction interface supports at least 20 languages ​​to ensure barrier-free access for users around the world, and collects at least 95% of user feedback through the interface for subsequent decision support.

[0023] S9. Security and privacy protection: The security and privacy protection module implements AES 256-bit encryption on all patient data to ensure the security of data during transmission and storage, in compliance with international data protection standards.

[0024] S10. Continuous optimization loop: Based on user feedback and newly collected data, the system performs a cycle from data analysis to recommendation optimization at least once every 24 hours to achieve continuous decision optimization to adapt to changes in patient conditions and updates to the medical environment.

[0025] Through this process, the personalized medical decision-making method not only ensures the personalization and accuracy of medical advice, but also improves the quality and efficiency of medical services through continuous learning and optimization, while ensuring the security and privacy of patient data.

[0026] The system of the present invention realizes a highly integrated and automated personalized medical decision support system through the above modules and processes, which can provide efficient and accurate medical services to medical professionals and patients.

[0027] The technical solution of the present invention can bring the following significant technical effects: 1. Improve the accuracy and personalization of medical decision-making: Through the real-time data collection module and the historical data integration module, the system can comprehensively analyze the patient's physiological indicators and historical medical records, and combine the clinical guideline adaptation module and the expert knowledge base to generate medical advice that meets the individual differences of patients. This in-depth personalized analysis ensures the accuracy of medical decisions and can provide customized treatment plans based on the patient's specific situation.

[0028] 2. Enhance the timeliness and dynamic adaptability of medical decision-making: The adaptive learning module uses reinforcement learning technology to continuously optimize the decision-making algorithm based on medical feedback and new data, enabling the system to quickly respond to changes in patient conditions and updates to the medical environment. This dynamic adaptability significantly improves the timeliness of medical decision-making and ensures that patients can receive the most appropriate medical advice in a timely manner.

[0029] 3. Ensure the security and privacy of patient data: The design of the security and privacy protection module ensures the safe storage and transmission of patient information throughout the system. Through advanced encryption technology and access control, the system protects the privacy of patients while complying with relevant data protection regulations. This not only enhances patients' trust in the system, but also provides medical institutions with a data management solution that complies with regulatory requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a diagram of the personalized medical decision support system of the present invention Figure 2 This is a step diagram of the personalized medical decision-making method of the present invention DETAILED DESCRIPTION

[0031] The following will be combined with the attached embodiment of the present invention Figure 1 and Figure 2 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] A personalized medical decision support system includes: a real-time data acquisition module, a historical data integration module, a clinical guideline adaptation module, an expert knowledge base, a data analysis and processing engine, a personalized suggestion generation module, an adaptive learning module, a user interaction interface, and a security and privacy protection module. The real-time data acquisition module captures the patient's physiological indicators in real time through advanced monitoring equipment. The historical data integration module integrates the received real-time data with the patient's historical medical records and performs data cleaning and standardization processing to build a comprehensive patient health record. The clinical guideline adaptation module and the expert knowledge base are updated synchronously, and the latest research results and expert experience are integrated into the decision-making process to ensure the scientificity and authority of the suggestions. The data analysis and processing engine uses statistics and machine learning techniques to conduct in-depth analysis of data. The personalized suggestion generation module generates customized medical suggestions based on the analysis results. The adaptive learning module continuously optimizes the algorithm based on feedback to improve the accuracy of the suggestions. The user interaction interface provides an intuitive operating platform to facilitate medical professionals and patients to access and respond to suggestions. The security and privacy protection module ensures data security and patient privacy.

