AI auxiliary diagnosis and treatment system and method for plateau acute critical diseases

Abstract

The invention discloses an AI auxiliary diagnosis and treatment system and method for plateau acute critical symptoms, which comprehensively receives and stores patient acute critical symptom data of a plurality of plateau medical institutions in real time by integrating a data field, a pointer field, a cluster sequence module and an AI auxiliary diagnosis and treatment suggestion unit, reduces redundancy, improves data access efficiency, accelerates diagnosis and treatment decision, and improves diagnosis and treatment efficiency. The pointer domain module constructs a data association pointer, deeply mines a logic relation, reveals risk factors, development paths and symptom relations of plateau acute and critical diseases and enhances data availability, and the cluster sequence module performs clustering analysis by applying a deep learning algorithm, accurately identifies plateau disease features, establishes a targeted treatment suggestion library and improves diagnosis and treatment pertinence. The AI auxiliary diagnosis and treatment suggestion unit combines real-time data and a treatment suggestion library, automatically provides emergency treatment, treatment scheme optimization and illness state prediction, relieves the burden of medical staff, improves the decision-making efficiency and accuracy, and achieves personalized precise medical treatment.

Description

Technical Field

[0001] The present invention relates to the field of high-altitude assisted diagnosis and treatment technology, and in particular to an AI-assisted diagnosis and treatment system and method for critical and severe diseases at high altitudes. Background Art

[0002] Due to its unique geographical and climatic conditions, the plateau area has a series of special impacts on human health. In a high-altitude environment, the human body will undergo a series of physiological adaptive changes, such as increased heart rate, increased respiratory rate, and decreased blood oxygen saturation. These physiological changes are normally the body's normal adaptation to the plateau environment, but in some cases, they may aggravate or induce the occurrence of critical illness.

[0003] High-altitude critical and emergency diseases include but are not limited to high-altitude pulmonary edema, high-altitude cerebral edema, acute mountain sickness, acute cardiovascular disease, etc. These diseases usually have an acute onset and progress rapidly, posing a serious threat to the patient's life. Due to the relative shortage of medical resources in plateau areas and inconvenient transportation, it is difficult for patients to obtain professional medical treatment in a timely manner, which further increases the mortality and disability rates of high-altitude critical and emergency diseases.

[0004] Traditional diagnosis and treatment of plateau emergencies and critical illnesses mainly rely on the clinical experience and professional knowledge of medical staff. However, the complexity and diversity of diseases in plateau environments pose many challenges to traditional diagnosis and treatment methods. On the one hand, the experience and knowledge of medical staff may be difficult to cover all plateau emergencies and critical illnesses; on the other hand, medical facilities and equipment in plateau areas are relatively backward, which limits the accuracy and timeliness of diagnosis and treatment. Summary of the invention

[0005] In view of this, the present invention proposes an AI-assisted diagnosis and treatment system and method for high-altitude critical diseases, which can effectively solve the defects of the existing technology that it is difficult to cover all high-altitude critical diseases and limit the accuracy and timeliness of diagnosis and treatment.

[0006] The technical solution of the present invention is achieved in this way:

[0007] An AI-assisted diagnosis and treatment system for critical and severe diseases in plateaus, including:

[0008] The data domain module is used to receive and store the critical and severe disease data of patients from medical institutions in plateau areas;

[0009] The pointer domain module is used to construct the associated pointers between data, analyze the data in the data domain through algorithms, and establish the logical relationship between different types of data;

[0010] The cluster sequence module is used to perform cluster analysis on the data processed by the pointer domain module based on the deep learning algorithm, identify the typical characteristics and patterns of critical and severe diseases in the plateau, and form a treatment recommendation library for specific diseases in the plateau environment;

[0011] The AI-assisted diagnosis and treatment recommendation unit is used to use the disease-specific treatment recommendation library generated by the cluster sequence module, combined with real-time input patient data, to automatically provide emergency treatment suggestions, treatment plan optimization suggestions and disease development trend predictions to assist medical staff in making diagnosis and treatment decisions.

[0012] As a further optional solution of the AI-assisted diagnosis and treatment system for plateau critical illness, the data domain module receives and stores critical illness data of patients from medical institutions in plateau areas, specifically including:

[0013] Configure data interfaces with medical institutions in plateau areas to receive real-time data on critical and severe illnesses of patients;

[0014] Preprocess the data based on the statistical threshold screening method;

[0015] Design the database structure according to the characteristics and requirements of the data;

[0016] The preprocessed data is stored in the database according to the designed database structure.

