Intelligent medical handover system based on key nodes in diagnosis and treatment process

Abstract

The invention discloses an intelligent medical handover system based on key nodes in a diagnosis and treatment process. The system comprises a data acquisition module; the key node identification module is used for analyzing the collected diagnosis and treatment data and identifying key nodes in the diagnosis and treatment process; the weighted calculation module is used for carrying out weighted calculation on the importance of the key nodes; the intelligent recommendation module is used for intelligently recommending the diagnosis and treatment action or the handover list of the next step according to the weighted calculation result; the user interaction module provides an interface for interaction between medical staff and the system; a data storage module; and a safety protection module. According to the invention, by introducing functional modules of data acquisition, key node identification, weighted calculation, intelligent recommendation and the like, automatic identification and management of key nodes in the diagnosis and treatment process are realized. Meanwhile, in combination with a user interaction module, a data storage module, a safety protection module and other modules, the intelligent level and safety of the system are improved, and a more efficient and accurate decision support tool is provided for medical staff.

Description

Technical Field

[0001] The present invention belongs to the field of data processing, and more specifically, particularly relates to an intelligent medical handover system based on key nodes in the diagnosis and treatment process. Background Art

[0002] With the development of modern medical technology, the diagnosis and treatment process has become more and more complex and diverse. Traditional medical handover systems usually rely on paper records or simple electronic records, which are not only inefficient but also prone to errors, making it difficult to meet the needs of efficient and accurate medical care. In particular, in the identification and management of key nodes, traditional methods often lack intelligent support, resulting in medical staff not being able to respond quickly enough when dealing with emergencies, affecting the treatment effect and safety of patients.

[0003] Some existing smart medical systems have begun to introduce electronic medical records and basic data processing functions, but these systems still have the following shortcomings:

[0004] Insufficient identification of key nodes: Most existing systems lack the ability to automatically identify key nodes in the diagnosis and treatment process. For example, for key decision points such as diagnosis confirmation, surgery arrangement, and treatment plan adjustment, medical staff need to manually judge and record them, which not only increases the workload but also easily leads to errors due to human factors.

[0005] Lack of intelligent recommendation function: When providing diagnosis and treatment suggestions, the existing system often simply makes recommendations based on preset rules, failing to fully consider individual differences in patients and actual clinical conditions. This recommendation method has low accuracy and practicality, making it difficult to provide effective decision-making support for medical staff;

[0006] Data quality and consistency issues: Due to the diversity of data sources, the inconsistent data formats and standards between different medical information systems make data integration difficult, affecting the quality and consistency of data. In addition, there may be noise and redundant information in the original data, which requires cleaning and preprocessing.

[0007] Insufficient security and privacy protection: Medical data involves patient privacy, and its security is of vital importance. Existing systems often lack adequate encryption and access control measures during data transmission and storage, posing the risk of data leakage and unauthorized access.

[0008] Slow response speed: Under high concurrent access conditions, the performance and response speed of existing systems often cannot meet real-time requirements, affecting the work efficiency and diagnosis and treatment effects of medical staff;

[0009] In response to the above, we propose an intelligent medical handover system based on key nodes in the diagnosis and treatment process to effectively solve the above problems. Summary of the invention

[0010] The purpose of the present invention is to solve the shortcomings of the prior art, and to propose a smart medical handover system based on key nodes in the diagnosis and treatment process, which realizes the automatic identification and management of key nodes in the diagnosis and treatment process by introducing functional modules such as data collection, key node identification, weighted calculation, and intelligent recommendation. At the same time, combined with user interaction, data storage, security protection and other modules, the intelligence level and security of the system are improved, providing medical staff with a more efficient and accurate decision support tool.

[0011] To achieve the above object, the present invention provides the following technical solutions:

[0012] A smart medical handover system based on key nodes in the diagnosis and treatment process, including:

[0013] The data collection module is used to collect the patient's diagnosis and treatment data from the medical information system, including medical history information, examination results, treatment plans and doctor's advice information;

[0014] The key node identification module is used to analyze the collected diagnosis and treatment data and identify the key nodes in the diagnosis and treatment process, including diagnosis confirmation, surgery arrangement, and treatment plan adjustment;

[0015] A weighted calculation module is used to perform weighted calculation on the importance of key nodes, wherein each key node is assigned a weight value according to its importance in the diagnosis and treatment process;

[0016] Intelligent recommendation module, based on weighted calculation results, intelligently recommends the next step of diagnosis and treatment actions or handover items to optimize the diagnosis and treatment process and improve medical efficiency;

[0017] The user interaction module provides an interface for medical staff to interact with the system, displays recommended diagnosis and treatment actions and handover items, and allows medical staff to adjust and confirm according to actual conditions;

[0018] Data storage module, used to store patients' diagnosis and treatment data, key node information, weighted calculation results and recommended actions to ensure data security and integrity;

[0019] The security protection module ensures that data exchange and storage in the system comply with medical information security standards, including encrypted transmission, access control and audit tracking.