[0033] The operation process of the real-time data acquisition module is refined and coherent: in the device pairing and initialization stage, the module uses Bluetooth 5.0 wireless communication technology to pair with the smart device worn by the patient, ensuring the secure connection between the devices and the accuracy of data transmission. The transmission rate reaches 2Mbps per second, laying the foundation for subsequent data synchronization. Entering the wireless communication establishment stage, after successful pairing, a stable data transmission channel is established between the module and the wearable device, ensuring that the physiological index data updated every 5 seconds, such as heart rate, blood pressure, etc., can be transmitted to the system in real time and continuously. The real-time data acquisition and transmission stage is then started, and the wearable device captures and sends data in real time according to the preset monitoring frequency. Subsequently, in the data preprocessing stage, after receiving the data, the module performs format conversion, timestamp marking (accurate to milliseconds) and outlier detection to ensure the quality and availability of the data. In the data synchronization to the central database stage, the data is securely synchronized with the AES 256-bit encryption standard to ensure the protection of patient privacy during the transmission process. In the data integration and subsequent analysis stage, the data synchronized to the database is integrated with the patient's historical medical records, providing a data foundation for the data analysis and processing engine to process millions of records per second. In the adaptive adjustment and optimization stage, the system dynamically adjusts the frequency of data collection based on data analysis results and user feedback to optimize monitoring effects and data quality. Finally, in the continuous monitoring and dynamic update stage, the real-time data acquisition module runs 24 / 7 to maintain continuous monitoring of patients' physiological indicators, ensure real-time data updates, provide the system with the latest patient status information, and support the dynamic process of processing thousands of medical decisions per second.

[0034] The historical data integration module has efficient data deduplication, error detection and standardization processing functions. In the data deduplication stage, the module uses hash algorithms and timestamp comparison to identify and eliminate duplicate records with 99.9% accuracy. In the error detection stage, through the set medical parameter range and logical rules, the module can detect and mark abnormal data points with more than 95% accuracy, such as identifying records that exceed the normal blood pressure range (90 / 60 mmHg to 120 / 80 mmHg) as potential errors. During the standardization process, the module converts the data into a unified International System of Units (SI), such as converting milligrams per deciliter (mg / dL) of blood glucose to millimoles per liter (mmol / L) to ensure the consistency of the data format. In addition, the module automatically updates patient records every hour to maintain the timeliness of the data, and uses AES 256-bit encryption technology to protect patient privacy, in compliance with data protection standards such as HIPAA and GDPR. Through these precisely controlled processes, the historical data integration module provides the system with an accurate, consistent and secure data foundation to support highly personalized medical decisions.

[0035] The clinical guideline adaptation module has an automatic update function to ensure that the system's decisions are synchronized with the latest medical research results. The module is set to automatically perform a comprehensive search every 90 days. Through advanced web crawler technology, it conducts real-time monitoring and data collection from more than 3,000 authoritative medical databases and journals based on 1,000 predefined keywords. Using natural language processing technology, the module analyzes more than 10,000 latest research papers every hour, extracts key treatment recommendations and clinical indicators, and converts this information into system-executable guidelines with an accuracy rate of 98%. At the same time, the module implements an expert verification mechanism, inviting at least 5 medical experts to review the updated content to ensure that each update is verified within 7 working days. Through these processes, the clinical guideline adaptation module achieves rapid response and accurate integration of medical knowledge, ensuring that the system can provide medical advice based on the latest medical research, while ensuring the scientificity and authority of the advice.

[0036] The expert knowledge base module enables direct participation of experts through a secure and user-friendly online interface, allowing them to update and maintain knowledge items online at least once a quarter. The module uses a multi-factor authentication system to ensure that only verified medical experts can update knowledge, ensuring the authority and security of information. When experts submit updates through the interface, they must provide at least two peer-reviewed references to support the scientific nature of their insights. The system's built-in automated review process ensures that each submission is peer-reviewed by at least 3 experts in the relevant field within 7 working days, and the review pass rate must reach more than 95% before it can be included in the knowledge base. In addition, the knowledge base's version control system records the historical changes of each knowledge entry, achieving 100% traceability of changes, thereby ensuring the continuous updating and high reliability of the knowledge base content, providing the system with an evolving professional knowledge base.

[0037] The data analysis and processing engine is a high-performance analysis tool consisting of feature extraction, model training, and validation submodules. The feature extraction submodule identifies key data features with 90% accuracy, reduces data dimensions, and highlights important variables by applying principal component analysis (PCA) and correlation analysis. The model training submodule uses machine learning algorithms, including but not limited to support vector machines (SVMs), random forests, and deep neural networks (DNNs), to train feature data, where the depth of the DNN model is usually set to 5 layers, and the accuracy of model training exceeds 95%. The validation submodule performs K-fold cross validation (K=10) to ensure the stability and generalization ability of the model, and control the validation error rate of the model within 5%. The entire process is carried out in a high-configuration computing environment to ensure the speed and efficiency of data processing and provide the system with scientific and accurate personalized medical advice.

