A knowledge mining method and system for the field of trauma care medicine

Abstract

The present invention relates to the technical field of healthcare data mining, and particularly to a knowledge mining method and system for the field of trauma care medicine. The method includes the following steps: obtaining original trauma medical data; performing data preprocessing on the original trauma medical data to obtain trauma medical data; performing standardization processing on the trauma medical data to obtain standard trauma medical data; obtaining trauma patient care monitoring data; and performing monitoring type classification on the trauma patient care monitoring data to obtain care monitoring type data. The present invention uses data processing technology, pattern recognition technology, and deep learning technology to achieve the fusion of trauma care medical data for trauma medical text information, image information, and care monitoring information, and to achieve the division of relevant knowledge levels for the fused trauma care medical data, thereby enabling a more comprehensive presentation of the knowledge system in the field of trauma care medicine.

Description

Technical Field

[0001] The present invention relates to the technical field of healthcare data mining, and particularly to a knowledge mining method and system for the field of trauma care medicine. Background Art

[0002] Initial knowledge mining mainly relied on traditional statistical methods and expert systems to extract useful knowledge by analyzing historical data and expert experience. With the application of big data technology in the field of trauma care medicine, researchers began to use massive medical data for knowledge mining, specifically using big data technology to process complex and multi-dimensional trauma care medicine data sets. The current trauma care knowledge mining methods highly rely on data, especially large-scale and high-quality medical data sets, which include patient medical record information, physiological monitoring data, imaging materials, etc. By analyzing these data, representative feature information in the field of trauma care medicine is extracted. However, existing knowledge mining technologies are difficult to perform trauma care medicine data fusion on trauma medical text information, image information, and nursing monitoring information, and it is difficult to classify the associated knowledge levels of the fused trauma care medicine data, resulting in a low presentation degree of the knowledge system in the field of trauma care medicine. Summary of the Invention

[0003] Based on this, it is necessary to provide a knowledge mining method and system for the field of trauma care medicine to solve at least one of the above technical problems.

[0004] To achieve the above object, a knowledge mining method for the field of trauma care medicine, the method includes the following steps:

[0005] Step S1: Obtain original trauma medical data; perform data preprocessing on the original trauma medical data to obtain trauma medical data; perform standardization processing on the trauma medical data to obtain standard trauma medical data;

[0006] Step S2: Obtain trauma patient nursing monitoring data; perform monitoring type classification on the trauma patient nursing monitoring data to obtain nursing monitoring type data; perform data fusion on the standard trauma medical data and the nursing monitoring type data to generate trauma medicine - nursing monitoring data;

[0007] Step S3: Determine trauma type characteristics for the trauma medicine - nursing monitoring data to generate trauma type characteristic data; evaluate the degree of trauma recovery for the trauma medicine - nursing monitoring data to generate trauma recovery degree data; perform trauma care relevance analysis on the trauma medicine - nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma care association data; perform trauma care knowledge level classification on the trauma care association data to generate trauma care knowledge level data;

[0008] Step S4: Hierarchically cluster the trauma care knowledge level data to obtain hierarchically clustered data; construct a trauma care knowledge system from the hierarchically clustered data to generate a trauma care knowledge system.

[0009] By obtaining the original trauma medical data and performing data preprocessing and standardization, the present invention can ensure the accuracy and consistency of trauma medical data. This lays a solid foundation for subsequent data analysis and applications, improving the usability and reliability of the data. By obtaining the trauma patient care monitoring data and classifying the monitoring types, the care monitoring data can be effectively classified and organized. Fusing the standard trauma medical data with the care monitoring type data to generate trauma medical-care monitoring data realizes the organic combination of medical data and care monitoring data. Determining the trauma type characteristics and evaluating the trauma recovery degree of the trauma medical-care monitoring data can accurately identify the trauma type and evaluate the trauma recovery situation. Based on the trauma type characteristic data and the trauma recovery degree data, performing a trauma care relevance analysis on the trauma medical-care monitoring data reveals the internal connection between trauma care and patient recovery. Performing a trauma care knowledge level division on the trauma care correlation data to generate trauma care knowledge level data provides a basis for constructing a systematic trauma care knowledge system. Hierarchically clustering the trauma care knowledge level data can classify and integrate similar care knowledge to form a clearer and more systematic knowledge structure. Constructing a trauma care knowledge system from the hierarchically clustered data to generate a trauma care knowledge system provides systematic and structured knowledge support for the practice and research of trauma care, improving the quality and efficiency of trauma care. Therefore, the present invention uses data processing technology, pattern recognition technology, and deep learning technology to achieve the fusion of trauma care medical data for trauma medical text information, image information, and care monitoring information, and to achieve the division of relevant knowledge levels for the fused trauma care medical data, thereby making the knowledge system in the field of trauma care medicine more comprehensively presented.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the original trauma medical data, where the original trauma medical data includes trauma patient imaging examination data, trauma patient biochemical test data, and trauma patient electronic medical record data;