[0020] Preferably, the data acquisition module includes:

[0021] The data source interface layer is responsible for connecting with various medical information systems in the hospital and obtaining patients' diagnosis and treatment data through standardized data exchange protocols;

[0022] The data preprocessing layer performs preprocessing operations such as cleaning, deduplication, and format conversion on the collected raw data to ensure the quality and consistency of the data;

[0023] The data cache layer is used to improve the system response speed and store the recently frequently used diagnosis and treatment data in memory to reduce the access pressure on the back-end database;

[0024] The specific workflow of the data acquisition module is as follows:

[0025] 1) Connect with various medical information systems in the hospital through the data source interface layer to establish a data exchange channel;

[0026] 2) Extract the patient's diagnosis and treatment data from the medical information system according to the preset data extraction rules, including medical records, examination results, treatment plans and doctor's advice information;

[0027] 3) Perform pre-processing operations such as cleaning, deduplication, and format conversion on the extracted raw data to ensure data quality and consistency;

[0028] 4) Store the preprocessed data in the data cache layer for quick access by subsequent modules;

[0029] 5) Regularly write the data in the cache into batches into the data storage module to ensure persistent storage of the data.

[0030] Preferably, the key node identification module includes:

[0031] The data input layer receives the diagnosis and treatment data from the data acquisition module, including medical records, examination results, treatment plans, and doctor's orders;

[0032] The data processing layer further processes and analyzes the input diagnosis and treatment data to extract the features and indicators related to key nodes;

[0033] The key node identification algorithm layer is used to identify the key nodes from the processed data according to the preset key node identification criteria;

[0034] Output layer: Outputs the identified key nodes in a structured form for use by the weighted calculation module and the intelligent recommendation module.

[0035] Preferably, when the key node identification algorithm layer performs diagnosis confirmation, if the examination result X is within the abnormal value range [a, b] and the doctor has a clear diagnosis conclusion, the diagnosis is confirmed as a key node, and the expression is:

[0036] (a≤X≤b)∧(DeqNone)⇒diagnosis confirmed as a key node;

[0037] In the formula, [a, b] represents the range of abnormal values, where a is the lower limit of the abnormal value range, b is the upper limit of the abnormal value range, D represents the doctor's diagnosis conclusion, if the doctor has a clear diagnosis conclusion, then DeqNone, otherwise D=None, ∧ represents and, ⇒ represents then;

[0038] When arranging surgery, the key node identification algorithm layer predicts possible future surgery time points based on the patient's disease progression and historical surgery records. If the predicted surgery time point is close to the current date, the surgery arrangement is considered to be a key node. The expression is:

[0039] |P−C|<Y Z ⇒ Surgery is scheduled as a key node;

[0040] P represents the predicted surgery time point, C represents the current date, |P−C| represents the absolute value of the difference between the predicted surgery time point and the current date, and YZ represents the threshold;

[0041] Y Z The specific standards are:

[0042] For emergency surgery, the threshold is set at 24 hours; for semi-urgent surgery, the threshold is set at 72 hours; for planned surgery, the threshold is set at 7 days;

[0043] When adjusting the treatment plan, the key node identification algorithm layer determines whether the treatment plan needs to be adjusted based on the patient's current treatment plan, treatment effect and side effect factors. If adjustment is required, the treatment plan adjustment is considered to be a key node, and the expression is:

[0044] C(A, E, S) = Y⇒ Treatment plan adjusted to key nodes;

[0045] Where A represents the current treatment plan, E represents the treatment effect, S represents the side effect, and C(A, E, S) represents the classification algorithm, which determines whether the treatment plan needs to be adjusted based on the current treatment plan, treatment effect, and side effects. Y represents the need for adjustment.

[0046] Preferably, the weighted calculation module includes:

[0047] The data input layer receives the key node information from the key node identification module, including the type of key nodes and their related characteristics and indicators;

[0048] The weight assignment layer assigns an initial weight value to each key node according to the preset weight assignment rules or algorithms;

[0049] The weight adjustment layer dynamically adjusts the initial weight values ​​according to the characteristics and indicators of the key nodes to more accurately reflect the importance of the key nodes;

[0050] Output layer: outputs the weighted key nodes and their weight values ​​to the intelligent recommendation module for subsequent diagnosis and treatment action recommendations;

[0051] The specific process of weighted calculation of the weighted calculation module is as follows:

[0052] 1) Assigning an initial weight value to each key node, wherein the initial weight value is pre-set based on experience or expert opinion;

[0053] 2) Extracting features and indicators related to importance from the information of key nodes, including severity of disease, urgency of treatment, and patient age;

[0054] 3) Dynamically adjust the initial weight values ​​based on the extracted features and indicators, including appropriately increasing the weight values ​​of key nodes for patients with higher disease severity and appropriately increasing the weight values ​​of key nodes for patients with higher treatment urgency;

[0055] 4) Normalize the weight values ​​of all key nodes so that their value range is between [0, 1];

[0056] 5) Output the weighted key nodes and their weight values ​​to the intelligent recommendation module.