[0038] The Personalized Recommendation Generation Module provides highly customized medical advice to patients through a series of sophisticated analytical processes. The module first uses a feature matching algorithm to identify the correlation between the patient's current condition and historical data with 95% accuracy. Then, a logistic regression model and decision tree are applied to conduct personalized analysis in combination with the patient's specific information, such as age, gender, and past medical history. During the analysis process, the module also considers the patient's treatment preferences, such as the preference for drug selection (measured on a scoring system of 1 to 5) and personal inclinations for treatment methods (such as preference for surgical versus non-surgical options). Finally, the Personalized Recommendation Generation Module combines all these parameters to output a set of medical recommendations that comprehensively consider the patient's specific situation and preferences with a personalized matching rate of over 90%. These recommendations are presented in an easy-to-understand format through a user interface, ensuring that medical professionals and patients can make informed decisions based on full understanding.

[0039] The adaptive learning module uses reinforcement learning technology to continuously improve the accuracy of recommendations. The module first collects feedback data from the user interface, including the adoption rate of generated recommendations by medical professionals, patient satisfaction scores, and quantitative evaluations of treatment effects. Using this feedback, the module adjusts model parameters in real time with the ability to process thousands of feedback messages per second. The reinforcement learning algorithm is continuously optimized in this process, setting reward functions to encourage behaviors that receive positive feedback while reducing those that do not achieve the expected results. For example, if a recommendation improves the patient's recovery speed in multiple cases, the parameter settings related to the recommendation will receive a higher reward score and will be adopted more frequently in future decisions. The iteration cycle of the adaptive learning module is set to update at least once every 24 hours to ensure that the system can quickly adapt to new medical data and user needs. Through this process of continuous learning and self-optimization, the adaptive learning module significantly improves the accuracy of personalized medical recommendations and the effectiveness of clinical applications.

[0040] The user interface module provides powerful multi-language support, covering more than 20 languages, including Chinese, English, Spanish, French, German, etc., to meet the specific needs of users in different countries and regions. The interface uses adaptive language recognition technology, which can automatically switch to the user's preferred language environment with 99% accuracy when the user logs in through the device language setting or manual selection by the user. The interface design follows the internationally accepted barrier-free design standards to ensure that users of all language versions can enjoy consistent ease of use and accessibility. The interface integrates speech synthesis technology to support the conversion of text content into speech output, with a speech synthesis accuracy of 98%, providing assistance for users with visual impairments or reading difficulties. The real-time feedback mechanism allows users to directly evaluate and feedback medical suggestions in at least 5 different feedback types (such as satisfaction ratings, comments, and suggestion adoption status) on the interface. These feedback data collect at least 10 feedbacks for each suggestion, providing a basis for continuous optimization of the system. When the system is upgraded or maintained, the user interface pushes notifications in multiple languages ​​to ensure that users receive system status updates in a timely manner, and the arrival rate of push notifications remains above 95%. Through these meticulous designs and functional implementations, the user interface module has greatly enhanced the internationalization level of the system and user experience, providing barrier-free personalized medical decision support for global users.

[0041] The detailed steps and processes of the personalized medical decision-making method of the system are as follows: S1. Real-time data collection: Through the real-time data collection module, the system collects the patient's physiological index data at an interval of no more than 5 seconds, including but not limited to heart rate, blood pressure, blood sugar and body temperature, to ensure the real-time and continuity of the data.

[0042] S2. Historical data integration: The historical data integration module imports and processes the patient's medical records for at least the past year, including outpatient records, hospitalization records, laboratory test results, and imaging reports, to build a comprehensive patient health record.

[0043] S3. Clinical guideline synchronization: The clinical guideline adaptation module automatically synchronizes with the authoritative medical database at least once a quarter through the API interface to obtain the latest clinical guidelines and medical research results.

[0044] S4. Expert knowledge retrieval: The expert knowledge base provides at least 1,000 peer-reviewed expert experiences and suggestions. The system retrieves the opinions of at least 3 experts in related fields based on the patient's condition.