[0012] Step S12: Perform image enhancement on the trauma patient imaging examination data to obtain enhanced trauma imaging data; perform data calibration on the trauma patient biochemical test data to obtain calibrated biochemical test data; perform text cleaning on the trauma patient electronic medical record data to obtain standardized electronic medical record data;

[0013] Step S13: Integrate the trauma image enhancement data, biochemical test calibration data, and electronic medical record specification data to obtain trauma medical data;

[0014] Step S14: Standardize the corresponding formats of the trauma medical data to generate medically formatted standard data; perform standardized coding on the trauma medical data to obtain standard trauma medical data.

[0015] The present invention comprehensively obtains the original medical data of trauma patients, including imaging examination data, biochemical test data, and electronic medical record data. The comprehensive acquisition of these data provides a rich information basis for subsequent analysis and processing, ensuring the integrity and diversity of the data. By performing image enhancement processing on the imaging examination data of trauma patients, the clarity and readability of the images can be improved, making the detailed features in the images more obvious. Data calibration of the biochemical test data of trauma patients can eliminate measurement errors and ensure the accuracy and reliability of the biochemical test data. Text cleaning of the electronic medical record data of trauma patients can remove irrelevant information and incorrect data to obtain standardized electronic medical record data. Integrating the trauma image enhancement data, biochemical test calibration data, and electronic medical record specification data enables the organic combination of data from different sources and types, forming a comprehensive data set. Performing format standardization and coding standardization processing on the trauma medical data generates medically formatted standard data and standard trauma medical data. Format standardization ensures the compatibility and consistency of the data between different systems and platforms, facilitating data sharing and exchange. Coding standardization gives the data a unified identifier and specification, facilitating data management and analysis and improving the operability and traceability of the data.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Monitor the wound healing status of the patient; obtain the patient's medication usage; record the activity of the patient's traumatized limb;

[0018] Step S22: Identify the wound healing characteristics of the patient's wound healing status to generate wound healing characteristic types; determine the wound medication situation of the patient's medication usage to generate wound medication situation types; classify the activity of the patient's traumatized limb to generate traumatized limb activity types;

[0019] Step S23: Classify the nursing monitoring data of trauma patients according to the wound healing characteristic types, wound medication situation types, and traumatized limb activity types to obtain nursing monitoring type data;

[0020] Step S24: Integrate the standard trauma medical data with the nursing monitoring type data to generate trauma medical-nursing monitoring data.

[0021] By monitoring the wound healing status of patients, obtaining the drug usage of patients, and recording the activities of the traumatized limbs of patients, the present invention can comprehensively understand various key indicators and behavioral characteristics of patients during the trauma recovery process, ensuring a comprehensive grasp of the patient's recovery status; identifying the characteristics of the patient's wound healing status, determining the medication situation for the drug usage, and classifying the types of activities of the traumatized limbs can effectively simplify and classify the monitored complex information, facilitating targeted analysis and evaluation of different aspects during the patient's recovery process; classifying the nursing monitoring data of trauma patients according to the wound healing characteristic type, wound medication situation type, and traumatized limb activity type can classify the nursing monitoring data more meticulously and systematically. The obtained nursing monitoring type data can better understand the performance and needs of patients in different nursing monitoring dimensions; fusing the standard trauma medical data with the nursing monitoring type data organically combines the medical data and the nursing monitoring data, forming a more comprehensive and integrated data set. This provides richer information support for analyzing the recovery process of trauma patients.