[0057] Preferably, the weighted calculation module has n key nodes in the weighted calculation, and the initial weight value of each key node is w i (i=1, 2, ..., n), the extracted features and indicators are x ij (j=1, 2, ..., m), the weighted calculation formula is expressed as:

[0058] ;

[0059] In the formula, a j is the weight coefficient of the jth feature or indicator, which is used to control the contribution of the feature or indicator to the weighted result. j The value of can adjust the impact of different characteristics or indicators on the weighted results; Represents the updated weight value of the i-th key node, which is based on the initial weight w of the key node i And the characteristics or indicators x related to the key node ij The weighted calculation result of It represents the sum of the weighted sums of all features or indicators.

[0060] Preferably, the intelligent recommendation module includes:

[0061] The data input layer receives the key nodes and their weight value information from the weighted calculation module;

[0062] The recommendation algorithm layer generates a recommended list of the next step of diagnosis and treatment actions or handover items based on the weight values ​​of key nodes and preset recommendation standards;

[0063] The output layer outputs the recommended diagnosis and treatment actions or handover items in a structured form to medical staff for their reference and execution.

[0064] Preferably, the specific workflow of the user interaction module is:

[0065] 1) After the medical staff logs into the system, they enter the home page;

[0066] 2) Click the entrance of the corresponding module as needed to enter the corresponding function interface;

[0067] 3) In the key node identification interface, view the identified key nodes and their related information, and perform necessary editing, deletion or addition operations;

[0068] 4) In the weighted calculation interface, check the weight value of each key node and its calculation basis, and make manual adjustments as needed. At the same time, you can confirm or reject the weighted calculation results;

[0069] 5) In the smart recommendation interface, check the recommended next step of diagnosis and treatment actions or handover items, and select, modify or add new suggestions according to actual needs;

[0070] 6) If you encounter any problems during use, you can enter the help and support interface to seek help or contact technical support services;

[0071] 7) After completing all operations, log out of the system and close the browser window.

[0072] Preferably, the specific workflow of the security protection module is:

[0073] 1) When a user logs into the system, identity authentication and authorization are required;

[0074] 2) When a user performs any operation in the system, the system will record its operation log;

[0075] 3) Administrators can query and analyze operation logs to discover abnormal behaviors and take corresponding measures;

[0076] 4) The system will regularly back up data and restore data when necessary;

[0077] 5) The system will regularly detect and repair security vulnerabilities to ensure the security of the system;

[0078] 6) Medical staff can receive safety training and learning through the interface provided by the system;

[0079] 7) The system will issue security announcements and warning messages to remind medical staff of the importance of network security and data protection.

[0080] Technical effects and advantages of the present invention: The present invention provides a smart medical handover management system based on key nodes in the diagnosis and treatment process. Compared with the prior art, the present invention has the following advantages:

[0081] Improve diagnosis and treatment efficiency: Through the intelligent recommendation module, the system can automatically recommend the next step of diagnosis and treatment actions or handover items based on key nodes and their weight values, which helps medical staff quickly obtain decision-making support information and reduce the time for manual search and analysis of data, thereby improving the efficiency of the diagnosis and treatment process;

[0082] Optimize resource allocation: The weighted calculation module can accurately evaluate the importance of each key node, so that medical resources can be more reasonably allocated to the most needed links. For patients with higher severity of illness or more urgent treatment, the weight value of their key nodes will be appropriately increased to ensure that these patients can receive timely attention and treatment;

[0083] Improve patient safety: Through accurate key node identification and intelligent recommendation, the system can help medical staff to promptly identify and deal with potential risk points, thereby avoiding harm to patients caused by misdiagnosis and delayed surgery, and improving the safety level of patients;

[0084] Enhance data consistency and quality: The data preprocessing layer in the data acquisition module cleans, removes duplications, and converts the format of the original data, ensuring the quality and consistency of the data and providing a reliable data foundation for subsequent analysis and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 This is the architecture diagram of the smart medical handover management system based on key nodes in the diagnosis and treatment process of the present invention;

[0086] Figure 2 The figure is a specific flow chart of weighted calculation of the weighted calculation module in the embodiment of the present invention. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific 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. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0088] The present invention provides a smart medical handover management system based on key nodes in the diagnosis and treatment process. The value of this system in the medical field is mainly reflected in improving diagnosis and treatment efficiency, optimizing resource allocation, and improving patient safety, which has brought significant improvements to modern medical services.