[0017] As a further optional solution for the AI-assisted diagnosis and treatment system for plateau critical illnesses, the pointer domain module constructs associated pointers between data, analyzes the data in the data domain through algorithms, and establishes logical relationships between various types of data, specifically including:

[0018] Extract the data needed to construct the associated pointer from the data domain module;

[0019] Define entities in the data as nodes in the graph neural network and assign a unique identifier to each node;

[0020] Define the edges between nodes based on the logical relationship between data;

[0021] Extracting a feature vector for each node, wherein the feature vector includes the attributes of the node and the relationship between the node and other nodes;

[0022] In the graph neural network, each node aggregates the feature information of neighboring nodes to form a fusion of the local structural information of the node;

[0023] Based on the aggregated feature information, the logical relationship between different types of data is established through algorithm analysis.

[0024] As a further optional solution of the AI-assisted diagnosis and treatment system for plateau acute and critical illnesses, the cluster sequence module performs cluster analysis on the data processed by the pointer domain module based on a deep learning algorithm, identifies typical characteristics and patterns of plateau acute and critical illnesses, and forms a treatment recommendation library for specific diseases in plateau environments, specifically including:

[0025] Receive data processed by the graph neural network from the pointer domain module;

[0026] The clustering loss function is designed based on the clustering tightness, inter-class separation and the particularity of high-altitude critical illness;

[0027] Use the training data set to train the deep learning model and optimize the parameters of the deep learning model by minimizing the clustering loss function;

[0028] Perform cluster analysis on the data processed by the pointer domain module based on the deep learning model with optimized parameters to obtain the clustering results of the data;

[0029] Analyze the clustering results and identify the typical characteristics and patterns of plateau critical illness;

[0030] Based on the identified typical characteristics and patterns of high-altitude critical illnesses, combined with medical knowledge and expert experience, specific disease treatment recommendations for high-altitude environments are generated.

[0031] As a further optional solution for the AI-assisted diagnosis and treatment system for plateau critical illness, the clustering loss function is specifically:

[0032] L=λ1·L intra +λ2·L inter +λ3·L special ;

[0033] Among them, L represents the clustering loss function, L intra represents the intra-class compactness loss, L inter represents the inter-class separation loss, L special A loss item that represents the specificity of high altitude critical illness.

[0034] As a further optional solution of the AI-assisted diagnosis and treatment system for plateau critical illnesses, the AI-assisted diagnosis and treatment suggestion unit uses the treatment suggestion library generated by the cluster sequence module and combines the real-time input patient data to automatically provide emergency treatment suggestions, treatment plan optimization suggestions and disease development trend prediction, specifically including:

[0035] Collect patients’ vital signs, symptom descriptions, and medical history information in real time;

[0036] Match the collected patient data with the treatment recommendation library generated by the cluster sequence module to find the most similar cases or disease patterns;

[0037] According to the matching results, corresponding emergency treatment suggestions and treatment plan optimization suggestions are extracted from the treatment suggestion library;

[0038] Using historical data and expert experience to train deep learning models, which are used to identify disease patterns, predict disease progression, and evaluate treatment effectiveness;

[0039] Real-time data is input into the deep learning model and combined with the preliminary suggestions in the treatment recommendation library to generate emergency treatment suggestions, treatment plan optimization suggestions and the patient's disease development trend.

[0040] As a further optional solution of the AI-assisted diagnosis and treatment system for plateau critical illness, the system also includes:

[0041] The feedback optimization mechanism is used to dynamically adjust the parameters and models of the data domain module, pointer domain module and cluster sequence module according to the actual clinical diagnosis and treatment results and expert feedback.

[0042] An AI-assisted diagnosis and treatment method for critical and severe diseases at high altitudes, specifically including:

[0043] Data receiving and storage steps: receiving and storing critical and severe disease data of patients from medical institutions in plateau areas;

[0044] Data association construction steps: construct association pointers between data, analyze the data in the data domain through algorithms, and establish logical relationships between different types of data;

[0045] Cluster analysis and pattern recognition steps: Perform cluster analysis on the data after data association processing based on deep learning algorithms to identify the typical characteristics and patterns of plateau critical illnesses and form a treatment recommendation library for specific diseases in plateau environments;

[0046] Steps for generating AI-assisted diagnosis and treatment recommendations: The AI-assisted diagnosis and treatment recommendation unit uses the generated disease-specific treatment recommendation library, combined with real-time input patient data, to automatically provide emergency treatment recommendations, treatment plan optimization recommendations, and disease development trend predictions through algorithm matching, optimization, and prediction.