[0022] Preferably, step S24 includes the following steps:

[0023] Step S241: Identify the wound appearance characteristics of the wound healing characteristic type to obtain wound appearance characteristic data; judge the wound healing process based on the wound appearance characteristic data to generate wound healing process data;

[0024] Step S242: Correlate the trauma image enhancement data with the wound appearance characteristic data and the wound healing process data to obtain image-healing corresponding data;

[0025] Step S243: Determine the types of wound medications for the wound medication situation type to obtain wound medication type data; record the wound disinfection frequency based on the wound medication type data to obtain the wound disinfection frequency; extract the medication dosage for the drug usage of trauma patients according to the wound disinfection frequency to generate the wound medication dosage;

[0026] Step S244: Match and fuse the biochemical detection calibration data with the wound medication type data, the wound disinfection frequency, and the wound medication dosage to obtain detection-medication fusion data;

[0027] Step S245: Mark the limb joints of the traumatized limb activity type to generate traumatized limb joint data; determine the range of joint activities based on the traumatized limb joint data to obtain the range of joint activities data;

[0028] Step S246: Extract the medical order requirements for the activities of the traumatized limb from the standardized electronic medical record data to obtain the medical order data for the activities of the traumatized limb; correspond the medical order data for the activities of the traumatized limb with the joint range of motion data to obtain the medical record-limb activity data;

[0029] Step S247: Integrate the image-healing correspondence data, the test-medication fusion data, and the medical record-limb activity data to generate the trauma medicine-nursing monitoring data.

[0030] The present invention identifies the appearance features of the wound healing characteristic types, and can accurately obtain the appearance feature data of the wound. Further, it judges the healing process based on the wound appearance feature data to generate the wound healing process data. This provides quantitative and qualitative bases for evaluating the healing status of the wound, making the monitoring of the wound healing more accurate and systematic; corresponding the enhanced trauma image data with the wound appearance feature data and the wound healing process data makes the image data closely combined with the wound healing status information, providing more intuitive and accurate data support for monitoring the wound healing by imaging means and better understanding the dynamic changes of the wound healing; determining the types of medications used for the wound and recording the disinfection frequency for the wound medication type data; extracting the medication dosage based on the wound disinfection frequency for the medication usage situation, making the management of the wound medication situation more refined and providing detailed data records and bases for rational medication; matching and integrating the biochemical test calibration data with the wound medication type data, the wound disinfection frequency, and the wound medication dosage makes the biochemical test results closely combined with the wound medication situation, providing more comprehensive and integrated data support for evaluating the drug usage effect and monitoring the changes in the patient's biochemical indicators and better analyzing the impact of the drug on the patient's biochemical indicators; marking the limb joints for the types of traumatized limb activities to generate the traumatized limb joint data, and determining the range of joint motion for the traumatized limb joint data, providing accurate joint motion information for monitoring the activities of the traumatized limb, making the evaluation of the range of limb motion more accurate and quantitative and clarifying the recovery status of the patient's limb function; extracting the medical order requirements for the activities of the traumatized limb from the standardized electronic medical record data to obtain the medical order data for the activities of the traumatized limb, and corresponding the medical order data for the activities of the traumatized limb with the joint range of motion data to obtain the medical record-limb activity data. This corresponding relationship makes the medical order information in the medical record closely combined with the actual limb activity situation, providing data support for evaluating whether the patient performs limb activities according to the medical order; integrating the image-healing correspondence data, the test-medication fusion data, and the medical record-limb activity data makes the data from different dimensions and sources organically combined to form a comprehensive data set.

[0031] Preferably, step S3 includes the following steps:

[0032] Step S31: Identify trauma characteristics from trauma medicine - nursing monitoring data to obtain trauma characteristic data; extract manifestation characteristics from the trauma characteristic data to generate trauma manifestation characteristic data;

[0033] Step S32: Divide the trauma manifestation characteristic data to obtain trauma division data; pair trauma type characteristics based on the trauma division data to generate trauma type characteristic data;

[0034] Step S33: Evaluate the degree of trauma recovery from the trauma medicine - nursing monitoring data to generate trauma recovery degree data;

[0035] Step S34: Analyze the correlation between trauma medicine - nursing monitoring data and trauma care based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma care correlation data;

[0036] Step S35: Divide the trauma care correlation data into levels of trauma care knowledge to generate trauma care knowledge level data.