[0089] like Figure 1-2 The system architecture specifically includes:

[0090] The data collection module is used to collect the patient's diagnosis and treatment data from the medical information system, including medical records, examination results, treatment plans and doctor's advice information; the data collection module includes:

[0091] The data source interface layer is responsible for connecting with various medical information systems in the hospital and obtaining patients' diagnosis and treatment data through standardized data exchange protocols;

[0092] The data preprocessing layer performs preprocessing operations such as cleaning, deduplication, and format conversion on the collected raw data to ensure the quality and consistency of the data;

[0093] The data cache layer is used to improve the system response speed and store the recently frequently used diagnosis and treatment data in memory to reduce the access pressure on the back-end database;

[0094] The specific workflow of the data acquisition module is as follows:

[0095] 1) Connect with various medical information systems in the hospital through the data source interface layer to establish a data exchange channel;

[0096] 2) Extract the patient's diagnosis and treatment data from the medical information system according to the preset data extraction rules, including medical records, examination results, treatment plans and doctor's advice information;

[0097] 3) Perform pre-processing operations such as cleaning, deduplication, and format conversion on the extracted raw data to ensure data quality and consistency;

[0098] 4) Store the preprocessed data in the data cache layer for quick access by subsequent modules;

[0099] 5) Regularly write the data in the cache into batches into the data storage module to ensure persistent storage of the data.

[0100] The key node identification module is used to analyze the collected diagnosis and treatment data and identify the key nodes in the diagnosis and treatment process, including diagnosis confirmation, surgery arrangement, and treatment plan adjustment; the key node identification module includes:

[0101] The data input layer receives the diagnosis and treatment data from the data acquisition module, including medical records, examination results, treatment plans, and doctor's orders;

[0102] The data processing layer further processes and analyzes the input diagnosis and treatment data to extract the features and indicators related to key nodes;

[0103] The key node identification algorithm layer is used to identify the key nodes from the processed data according to the preset key node identification criteria;

[0104] Output layer: Outputs the identified key nodes in a structured form for use by the weighted calculation module and the intelligent recommendation module;

[0105] Furthermore, when the key node identification algorithm layer is confirming the diagnosis, if the examination result X is within the abnormal value range [a, b] and the doctor has a clear diagnosis conclusion, the diagnosis is confirmed as a key node, and the expression is:

[0106] (a≤X≤b)∧(DeqNone)⇒diagnosis confirmed as a key node;

[0107] In the formula, [a, b] represents the range of abnormal values, where a is the lower limit of the abnormal value range, b is the upper limit of the abnormal value range, D represents the doctor's diagnosis conclusion, if the doctor has a clear diagnosis conclusion, then DeqNone, otherwise D=None, ∧ represents and, ⇒ represents then;

[0108] When scheduling surgery, the key node identification algorithm predicts possible future surgery times based on the patient's condition progression and historical surgery records. If the predicted surgery time is close to the current date, the surgery is considered a key node. The expression is:

[0109] |P−C|<Y Z ⇒ Surgery is scheduled as a key node;

[0110] P represents the predicted surgery time point, C represents the current date, |P−C| represents the absolute value of the difference between the predicted surgery time point and the current date, and YZ represents the threshold;

[0111] Y Z The specific standards are:

[0112] For emergency surgery, the threshold is set at 24 hours; for semi-urgent surgery, the threshold is set at 72 hours; for planned surgery, the threshold is set at 7 days;

[0113] When adjusting the treatment plan, the key node identification algorithm layer determines whether the treatment plan needs to be adjusted based on the patient's current treatment plan, treatment effect, and side effects. If adjustment is required, the treatment plan adjustment is considered a key node, and the expression is:

[0114] C(A, E, S) = Y⇒ Treatment plan adjusted to key nodes;

[0115] Where A represents the current treatment plan, E represents the treatment effect, S represents the side effect, and C(A, E, S) represents the classification algorithm, which determines whether the treatment plan needs to be adjusted based on the current treatment plan, treatment effect, and side effects. Y represents the need for adjustment.

[0116] Exemplary examples of diagnosis and confirmation of key node identification are as follows:

[0117] Patient information: Name: Zhang; Age: 45 years old; Gender: male;

[0118] Test results: X = 100 (in mg / dL);

[0119] Outlier range: a=90, b=110;

[0120] Doctor's diagnosis: D = hypertension (clear diagnosis);

[0121] According to the expression (a≤X≤b)∧(DeqNone)⇒diagnosis confirms that it is a key node and makes the following judgment:

[0122] 90≤100≤11090≤100≤110 holds true, that is, the inspection result X is within the range of abnormal values;

[0123] DeqNone, which means that the doctor has a clear diagnosis conclusion;

[0124] Therefore, diagnosis is confirmed as a critical node.