[0045] S5. Data Analysis and Processing: The data analysis and processing engine uses feature extraction technology to identify more than 500 key features and uses machine learning models with an accuracy of more than 95% for training and validation.

[0046] S6. Personalized advice generation: The personalized advice generation module generates at least three preliminary medical advice plans based on the patient's specific situation. Each plan includes detailed treatment steps and expected results.

[0047] S7. Adaptive learning and suggestion optimization: The adaptive learning module uses a reinforcement learning algorithm based on at least 10 user feedbacks to adjust and optimize suggestions in real time with an accuracy of 98%.

[0048] S8. User interaction and feedback collection: The user interaction interface supports at least 20 languages ​​to ensure barrier-free access for users around the world, and collects at least 95% of user feedback through the interface for subsequent decision support.

[0049] S9. Security and privacy protection: The security and privacy protection module implements AES 256-bit encryption on all patient data to ensure the security of data during transmission and storage, in compliance with international data protection standards.

[0050] S10. Continuous optimization loop: Based on user feedback and newly collected data, the system performs a cycle from data analysis to recommendation optimization at least once every 24 hours to achieve continuous decision optimization to adapt to changes in patient conditions and updates to the medical environment.

[0051] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A personalized medical decision support system, characterized in that: include: Real-time data acquisition module, historical data integration module, clinical guideline adaptation module, expert knowledge base, data analysis and processing engine, personalized suggestion generation module, adaptive learning module, user interaction interface, security and privacy protection module. The real-time data acquisition module captures the patient's physiological indicators in real time through advanced monitoring equipment. The historical data integration module integrates the received real-time data with the patient's historical medical records and performs data cleaning and standardization processing to build a comprehensive patient health record. The clinical guideline adaptation module and the expert knowledge base are updated synchronously, and the latest research results and expert experience are integrated into the decision-making process to ensure the scientificity and authority of the suggestions. The data analysis and processing engine uses statistical and machine learning techniques to conduct in-depth analysis of the data. The personalized suggestion generation module generates customized medical suggestions based on the analysis results. The adaptive learning module continuously optimizes the algorithm based on feedback to improve the accuracy of the suggestions. The user interaction interface provides an intuitive operating platform to facilitate medical professionals and patients to access and respond to suggestions. The security and privacy protection module ensures data security and patient privacy.

2. The system according to claim 1, characterized in that The real-time data acquisition module synchronizes data with the device worn by the patient in real time through wireless communication technology.

3. The system according to claim 1, characterized in that The historical data integration module has the functions of data deduplication, error detection and standardization processing.

4. The system according to claim 1, characterized in that The clinical guideline adaptation module can be automatically updated to reflect the latest medical research results.

5. The system according to claim 1, characterized in that The expert knowledge base allows experts to update and maintain knowledge items online.

6. The system according to claim 1, characterized in that The data analysis and processing engine includes feature extraction, model training and verification submodules.

7. The system according to claim 1, characterized in that The personalized suggestion generation module can generate customized suggestions based on the patient's specific situation and preferences.

8. The system according to claim 1, characterized in that The adaptive learning module uses reinforcement learning techniques to improve the accuracy of recommendations.

9. The system according to claim 1, characterized in that The user interaction interface provides multi-language support to meet the needs of different users.

10. A personalized medical decision-making method using the system according to any one of claims 1 to 9, comprising the following steps: S1. Collect the patient's physiological index data through the real-time data acquisition module; S2. Import and process the patient's historical medical records through the historical data integration module; S3. Obtain the latest clinical guideline information through the clinical guideline adaptation module; S4. Retrieve relevant expert experience and suggestions through the expert knowledge base; S5. Perform feature extraction and model training on the collected data through data analysis and processing engines; S6. Generate preliminary medical advice according to the analysis results through the personalized advice generation module; S7. Adjust and optimize recommendations based on medical feedback through adaptive learning modules; S8. Display suggestions and collect user feedback through the user interaction interface; S9. Ensure the secure storage and transmission of patient information through security and privacy protection modules; S10. Repeat steps five to nine based on user feedback and newly collected data to achieve continuous decision optimization.

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