[0037] The present invention identifies trauma characteristics from trauma medicine - nursing monitoring data, which can accurately extract trauma characteristic data. Further, by extracting manifestation characteristics from the trauma characteristic data, trauma manifestation characteristic data is generated. This provides detailed basic data for in - depth analysis of trauma characteristics, making the understanding of trauma characteristics more comprehensive and in - depth; dividing the trauma manifestation characteristic data to obtain trauma division data, and pairing trauma type characteristics based on the trauma division data to generate trauma type characteristic data. This process makes the classification of trauma characteristics clearer and more systematic, better identifying and distinguishing different types of trauma; evaluating the degree of trauma recovery from the trauma medicine - nursing monitoring data to generate trauma recovery degree data. This kind of evaluation can quantify the recovery situation of trauma, providing objective data support for monitoring the trauma recovery process, making the monitoring of trauma recovery more accurate and quantifiable, and better understanding the patient's recovery status; analyzing the correlation between trauma medicine - nursing monitoring data and trauma care based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma care correlation data. This kind of analysis can reveal the internal connection between trauma characteristics and nursing measures, providing a scientific basis for optimizing the nursing plan, improving the effectiveness and pertinence of nursing, and ensuring that nursing measures can better meet the patient's recovery needs; dividing the trauma care correlation data into levels of trauma care knowledge makes the organization of trauma care knowledge more systematic and hierarchical, providing a basis for constructing a comprehensive trauma care knowledge system and better managing and applying trauma care knowledge.

[0038] Preferably, step S33 includes the following steps:

[0039] Step S331: Determine the tissue structure of the wound healing area for the image-healing corresponding data to obtain the tissue structure of the healing area; identify the integrity of tissue healing for the tissue structure of the healing area to generate a structural healing integrity index;

[0040] Step S332: Determine the medication cycle for the detection-medication fusion data to obtain the wound medication cycle data; map the promoting effect of the wound healing drug on the tissue structure of the healing area according to the wound medication cycle data to generate a drug promoting effect index;

[0041] Step S333: Judge the movable range of the limb for the medical record-limb activity data to obtain the movable range data of the limb; determine the degree of limb function recovery for the movable range of the limb to generate a limb function recovery index;

[0042] Step S334: Calculate the wound recovery degree index based on the structural healing integrity index, the drug promoting effect index and the limb function recovery index to obtain the wound recovery degree data.

[0043] The present invention determines the tissue structure of the wound healing area for the image-healing corresponding data, and can accurately identify the tissue structure characteristics of the healing area. Further, identify the integrity of tissue healing for the tissue structure of the healing area to generate a structural healing integrity index. This provides a quantitative index for evaluating the tissue integrity of wound healing, making the monitoring of the tissue structure recovery of the healing area more accurate and objective; determine the medication cycle for the detection-medication fusion data to obtain the wound medication cycle data. Map the promoting effect of the wound healing drug on the tissue structure of the healing area according to the wound medication cycle data to generate a drug promoting effect index. This process can quantify the promoting effect of the drug on wound healing, provide specific data support for evaluating the role of the drug in the wound recovery process, and better understand the impact of the drug on the healing process. Judge the movable range of the limb for the medical record-limb activity data, and further determine the degree of limb function recovery for the movable range of the limb to generate a limb function recovery index. This provides a quantitative index for evaluating the functional recovery of the wounded limb, making the monitoring of the limb movement ability and functional recovery more accurate and comprehensive, and better understanding the recovery status of the patient's limb function. Calculate the wound recovery degree index based on the structural healing integrity index, the drug promoting effect index and the limb function recovery index to obtain the wound recovery degree data. The comprehensive calculation makes the evaluation of the wound recovery degree more comprehensive and comprehensive, and can comprehensively consider the impacts of tissue healing, drug action and limb function recovery, etc.

[0044] Preferably, step S34 includes the following steps:

[0045] Step S341: Based on the trauma type characteristic data, perform healing characteristic nursing mode recognition on the image-healing correspondence data to obtain the healing characteristic nursing mode;

[0046] Step S342: According to the healing characteristic nursing mode, perform drug usage association on the detection-medication fusion data to obtain the drug usage association data;

[0047] Step S343: Based on the trauma recovery degree data, extract the contribution degree of the trauma nursing mode to the medical record-limb movement data to obtain the nursing mode contribution degree;

[0048] Step S344: Sort the contribution degrees of the nursing modes to generate a recovery contribution degree sorting value; according to the recovery contribution degree sorting value, perform nursing effect association on the trauma medicine-nursing monitoring data to obtain the trauma nursing effect data;

[0049] Step S345: Combine the healing characteristic nursing mode, the drug usage association data, and the trauma nursing effect data through trauma nursing association to obtain the trauma nursing association data.