[0125] Identification of key nodes for surgical arrangement:

[0126] Patient information: Name: Li; Age: 30 years old; Gender: Female;

[0127] Predicted surgery time point: P = June 1, 2024;

[0128] Current date: C = May 30, 2024;

[0129] Threshold: Y Z =72 hours (semi-emergency surgery);

[0130] According to the expression |P−C|<Y Z ⇒ Surgery arrangement is a key node, and the following judgments are made:

[0131] |June 1, 2024-May 30, 2024|<72 hours |June 1, 2024-May 30, 2024|<72 hours is true, that is, the difference between the predicted surgery time point and the current date is less than 72 hours;

[0132] Therefore, surgical scheduling is a critical node.

[0133] Identification of key nodes for treatment plan adjustment:

[0134] Patient information: Name: Wang; Age: 50 years old; Gender: male;

[0135] Current treatment regimen: A=chemotherapy regimen;

[0136] Treatment effect: E = stable condition;

[0137] Side effects: S = nausea, vomiting;

[0138] Classification algorithm: C(A, E, S) = needs adjustment;

[0139] According to the expression C(A, E, S) = Y⇒treatment plan adjustment is a key node, and the following judgment is made:

[0140] C (chemotherapy regimen, stable condition, nausea and vomiting) = need to adjust, that is, based on the current treatment regimen, treatment effect and side effects, the classification algorithm determines that the treatment plan needs to be adjusted;

[0141] Therefore, the treatment plan was adjusted to key nodes.

[0142] A weighted calculation module is used to perform weighted calculation on the importance of key nodes, wherein each key node is assigned a weight value according to its importance in the diagnosis and treatment process; the weighted calculation module includes:

[0143] The data input layer receives the key node information from the key node identification module, including the type of key nodes and their related characteristics and indicators;

[0144] The weight assignment layer assigns an initial weight value to each key node according to the preset weight assignment rules or algorithms;

[0145] The weight adjustment layer dynamically adjusts the initial weight values ​​according to the characteristics and indicators of the key nodes to more accurately reflect the importance of the key nodes;

[0146] Output layer: outputs the weighted key nodes and their weight values ​​to the intelligent recommendation module for subsequent diagnosis and treatment action recommendations;

[0147] The specific process of weighted calculation in weighted calculation module is as follows:

[0148] 1) Assign an initial weight value to each key node, which is pre-set based on experience or expert opinion;

[0149] 2) Extracting features and indicators related to importance from the information of key nodes, including severity of disease, urgency of treatment, and patient age;

[0150] 3) Dynamically adjust the initial weight values ​​based on the extracted features and indicators, including appropriately increasing the weight values ​​of key nodes for patients with higher disease severity and appropriately increasing the weight values ​​of key nodes for patients with higher treatment urgency;

[0151] 4) Normalize the weight values ​​of all key nodes so that their value range is between [0, 1];

[0152] 5) Output the weighted key nodes and their weight values ​​to the intelligent recommendation module.

[0153] It should be noted that the weighted calculation module has n key nodes in the weighted calculation, and the initial weight value of each key node is w i (i=1, 2, ..., n), the extracted features and indicators are x ij (j=1, 2, ..., m), the weighted calculation formula is expressed as:

[0154] ;

[0155] In the formula, a j is the weight coefficient of the jth feature or indicator, which is used to control the contribution of the feature or indicator to the weighted result. j The value of can adjust the impact of different characteristics or indicators on the weighted results; Represents the updated weight value of the i-th key node, which is based on the initial weight w of the key node i And the characteristics or indicators x related to the key node ij The weighted calculation result of It represents the sum of the weighted sums of all features or indicators.

[0156] The intelligent recommendation module intelligently recommends the next step of diagnosis and treatment actions or handover items based on the weighted calculation results to optimize the diagnosis and treatment process and improve medical efficiency; the intelligent recommendation module includes:

[0157] The data input layer receives the key nodes and their weight value information from the weighted calculation module;

[0158] The recommendation algorithm layer generates a recommended list of the next step of diagnosis and treatment actions or handover items based on the weight values ​​of key nodes and preset recommendation standards;

[0159] The output layer outputs the recommended diagnosis and treatment actions or handover items in a structured form to medical staff for their reference and execution;

[0160] The recommendation algorithm of the recommendation algorithm layer includes at least one of the following:

[0161] Rule-based recommendation algorithm: formulate a series of recommendation rules according to the type and weight value of key nodes;

[0162] For example, for patients whose diagnosis is confirmed as a key node and whose weight value is high, further examination or treatment can be recommended; for patients whose surgery is scheduled as a key node and whose weight value is moderate, preparation for surgery is recommended. By matching these rules, a recommendation list is generated.

[0163] Recommendation algorithm based on machine learning: collect historical diagnosis and treatment data and recommendation results, build a training data set, select a suitable machine learning model, and train it using the training data set, apply the trained model to the current key nodes and their weight values, and predict the next step of diagnosis and treatment actions or handover items. Generate a recommendation list based on the prediction results.