[0047] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned AI-assisted diagnosis and treatment method for critical and severe diseases at high altitude when executing the computer program.

[0048] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned AI-assisted diagnosis and treatment method for critical and severe diseases at high altitudes.

[0049] The beneficial effects of the present invention are as follows: the data domain module can receive and store the critical and severe disease data of patients from multiple medical institutions in the plateau area, ensuring the comprehensiveness and real-time nature of the data. Through centralized storage and management, the system can reduce data redundancy and improve data access efficiency, thereby accelerating the diagnosis and treatment decision-making process. The pointer domain module can deeply explore the logical relationship between data by constructing association pointers between data. This association analysis helps to reveal the potential risk factors of critical and severe diseases in the plateau, the disease development path, and the inherent connection between different symptoms. It can also improve the availability of data and provide more accurate data support for subsequent cluster analysis and diagnosis and treatment recommendations. The module performs cluster analysis on the processed data based on deep learning algorithms, and can accurately identify the typical characteristics and patterns of critical and severe diseases in plateaus. This recognition capability helps to establish a specific disease treatment recommendation library for plateau environments, and improve the pertinence and effectiveness of diagnosis and treatment. The AI-assisted diagnosis and treatment recommendation unit can use the specific disease treatment recommendation library, combined with real-time input patient data, to automatically provide emergency treatment suggestions, treatment plan optimization suggestions and disease development trend predictions, which greatly reduces the burden on medical staff and improves the efficiency and accuracy of diagnosis and treatment decisions. It can also provide personalized diagnosis and treatment suggestions based on individual differences of patients and the development of their disease, which helps to achieve precision medicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 This is a schematic diagram of the composition of an AI-assisted diagnosis and treatment system for plateau critical illnesses according to the present invention;

[0052] Figure 2 This is a flow chart of an AI-assisted diagnosis and treatment method for plateau critical illnesses according to the present invention;

[0053] Figure 3 The figure is a schematic diagram of the composition of a computing device according to the present invention. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0055] refer to Figures 1 to 3 , an AI-assisted diagnosis and treatment system for critical and severe diseases in plateaus, including:

[0056] The data domain module is used to receive and store the critical and severe disease data of patients from medical institutions in plateau areas;

[0057] The pointer domain module is used to construct the associated pointers between data, analyze the data in the data domain through algorithms, and establish the logical relationship between different types of data;

[0058] The cluster sequence module is used to perform cluster analysis on the data processed by the pointer domain module based on the deep learning algorithm, identify the typical characteristics and patterns of critical and severe diseases in the plateau, and form a treatment recommendation library for specific diseases in the plateau environment;

[0059] The AI-assisted diagnosis and treatment recommendation unit is used to use the specific disease treatment recommendation library generated by the cluster sequence module, combined with real-time input patient data, to automatically provide emergency treatment suggestions, treatment plan optimization suggestions and disease development trend predictions to assist medical staff in making diagnosis and treatment decisions.

[0060] In this embodiment, the data domain module can receive and store the critical and severe disease data of patients from multiple medical institutions in the plateau area, ensuring the comprehensiveness and real-time nature of the data. Through centralized storage and management, the system can reduce data redundancy and improve data access efficiency, thereby accelerating the diagnosis and treatment decision-making process. The pointer domain module can deeply explore the logical relationship between data by constructing association pointers between data. This association analysis helps to reveal the potential risk factors of critical and severe diseases in the plateau, the disease development path, and the intrinsic connection between different symptoms. It can also improve the availability of data and provide more accurate data support for subsequent cluster analysis and diagnosis and treatment recommendations. The cluster sequence module Cluster analysis of the processed data based on deep learning algorithms can accurately identify the typical characteristics and patterns of critical and severe diseases in plateaus. This recognition capability helps to establish a specific disease treatment recommendation library for plateau environments, and improve the pertinence and effectiveness of diagnosis and treatment. The AI-assisted diagnosis and treatment recommendation unit can use the specific disease treatment recommendation library, combined with real-time input patient data, to automatically provide emergency treatment recommendations, treatment plan optimization recommendations and disease development trend predictions. This greatly reduces the burden on medical staff and improves the efficiency and accuracy of diagnosis and treatment decisions. It can also provide personalized diagnosis and treatment recommendations based on individual differences and disease progression of patients, which helps to achieve precision medicine.