[0050] Based on the trauma type characteristic data, the present invention performs healing characteristic nursing mode recognition on the image-healing correspondence data, which can accurately identify the nursing mode matching the trauma healing characteristics, obtain the healing characteristic nursing mode, enable the nursing measures to better adapt to the actual situation of trauma healing, and improve the pertinence and effectiveness of nursing. According to the healing characteristic nursing mode, perform drug usage association on the detection-medication fusion data to obtain the drug usage association data. It can combine the drug usage situation with the nursing mode to better understand the specific application of drugs in trauma nursing. Based on the trauma recovery degree data, extract the contribution degree of the trauma nursing mode to the medical record-limb movement data, which can quantify the contribution degree of different nursing modes to trauma recovery, provide specific data indicators for evaluating the effectiveness of nursing modes, and identify the nursing modes with greater contribution to trauma recovery. Sort the contribution degrees of the nursing modes and perform nursing effect association on the trauma medicine-nursing monitoring data according to the recovery contribution degree sorting value, making the effect evaluation of the nursing mode clearer and more systematic, and enabling an intuitive understanding of the influence degree of different nursing modes on trauma recovery. Combine the healing characteristic nursing mode, the drug usage association data, and the trauma nursing effect data through trauma nursing association to obtain the trauma nursing association data. This combination organically combines nursing information from different dimensions to form a comprehensive nursing association dataset, providing rich data support for comprehensively analyzing all aspects of trauma nursing and helping to better understand and optimize the entire process of trauma nursing.

[0051] Preferably, step S35 includes the following steps:

[0052] Step S351: Identify the healing characteristic stage for the trauma care - related data to obtain the healing characteristic stage; divide the trauma care mode hierarchy for the healing characteristic care mode according to the healing characteristic stage to generate the trauma care mode hierarchy;

[0053] Step S352: Identify the types of nursing drugs for the trauma care - related data to obtain the types of nursing drugs; extract the nursing drug doses for the trauma care - related data to obtain the nursing drug doses; determine the nursing medication cycle for the trauma care - related data to obtain the nursing medication cycle;

[0054] Step S353: Divide the trauma care drug hierarchy according to the types of nursing drugs, nursing drug doses, and nursing medication cycles to generate the trauma care drug hierarchy;

[0055] Step S354: Divide the trauma care effect hierarchy for the trauma care - related data to generate the trauma care effect hierarchy;

[0056] Step S355: Divide the trauma care mode hierarchy, trauma care drug hierarchy, and trauma care effect hierarchy for trauma care knowledge hierarchy to obtain the trauma care knowledge hierarchy data.

[0057] The present invention identifies the healing characteristic stages of trauma care-related data, can accurately divide different stages of trauma healing, and obtain the healing characteristic stages. According to the healing characteristic stages, the healing characteristic care models are hierarchically divided to generate the trauma care model hierarchy. This provides a basis for formulating hierarchical care models according to different stages of trauma healing, enables the care measures to better adapt to the dynamic changes of trauma healing, and improves the pertinence and systematicness of care. By identifying the types of care drugs, extracting the doses of care drugs, and determining the care medication cycles for trauma care-related data, the drug information involved in trauma care can be comprehensively obtained, and the types of care drugs, the doses of care drugs, and the care medication cycles are obtained. The acquisition of this information provides detailed data support for subsequent drug hierarchical division and care optimization. According to the types of care drugs, the doses of care drugs, and the care medication cycles, the trauma care drug hierarchy is divided to generate the trauma care drug hierarchy. This division can systematically organize and classify drug information, and improve the rationality and effectiveness of drug use. By dividing the trauma care-related data into levels of trauma care effects, the trauma care effect hierarchy is generated. This makes the evaluation of care effects more systematic and hierarchical, can analyze the effects of care measures from different dimensions and levels, provides a more comprehensive and detailed data basis for the monitoring and improvement of care effects, and helps to identify more effective care strategies. By comprehensively dividing the trauma care model hierarchy, the trauma care drug hierarchy, and the trauma care effect hierarchy, the trauma care knowledge hierarchy data is obtained. This comprehensive division makes the organization of trauma care knowledge more systematic and hierarchical, provides a basis for constructing a comprehensive trauma care knowledge system, helps to better manage and apply trauma care knowledge, and improves the efficiency and quality of care work.