[0164] Hybrid recommendation algorithm: Combines rule-based recommendation algorithm and machine learning-based recommendation algorithm to improve the accuracy and flexibility of recommendation. First, the rule-based recommendation algorithm is applied to generate a preliminary recommendation list; then, the machine learning-based recommendation algorithm is applied to optimize and adjust the preliminary recommendation list;

[0165] Recommendation result sorting algorithm: Sort the diagnosis and treatment actions or handover items in the recommendation list according to the score or confidence of the recommendation algorithm, and give priority to the diagnosis and treatment actions or handover items with higher scores or confidence.

[0166] The user interaction module provides an interface for medical staff to interact with the system, displays recommended diagnosis and treatment actions and handover items, and allows medical staff to adjust and confirm according to actual conditions; the specific workflow of the user interaction module is as follows:

[0167] 1) After the medical staff logs into the system, they enter the home page;

[0168] 2) Click the entrance of the corresponding module as needed to enter the corresponding function interface;

[0169] 3) In the key node identification interface, view the identified key nodes and their related information, and perform necessary editing, deletion or addition operations;

[0170] 4) In the weighted calculation interface, check the weight value of each key node and its calculation basis, and make manual adjustments as needed. At the same time, you can confirm or reject the weighted calculation results;

[0171] 5) In the smart recommendation interface, check the recommended next step of diagnosis and treatment actions or handover items, and select, modify or add new suggestions according to actual needs;

[0172] 6) If you encounter any problems during use, you can enter the help and support interface to seek help or contact technical support services;

[0173] 7) After completing all operations, log out of the system and close the browser window.

[0174] Furthermore, the user interaction module includes:

[0175] Key node identification interface: displays the current patient's diagnosis and treatment data, including medical history information, examination results, treatment plan and doctor's advice information; displays the identified key nodes and related information, such as type and weight value; provides operation buttons for editing, deleting or adding key nodes.

[0176] Weighted calculation interface: displays the weight value of each key node and its calculation basis; provides the function of manually adjusting the weight value; supports medical staff to confirm or reject the weighted calculation results.

[0177] Intelligent recommendation interface: displays the next step of diagnosis and treatment actions or handover items recommended by the intelligent recommendation; provides the function of sorting, filtering or custom query of recommendation results; supports medical staff to select, modify or add new suggestions to the recommendation results.

[0178] Personal Center: Allows medical staff to set personal information and passwords; provides statistics and analysis functions for system usage.

[0179] Help and support: Provide system instructions, FAQs and other help documents; support online customer service or technical support services.

[0180] Data storage module, used to store patients' diagnosis and treatment data, key node information, weighted calculation results and recommended actions to ensure data security and integrity;

[0181] The workflow of the data storage module is:

[0182] Data collection: Extract patient diagnosis and treatment data from different sources through ETL tools;

[0183] Data cleaning: Clean and format the raw data in the data preprocessing layer;

[0184] Data storage: Store the cleaned data into a structured database;

[0185] Data encryption: Encrypt sensitive data;

[0186] Access control: Implement role-based access control policies;

[0187] Data backup: perform data backup tasks regularly;

[0188] Data service: Provide data services to other modules through API interface.

[0189] The security protection module ensures that data exchange and storage in the system comply with medical information security standards, including encrypted transmission, access control, and audit tracking. The specific workflow of the security protection module is as follows:

[0190] 1) When a user logs into the system, identity authentication and authorization are required;

[0191] 2) When a user performs any operation in the system, the system will record its operation log;

[0192] 3) Administrators can query and analyze operation logs to discover abnormal behaviors and take corresponding measures;

[0193] 4) The system will regularly back up data and restore data when necessary;

[0194] 5) The system will regularly detect and repair security vulnerabilities to ensure the security of the system;

[0195] 6) Medical staff can receive safety training and learning through the interface provided by the system;

[0196] 7) The system will issue security announcements and warning messages to remind medical staff of the importance of network security and data protection;

[0197] Furthermore, the security protection module system architecture includes:

[0198] Data encryption transmission unit: using encryption protocols such as SSL / TLS to encrypt the data during transmission in the network to prevent the data from being stolen or tampered with; encrypt sensitive data to ensure the security of data during transmission;

[0199] Access control unit: implements role-based access control, restricting users’ access to system resources according to their roles and permissions; authenticates and authorizes key operations to ensure that only users with corresponding permissions can perform these operations;

[0200] Audit tracking unit: records all operation logs of the system, including user login, data query, and data modification; provides log query and analysis functions so that administrators can promptly detect abnormal behavior and take corresponding measures;

[0201] Data backup and recovery unit: regularly back up the data in the system to prevent data loss or damage; provide data recovery function so that data can be restored in time when data loss or damage occurs;

[0202] Security vulnerability detection and repair unit: Regularly detect security vulnerabilities in the system, identify potential security risks and repair them in a timely manner; track the latest security vulnerabilities and attack methods, and update the system's security policies and protection measures in a timely manner.