[0061] Preferably, the data domain module receives and stores the critical and severe disease data of patients from medical institutions in plateau areas, specifically including:

[0062] Configure data interfaces with medical institutions in plateau areas to receive real-time data on critical and severe illnesses of patients;

[0063] Preprocess the data based on the statistical threshold screening method;

[0064] Design the database structure according to the characteristics and requirements of the data;

[0065] The preprocessed data is stored in the database according to the designed database structure.

[0066] In this embodiment, by configuring the data interface with the medical institutions in the plateau area, the data domain module can receive the patient's critical and severe disease data in real time, which ensures the timeliness of the data and enables medical staff to obtain the latest patient information in a timely manner, thereby making faster and more accurate diagnosis and treatment decisions. The data interface is usually based on a standardized data transmission protocol, which can ensure the accuracy and completeness of the data, thereby avoiding misdiagnosis or missed diagnosis due to data transmission errors or omissions; the threshold screening method based on statistics can quickly pre-process the received data. This method can automatically screen out abnormal values ​​or invalid data by setting reasonable thresholds, thereby improving the efficiency of data processing. The threshold screening method can also ensure the quality of the data and reduce data noise by removing invalid or abnormal data. Improve the accuracy of subsequent data analysis and modeling; design the database structure according to the characteristics and requirements of the data to ensure the rationality and efficiency of the database, which includes selecting the appropriate database type, designing a reasonable table structure and index, etc., to improve the efficiency of data query and storage. The database design also needs to consider future scalability and flexibility. With the continuous increase in the amount of data in medical institutions in plateau areas and the changing needs of diagnosis and treatment, the database structure needs to be able to be easily adjusted and expanded to adapt to new data characteristics and requirements; when the data domain module stores the pre-processed data in the database according to the designed database structure, it is necessary to take necessary security measures to protect the security of the data, including using encryption technology to encrypt and store data, setting access permissions to control data access and use, etc.

[0067] It should be noted that the data interface configuration: the data domain module first configures the data interface with the medical institutions in the plateau area to ensure that it can receive the patient's critical and severe data in real time and stably. These data include but are not limited to vital sign parameters (such as heart rate, blood pressure, blood oxygen saturation, etc.), imaging data (such as X-rays, CT scans, etc.), blood test results (such as blood routine, biochemical indicators, etc.) and medical history information;

[0068] Data format definition: To ensure the accuracy and consistency of data, the data domain module needs to define a unified data format and standard, including data types, units, encoding rules, etc.;

[0069] Data cleaning: The received raw data may contain errors, missing values ​​or outliers. The data domain module removes noise and outliers based on statistical threshold screening.

[0070] Data standardization: converting data from different sources and units into a unified standard form to facilitate subsequent data analysis and processing;

[0071] Database design: Design a reasonable database structure based on the characteristics and requirements of the data, including table structure, index, relationship, etc.;

[0072] Data storage: The pre-processed data is stored in the database according to the designed database structure. To ensure the integrity and consistency of the data, a transaction processing mechanism needs to be adopted to ensure the security and reliability of the data during the storage process.

[0073] Preferably, the pointer domain module constructs the associated pointers between the data, analyzes the data in the data domain through an algorithm, and establishes the logical relationship between the various types of data, specifically including:

[0074] Extract the data needed to construct the associated pointer from the data domain module;

[0075] Define entities in the data as nodes in the graph neural network and assign a unique identifier to each node;

[0076] Define the edges between nodes based on the logical relationship between data;

[0077] Extracting a feature vector for each node, wherein the feature vector includes the attributes of the node and the relationship between the node and other nodes;

[0078] In the graph neural network, each node aggregates the feature information of neighboring nodes to form a fusion of the local structural information of the node;

[0079] Based on the aggregated feature information, the logical relationship between different types of data is established through algorithm analysis.