[0058] Preferably, step S4 includes the following steps:

[0059] Step S41: Perform inter-layer similarity measurement on the trauma care knowledge hierarchy data to obtain an inter-layer similarity measurement value; extract the inter-layer association strength from the inter-layer similarity measurement value to obtain the inter-layer association strength;

[0060] Step S42: Determine the inter-layer similarity coefficient for the trauma care knowledge hierarchy data according to the inter-layer association strength to generate a hierarchical similarity coefficient; based on the hierarchical similarity coefficient, perform similar layer marking on the trauma care knowledge hierarchy data to obtain similar knowledge layers; cluster and associate the similar knowledge layers to form hierarchical clustering data;

[0061] Step S43: Extract knowledge nodes from the hierarchical clustering data to obtain trauma care knowledge nodes; determine the hierarchical structure for the trauma care knowledge nodes to obtain node hierarchical structure data; determine the logical relationship for the trauma care knowledge nodes to obtain the node logical relationship;

[0062] Step S44: Based on the node hierarchy data and node logical relationships, construct the association relationships for the hierarchical clustering data to obtain the trauma care knowledge system.

[0063] The present invention measures the inter-layer similarity of the trauma care knowledge hierarchical data, which can quantify the similarity degree between different layers and obtain the inter-layer similarity measurement value. Further, extract the association strength from the inter-layer similarity measurement value to obtain the inter-layer association strength. This provides a quantitative basis for identifying the internal connections between knowledge layers, reveals the mutual relationships and influences between different nursing knowledge layers, and lays a foundation for subsequent knowledge integration and optimization. Determine the inter-layer similarity coefficient for the trauma care knowledge hierarchical data according to the inter-layer association strength to generate the hierarchical similarity coefficient. Based on the hierarchical similarity coefficient, mark the similar layers of the trauma care knowledge hierarchical data to obtain the similar knowledge layers, and cluster and associate the similar knowledge layers to form the hierarchical clustering data. This enables the effective identification and classification of similar knowledge layers, provides clearer and more ordered data support for constructing a systematic knowledge structure, and improves the logic and systematicness of knowledge organization. Extract the knowledge nodes from the hierarchical clustering data to obtain the trauma care knowledge nodes. Further, determine the hierarchical structure of the trauma care knowledge nodes to obtain the node hierarchy data, and determine the logical relationships of the trauma care knowledge nodes to obtain the node logical relationships. This provides specific data support for clarifying the hierarchical and logical relationships between knowledge nodes, makes the organization of knowledge nodes more orderly and reasonable, and lays a foundation for constructing a complete knowledge system. Based on the node hierarchy data and node logical relationships, construct the association relationships for the hierarchical clustering data to obtain the trauma care knowledge system. This construction makes the organization of trauma care knowledge more systematic and structured, forming a complete knowledge system.

[0064] In this specification, a knowledge mining system for the field of trauma care medicine is provided, which is used to execute the above-mentioned knowledge mining method for the field of trauma care medicine. The knowledge mining system for the field of trauma care medicine includes:

[0065] A trauma medicine data acquisition module, which is used to obtain the original trauma medical data; perform data preprocessing on the original trauma medical data to obtain the trauma medical data; perform standardization processing on the trauma medical data to obtain the standard trauma medical data;

[0066] A trauma medicine-nursing monitoring fusion module, which is used to obtain the nursing monitoring data of trauma patients; divide the nursing monitoring data of trauma patients into monitoring types to obtain the nursing monitoring type data; fuse the standard trauma medical data and the nursing monitoring type data to generate the trauma medicine-nursing monitoring data;

[0067] The trauma care knowledge level division module is used to determine the trauma type characteristics of trauma medicine - nursing monitoring data, generating trauma type characteristic data; evaluate the degree of trauma recovery for trauma medicine - nursing monitoring data, generating trauma recovery degree data; conduct trauma care correlation analysis on trauma medicine - nursing monitoring data based on the trauma type characteristic data and trauma recovery degree data, obtaining trauma care correlation data; perform trauma care knowledge level division on the trauma care correlation data, generating trauma care knowledge level data;

[0068] The trauma care knowledge system construction module is used to perform hierarchical clustering on the trauma care knowledge level data, obtaining hierarchical clustering data; construct a trauma care knowledge system from the hierarchical clustering data, generating a trauma care knowledge system.