[0203] In summary, compared with the prior art, the present invention has the following advantages:

[0204] Improve diagnosis and treatment efficiency: Through the intelligent recommendation module, the system can automatically recommend the next step of diagnosis and treatment actions or handover items based on key nodes and their weight values, which helps medical staff quickly obtain decision-making support information and reduce the time for manual search and analysis of data, thereby improving the efficiency of the diagnosis and treatment process;

[0205] Optimize resource allocation: The weighted calculation module can accurately evaluate the importance of each key node, so that medical resources can be more reasonably allocated to the most needed links. For patients with higher severity of illness or more urgent treatment, the weight value of their key nodes will be appropriately increased to ensure that these patients can receive timely attention and treatment;

[0206] Improve patient safety: Through accurate key node identification and intelligent recommendation, the system can help medical staff to promptly identify and deal with potential risk points, thereby avoiding harm to patients caused by misdiagnosis and delayed surgery, and improving the safety level of patients;

[0207] Enhance data consistency and quality: The data preprocessing layer in the data acquisition module cleans, removes duplications, and converts the format of the original data, ensuring the quality and consistency of the data and providing a reliable data foundation for subsequent analysis and application.

[0208] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. 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 handover system based on key nodes in the diagnosis and treatment process, characterized by: include: The data collection module is used to collect the patient's diagnosis and treatment data from the medical information system, including medical history information, examination results, treatment plans and doctor's advice information; The key node identification module is used to analyze the collected diagnosis and treatment data and identify the key nodes in the diagnosis and treatment process, including diagnosis confirmation, surgery arrangement, and treatment plan adjustment; A weighted calculation module is used to perform weighted calculation on the importance of key nodes, wherein each key node is assigned a weight value according to its importance in the diagnosis and treatment process; Intelligent recommendation module, based on weighted calculation results, intelligently recommends the next step of diagnosis and treatment actions or handover items to optimize the diagnosis and treatment process and improve medical efficiency; The user interaction module provides an interface for medical staff to interact with the system, displays recommended diagnosis and treatment actions and handover items, and allows medical staff to adjust and confirm according to actual conditions; Data storage module, used to store patients' diagnosis and treatment data, key node information, weighted calculation results and recommended actions to ensure data security and integrity; The security protection module ensures that data exchange and storage in the system comply with medical information security standards, including encrypted transmission, access control and audit tracking.

2. According to claim 1, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: The data acquisition module comprises: The data source interface layer is responsible for connecting with various medical information systems in the hospital and obtaining patients' diagnosis and treatment data through standardized data exchange protocols; The data preprocessing layer performs preprocessing operations such as cleaning, deduplication, and format conversion on the collected raw data to ensure the quality and consistency of the data; The data cache layer is used to improve the system response speed and store the recently frequently used diagnosis and treatment data in memory to reduce the access pressure on the back-end database; The specific workflow of the data acquisition module is as follows: 1) Connect with various medical information systems in the hospital through the data source interface layer to establish a data exchange channel; 2) Extract the patient's diagnosis and treatment data from the medical information system according to the preset data extraction rules, including medical records, examination results, treatment plans and doctor's advice information; 3) Perform pre-processing operations such as cleaning, deduplication, and format conversion on the extracted raw data to ensure data quality and consistency; 4) Store the preprocessed data in the data cache layer for quick access by subsequent modules; 5) Regularly write the data in the cache into batches into the data storage module to ensure persistent storage of the data.

3. According to claim 1, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: The key node identification module includes: The data input layer receives the diagnosis and treatment data from the data acquisition module, including medical records, examination results, treatment plans, and doctor's orders; The data processing layer further processes and analyzes the input diagnosis and treatment data to extract the features and indicators related to key nodes; The key node identification algorithm layer is used to identify the key nodes from the processed data according to the preset key node identification criteria; Output layer: Outputs the identified key nodes in a structured form for use by the weighted calculation module and the intelligent recommendation module.