[0080] In this embodiment, by defining nodes and edges to construct an association network of data, the potential connections between data can be deeply explored. This enhanced association helps to reveal the hidden patterns and laws in plateau critical and severe disease data; by aggregating the feature information of neighboring nodes in the graph neural network, a fusion of the local structural information of the nodes is formed. This fusion enables each node to obtain comprehensive information about its surrounding environment, thereby improving the accuracy and completeness of data representation; feature vectors including attribute information and relationship information are extracted for each node. These feature vectors can fully reflect the characteristics of the node and its association with other nodes, providing an effective basis for establishing logical relationships; based on the aggregated feature information, the logical relationship between various types of data can be accurately established through algorithm analysis, and this accuracy ensures the accuracy and reliability of subsequent AI-assisted diagnosis and treatment recommendations; as a powerful data representation and learning tool, the graph neural network can handle complex data structures and relationships. The scheme uses the graph neural network to analyze data in the data domain, showing good adaptability and flexibility, and can cope with data sets of different types and sizes; the scheme can handle multiple types of data, including but not limited to vital sign parameters, symptom descriptions, examination results, etc., and by constructing association pointers and feature vectors, it can achieve comprehensive analysis and utilization of these data.

[0081] It should be noted that data extraction: extracting data that need to construct associated pointers from the data domain module, including but not limited to the patient's basic information, vital signs parameters, imaging data, blood test results and medical history information;

[0082] Data cleaning: Clean the extracted data to remove duplicate, erroneous or irrelevant data to ensure data accuracy and consistency;

[0083] Data conversion: convert the cleaned data into a format that can be processed by graph neural networks, such as node feature vectors and edge relationship matrices;

[0084] Node definition: define entities in the data (such as patients, symptoms, diseases, etc.) as nodes in the graph neural network and assign a unique identifier to each node;

[0085] Edge definition: Define the edges between nodes based on the logical relationship between data. For example, the relationship between patients and symptoms can be defined as an edge, indicating that the patient has a certain symptom. The relationship between symptoms and diseases can also be defined as an edge, indicating that a certain symptom may be a sign of a certain disease.

[0086] Feature extraction: Extract feature vectors for each node. These feature vectors include the attributes of the node (such as the patient's age, gender, medical history, etc.) and the relationship between the node and other nodes (such as the similarity between the patient and other patients, the correlation between symptoms and diseases, etc.);

[0087] Information aggregation: Each node aggregates the feature information of neighboring nodes to form a fusion of the node’s local structural information. This is achieved through the aggregation step of the graph neural network, which allows the node to capture the characteristics of its surrounding environment.

[0088] Relationship establishment: Based on the aggregated feature information, logical relationships between various types of data are established through algorithmic analysis. These relationships can be explicit (such as a direct association between patients and diseases) or implicit (such as inferring possible diseases of patients through symptoms).

[0089] Preferably, the cluster sequence module performs cluster analysis on the data processed by the pointer domain module based on a deep learning algorithm, identifies typical characteristics and patterns of critical and severe diseases in plateaus, and forms a treatment recommendation library for specific diseases in plateau environments, specifically including:

[0090] Receive data processed by the graph neural network from the pointer domain module;

[0091] The clustering loss function is designed based on the clustering tightness, inter-class separation and the particularity of high-altitude critical illness;

[0092] Use the training data set to train the deep learning model and optimize the parameters of the deep learning model by minimizing the clustering loss function;

[0093] Perform cluster analysis on the data processed by the pointer domain module based on the deep learning model with optimized parameters to obtain the clustering results of the data;

[0094] Analyze the clustering results and identify the typical characteristics and patterns of plateau critical illness;

[0095] Based on the identified typical characteristics and patterns of high-altitude critical illnesses, combined with medical knowledge and expert experience, specific disease treatment recommendations for high-altitude environments are generated.

[0096] Preferably, the clustering loss function is specifically:

[0097] L=λ1·L intra +λ2·L inter +λ3·L special ;

[0098] Among them, L represents the clustering loss function, L intra represents the intra-class compactness loss, L inter represents the inter-class separation loss, L special A loss item that represents the specificity of high altitude critical illness.

[0099] In this embodiment, the cluster sequence module designs a special clustering loss function based on the clustering tightness, inter-class separation and particularity of high-altitude critical illness data. This design can ensure the accuracy and reliability of the clustering results and avoid clustering errors caused by uneven data distribution or blurred category boundaries. By using the training data set to train the deep learning model and minimizing the clustering loss function to optimize the model parameters, the cluster sequence module can learn the inherent laws and characteristics in the data. This training and optimization process improves the model's clustering analysis ability for high-altitude critical illness data, making the clustering results more accurate and stable. The cluster sequence module uses the deep learning model after optimizing the parameters to analyze the data processed by the pointer domain module. Cluster analysis is performed on the data to obtain the clustering results of the data. Through in-depth analysis of the clustering results, the typical characteristics and patterns of high-altitude critical and severe diseases can be identified. On the basis of identifying the typical characteristics and patterns of high-altitude critical and severe diseases, the cluster sequence module combines medical knowledge and expert experience to generate treatment recommendations for specific diseases in high-altitude environments. This combination ensures the scientificity and practicality of the treatment recommendations, and can provide strong support for medical staff. Based on the identified typical characteristics and patterns of high-altitude critical and severe diseases, the cluster sequence module can generate a library of treatment recommendations for specific diseases in high-altitude environments. These treatment recommendations cover the main types and common situations of high-altitude critical and severe diseases, and can provide medical staff with comprehensive and systematic treatment references.

[0100] It should be noted that by considering the clustering tightness of the data in the loss function, the algorithm can be prompted to pay more attention to the intrinsic connection between data points during the clustering process. This helps to form more compact and cohesive clusters and reduce the impact of noise points and outliers on the clustering results. The inter-class separation component in the loss function can ensure that the differences between different clusters are maximized, which helps to avoid overlap and confusion in the clustering results and improve the clarity and interpretability of the clustering. In view of the particularity of high-altitude critical and emergency data (such as uneven data distribution, blurred category boundaries, etc.), the designed clustering loss function can better adapt to these characteristics, which helps the algorithm maintain stable performance in the complex and changeable high-altitude medical environment. By optimizing the clustering loss function, the algorithm can more accurately Identifying the typical characteristics and patterns of high-altitude critical illnesses can help improve the accuracy and reliability of disease diagnosis and provide medical staff with more accurate treatment recommendations. Considering the complexity and diversity of high-altitude critical illness data, the designed clustering loss function can enhance the robustness of the model, which enables the model to maintain stable clustering performance when facing unknown or newly emerging data. By minimizing the clustering loss function, the algorithm can continuously optimize its internal parameters and structure to adapt to data changes and updates, which helps to improve the generalization ability of the model and enable it to better handle new situations and new problems that may arise in the future.

[0101] Preferably, the AI-assisted diagnosis and treatment suggestion unit uses the treatment suggestion library generated by the cluster sequence module, combined with the real-time input patient data, to automatically provide emergency treatment suggestions, treatment plan optimization suggestions and disease development trend prediction, specifically including:

[0102] Collect patients’ vital signs, symptom descriptions, and medical history information in real time;

[0103] Match the collected patient data with the treatment recommendation library generated by the cluster sequence module to find the most similar cases or disease patterns;

[0104] According to the matching results, corresponding emergency treatment suggestions and treatment plan optimization suggestions are extracted from the treatment suggestion library;

[0105] Using historical data and expert experience to train deep learning models, which are used to identify disease patterns, predict disease progression, and evaluate treatment effectiveness;

[0106] Real-time data is input into the deep learning model and combined with the preliminary suggestions in the treatment recommendation library to generate emergency treatment suggestions, treatment plan optimization suggestions and the patient's disease development trend.

[0107] In this embodiment, by collecting the patient's vital signs, symptom descriptions, and medical history information in real time, the AI-assisted diagnosis and treatment recommendation unit can quickly obtain a comprehensive view of the patient's current condition, which helps doctors obtain patient information in a timely manner and make decisions quickly. Especially in emergency situations, it can significantly shorten the response time and match the collected patient data with the treatment recommendation library generated by the cluster sequence module to find the most similar cases or disease patterns. Based on these matching results, AI can extract emergency treatment recommendations and treatment plan optimization recommendations that best match the current patient's condition from the treatment recommendation library. This precise matching and personalized recommendations can significantly improve the accuracy and pertinence of diagnosis and treatment, using deep learning models trained with historical data and expert experience. , can identify disease patterns, predict disease development and evaluate treatment effects. The adaptive learning ability of the deep learning model enables it to continuously learn from new data and optimize itself, so as to better adapt to changes in the disease. By inputting real-time data into the deep learning model and combining it with the preliminary suggestions in the treatment suggestion library, AI can generate a prediction of the patient's disease development trend, which helps doctors understand the possible changes in the patient's condition in advance and thus formulate a more forward-looking treatment plan. The emergency treatment suggestions, treatment plan optimization suggestions and disease development trend predictions provided by the AI-assisted diagnosis and treatment suggestion unit can provide doctors with strong decision-making support, which helps doctors to more reasonably allocate medical resources, such as beds, operation time and drugs, thereby improving the utilization efficiency of medical resources.