4. According to claim 3, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: When the key node identification algorithm layer is confirming the diagnosis, if the examination result X is within the abnormal value range [a, b] and the doctor has a clear diagnosis conclusion, the diagnosis is confirmed as a key node, and the expression is: (a≤X≤b)∧(DeqNone)⇒diagnosis confirmed as a key node; In the formula, [a, b] represents the range of abnormal values, where a is the lower limit of the abnormal value range, b is the upper limit of the abnormal value range, D represents the doctor's diagnosis conclusion, if the doctor has a clear diagnosis conclusion, then DeqNone, otherwise D=None, ∧ represents and, ⇒ represents then; When arranging surgery, the key node identification algorithm layer predicts possible future surgery time points based on the patient's disease progression and historical surgery records. If the predicted surgery time point is close to the current date, the surgery arrangement is considered to be a key node. The expression is: |P−C|<Y Z ⇒ Surgery is scheduled as a key node; P represents the predicted surgery time point, C represents the current date, |P−C| represents the absolute value of the difference between the predicted surgery time point and the current date, and YZ represents the threshold; Y Z The specific standards are: For emergency surgery, the threshold is set at 24 hours; for semi-urgent surgery, the threshold is set at 72 hours; for planned surgery, the threshold is set at 7 days; When adjusting the treatment plan, the key node identification algorithm layer determines whether the treatment plan needs to be adjusted based on the patient's current treatment plan, treatment effect and side effect factors. If adjustment is required, the treatment plan adjustment is considered to be a key node, and the expression is: C(A, E, S) = Y⇒ Treatment plan adjusted to key nodes; Where A represents the current treatment plan, E represents the treatment effect, S represents the side effect, and C(A, E, S) represents the classification algorithm, which determines whether the treatment plan needs to be adjusted based on the current treatment plan, treatment effect, and side effects. Y represents the need for adjustment.

5. According to claim 1, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: The weighted calculation module comprises: The data input layer receives the key node information from the key node identification module, including the type of key nodes and their related characteristics and indicators; The weight assignment layer assigns an initial weight value to each key node according to the preset weight assignment rules or algorithms; The weight adjustment layer dynamically adjusts the initial weight values ​​according to the characteristics and indicators of the key nodes to more accurately reflect the importance of the key nodes; Output layer: outputs the weighted key nodes and their weight values ​​to the intelligent recommendation module for subsequent diagnosis and treatment action recommendations; The specific process of weighted calculation of the weighted calculation module is as follows: 1) Assigning an initial weight value to each key node, wherein the initial weight value is pre-set based on experience or expert opinion; 2) Extracting features and indicators related to importance from the information of key nodes, including severity of disease, urgency of treatment, and patient age; 3) Dynamically adjust the initial weight values ​​based on the extracted features and indicators, including appropriately increasing the weight values ​​of key nodes for patients with higher disease severity and appropriately increasing the weight values ​​of key nodes for patients with higher treatment urgency; 4) Normalize the weight values ​​of all key nodes so that their value range is between [0, 1]; 5) Output the weighted key nodes and their weight values ​​to the intelligent recommendation module.

6. According to claim 5, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: The weighted calculation module has n key nodes in the weighted calculation, and the initial weight value of each key node is w i (i=1, 2, ..., n), the extracted features and indicators are x ij (j=1, 2, ..., m), the weighted calculation formula is expressed as: ； In the formula, a j is the weight coefficient of the jth feature or indicator, which is used to control the contribution of the feature or indicator to the weighted result. j The value of can adjust the impact of different characteristics or indicators on the weighted results; Represents the updated weight value of the i-th key node, which is based on the initial weight w of the key node i And the characteristics or indicators x related to the key node ij The weighted calculation result of It represents the sum of the weighted sums of all features or indicators.

7. According to claim 1, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: The intelligent recommendation module includes: The data input layer receives the key nodes and their weight value information from the weighted calculation module; The recommendation algorithm layer generates a recommended list of the next step of diagnosis and treatment actions or handover items based on the weight values ​​of key nodes and preset recommendation standards; The output layer outputs the recommended diagnosis and treatment actions or handover items in a structured form to medical staff for their reference and execution.

8. According to claim 1, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: The specific workflow of the user interaction module is as follows: 1) After the medical staff logs into the system, they enter the home page; 2) Click the entrance of the corresponding module as needed to enter the corresponding function interface; 3) In the key node identification interface, view the identified key nodes and their related information, and perform necessary editing, deletion or addition operations; 4) In the weighted calculation interface, check the weight value of each key node and its calculation basis, and make manual adjustments as needed. At the same time, you can confirm or reject the weighted calculation results; 5) In the smart recommendation interface, check the recommended next step of diagnosis and treatment actions or handover items, and select, modify or add new suggestions according to actual needs; 6) If you encounter any problems during use, you can enter the help and support interface to seek help or contact technical support services; 7) After completing all operations, log out of the system and close the browser window.

9. According to claim 1, a smart medical handover system based on key nodes in the diagnosis and treatment process is characterized in that: The specific workflow of the security protection module is as follows: 1) When a user logs into the system, identity authentication and authorization are required; 2) When a user performs any operation in the system, the system will record its operation log; 3) Administrators can query and analyze operation logs to discover abnormal behaviors and take corresponding measures; 4) The system will regularly back up data and restore data when necessary; 5) The system will regularly detect and repair security vulnerabilities to ensure the security of the system; 6) Medical staff can receive safety training and learning through the interface provided by the system; 7) The system will issue security announcements and warning messages to remind medical staff of the importance of network security and data protection.

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