[0108] Preferably, the system further comprises:

[0109] The feedback optimization mechanism is used to dynamically adjust the parameters and models of the data domain module, pointer domain module and cluster sequence module according to the actual clinical diagnosis and treatment results and expert feedback.

[0110] In this embodiment, the feedback optimization mechanism can receive actual clinical diagnosis and treatment results and expert feedback in real time, and dynamically adjust the parameters and models of each module based on this information. This real-time feedback and adjustment can ensure that the system is always synchronized with the latest clinical data and expert knowledge, thereby improving the accuracy and reliability of the system; by continuously optimizing the parameters and models of each module, the system can more accurately identify and analyze patient data and reduce misdiagnosis and missed diagnosis; through the feedback optimization mechanism, the system can continuously optimize its internal algorithms and models, thereby improving processing speed, which helps the system to analyze and process patient data more quickly and provide doctors with more timely auxiliary diagnosis and treatment suggestions.

[0111] An AI-assisted diagnosis and treatment method for critical and severe diseases at high altitudes, specifically including:

[0112] Data receiving and storage steps: receiving and storing critical and severe disease data of patients from medical institutions in plateau areas;

[0113] Data association construction steps: construct association pointers between data, analyze the data in the data domain through algorithms, and establish logical relationships between different types of data;

[0114] Cluster analysis and pattern recognition steps: Perform cluster analysis on the data after data association processing based on deep learning algorithms to identify the typical characteristics and patterns of plateau critical illnesses and form a treatment recommendation library for specific diseases in plateau environments;

[0115] Steps for generating AI-assisted diagnosis and treatment recommendations: The AI-assisted diagnosis and treatment recommendation unit uses the generated disease-specific treatment recommendation library, combined with real-time input patient data, to automatically provide emergency treatment recommendations, treatment plan optimization recommendations, and disease development trend predictions through algorithm matching, optimization, and prediction.

[0116] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned AI-assisted diagnosis and treatment method for critical and severe diseases at high altitude when executing the computer program.

[0117] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned AI-assisted diagnosis and treatment method for critical and severe diseases at high altitudes.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, 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. An AI-assisted diagnosis and treatment system for critical and severe diseases at high altitudes, characterized by: include: The data domain module is used to receive and store the critical and severe disease data of patients from medical institutions in plateau areas; The pointer domain module is used to construct the associated pointers between data, analyze the data in the data domain through algorithms, and establish the logical relationship between different types of data; The cluster sequence module is used to perform cluster analysis on the data processed by the pointer domain module based on the deep learning algorithm, identify the typical characteristics and patterns of critical and severe diseases in the plateau, and form a treatment recommendation library for specific diseases in the plateau environment; The AI-assisted diagnosis and treatment recommendation unit is used to use the specific disease treatment recommendation library generated by the cluster sequence module, combined with real-time input patient data, to automatically provide emergency treatment suggestions, treatment plan optimization suggestions and disease development trend predictions to assist medical staff in making diagnosis and treatment decisions.

2. According to claim 1, an AI-assisted diagnosis and treatment system for critical and severe diseases at high altitudes is characterized in that: The data domain module receives and stores critical and severe disease data of patients from medical institutions in plateau areas, specifically including: Configure data interfaces with medical institutions in plateau areas to receive real-time data on critical and severe illnesses of patients; Preprocess the data based on the statistical threshold screening method; Design the database structure according to the characteristics and requirements of the data; The preprocessed data is stored in the database according to the designed database structure.

3. The AI-assisted diagnosis and treatment system for plateau critical illness according to claim 2 is characterized in that: The pointer domain module constructs the associated pointers between data, analyzes the data in the data domain through algorithms, and establishes the logical relationship between various types of data, specifically including: Extract the data needed to construct the associated pointer from the data domain module; Define entities in the data as nodes in the graph neural network and assign a unique identifier to each node; Define the edges between nodes based on the logical relationship between data; Extracting a feature vector for each node, wherein the feature vector includes the attributes of the node and the relationship between the node and other nodes; In the graph neural network, each node aggregates the feature information of neighboring nodes to form a fusion of the local structural information of the node; Based on the aggregated feature information, the logical relationship between different types of data is established through algorithm analysis.

4. The AI-assisted diagnosis and treatment system for plateau critical illness according to claim 3 is characterized in that: The cluster sequence module performs cluster analysis on the data processed by the pointer domain module based on the deep learning algorithm, identifies the typical characteristics and patterns of critical and severe diseases in the plateau, and forms a treatment recommendation library for specific diseases in the plateau environment, which specifically includes: Receive data processed by the graph neural network from the pointer domain module; The clustering loss function is designed based on the clustering tightness, inter-class separation and the particularity of high-altitude critical illness; Use the training data set to train the deep learning model and optimize the parameters of the deep learning model by minimizing the clustering loss function; Perform cluster analysis on the data processed by the pointer domain module based on the deep learning model with optimized parameters to obtain the clustering results of the data; Analyze the clustering results and identify the typical characteristics and patterns of plateau critical illness; Based on the identified typical characteristics and patterns of high-altitude critical illnesses, combined with medical knowledge and expert experience, specific disease treatment recommendations for high-altitude environments are generated.

5. The AI-assisted diagnosis and treatment system for plateau critical illness according to claim 4 is characterized in that: The clustering loss function is specifically: L=λ1·Lintra+λ2·Linter+λ3·Lspecial; Among them, L represents the clustering loss function, L intra represents the intra-class compactness loss, L inter represents the inter-class separation loss, L special A loss item that represents the specificity of high altitude critical illness.

6. The AI-assisted diagnosis and treatment system for plateau critical illness according to claim 5 is characterized in that: The AI-assisted diagnosis and treatment suggestion unit uses the treatment suggestion library generated by the cluster sequence module and combines the real-time input patient data to automatically provide emergency treatment suggestions, treatment plan optimization suggestions and disease development trend prediction, including: Collect patients’ vital signs, symptom descriptions, and medical history information in real time; Match the collected patient data with the treatment recommendation library generated by the cluster sequence module to find the most similar cases or disease patterns; According to the matching results, corresponding emergency treatment suggestions and treatment plan optimization suggestions are extracted from the treatment suggestion library; Using historical data and expert experience to train deep learning models, which are used to identify disease patterns, predict disease progression, and evaluate treatment effectiveness; Real-time data is input into the deep learning model and combined with the preliminary suggestions in the treatment recommendation library to generate emergency treatment suggestions, treatment plan optimization suggestions and the patient's disease development trend.

7. The AI-assisted diagnosis and treatment system for plateau critical illness according to claim 6 is characterized in that: The system further comprises: The feedback optimization mechanism is used to dynamically adjust the parameters and models of the data domain module, pointer domain module and cluster sequence module according to the actual clinical diagnosis and treatment results and expert feedback.

8. An AI-assisted diagnosis and treatment method for critical and severe diseases at high altitudes, characterized in that: Specifically include: Data receiving and storage steps: receiving and storing critical and severe disease data of patients from medical institutions in plateau areas; Data association construction steps: construct association pointers between data, analyze the data in the data domain through algorithms, and establish logical relationships between different types of data; Cluster analysis and pattern recognition steps: Perform cluster analysis on the data after data association processing based on deep learning algorithms to identify the typical characteristics and patterns of plateau critical illnesses and form a treatment recommendation library for specific diseases in plateau environments; Steps for generating AI-assisted diagnosis and treatment recommendations: The AI-assisted diagnosis and treatment recommendation unit uses the generated disease-specific treatment recommendation library, combined with real-time input patient data, to automatically provide emergency treatment recommendations, treatment plan optimization recommendations, and disease development trend predictions through algorithm matching, optimization, and prediction.

9. A computing device, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the AI-assisted diagnosis and treatment method for high-altitude critical illnesses as described in claim 8 are implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by the processor, implements the steps of the AI-assisted diagnosis and treatment method for high-altitude critical illnesses as described in claim 8.

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