[0069] Through the trauma medicine data acquisition module of the present invention, the original trauma medical data is obtained and data pre - processing and standardization processing are carried out, which can ensure the accuracy and consistency of trauma medical data. This lays a solid foundation for subsequent data analysis and application, improving the usability and reliability of the data. Through the trauma medicine - nursing monitoring fusion module, the nursing monitoring data of trauma patients is obtained and the monitoring types are divided, which can effectively classify and organize the nursing monitoring data. By fusing the standard trauma medical data with the nursing monitoring type data, trauma medicine - nursing monitoring data is generated, realizing the organic combination of medical data and nursing monitoring data. Through the trauma care knowledge level division module, the trauma type characteristics are determined and the degree of trauma recovery is evaluated for the trauma medicine - nursing monitoring data, which can accurately identify the trauma type and evaluate the trauma recovery situation. Conducting trauma care correlation analysis on the trauma medicine - nursing monitoring data based on the trauma type characteristic data and trauma recovery degree data reveals the internal connection between trauma care and patient recovery. Performing trauma care knowledge level division on the trauma care correlation data generates trauma care knowledge level data, providing a basis for constructing a systematic trauma care knowledge system. Through the trauma care knowledge system construction module, hierarchical clustering is performed on the trauma care knowledge level data, which can classify and integrate similar nursing knowledge, forming a clearer and more systematic knowledge structure. Constructing a trauma care knowledge system from the hierarchical clustering data generates a trauma care knowledge system, providing systematic and structured knowledge support for the practice and research of trauma care, and improving the quality and efficiency of trauma care. Therefore, the present invention uses data processing technology, pattern recognition technology, and deep learning technology; realizes the fusion of trauma care medical data for trauma medical text information, image information, and nursing monitoring information, and realizes the division of relevant knowledge levels for the fused trauma care medical data; thus making the knowledge system in the field of trauma care medicine more comprehensively presented. Brief Description of the Drawings

[0070] Figure 1 It is a schematic diagram of the step process of a knowledge mining method for the field of trauma care medicine;

[0071] Figure 2 It is Figure 1 a detailed implementation step process schematic diagram of step S3 in

[0072] Figure 3 It is Figure 2 a detailed implementation step process schematic diagram of step S33 in

[0073] The realization, functional features and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. Specific Embodiments

[0074] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0075] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities are implemented in the form of software, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0076] It should be understood that although terms such as "first", "second", etc. may be used here to describe each unit, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit is called the second unit, and similarly the second unit is called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0077] To achieve the above object, please refer to Figures 1 to 3 , a knowledge mining method for the field of trauma care medicine, the method includes the following steps:

[0078] Step S1: Obtain the original trauma medical data; perform data preprocessing on the original trauma medical data to obtain trauma medical data; perform standardization processing on the trauma medical data to obtain standard trauma medical data;

[0079] Step S2: Obtain the nursing monitoring data of trauma patients; classify the monitoring types of the nursing monitoring data of trauma patients to obtain nursing monitoring type data; fuse the standard trauma medical data with the nursing monitoring type data to generate trauma medicine-nursing monitoring data;

[0080] Step S3: Determine the trauma type characteristics of the trauma medicine-nursing monitoring data to generate trauma type characteristic data; evaluate the trauma recovery degree of the trauma medicine-nursing monitoring data to generate trauma recovery degree data; conduct a trauma nursing relevance analysis on the trauma medicine-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma nursing correlation data; classify the trauma nursing correlation data into trauma nursing knowledge levels to generate trauma nursing knowledge level data;

[0081] Step S4: Perform hierarchical clustering on the trauma nursing knowledge level data to obtain hierarchical clustering data; construct a trauma nursing knowledge system from the hierarchical clustering data to generate a trauma nursing knowledge system.

[0082] By obtaining the original trauma medical data and performing data preprocessing and standardization processing, the present invention can ensure the accuracy and consistency of trauma medical data. This lays a solid foundation for subsequent data analysis and applications, and improves the usability and reliability of the data. By obtaining the nursing monitoring data of trauma patients and classifying the monitoring types, the nursing monitoring data can be effectively classified and organized. Fusing the standard trauma medical data with the nursing monitoring type data to generate trauma medicine-nursing monitoring data realizes the organic combination of medical data and nursing monitoring data. Determining the trauma type characteristics and evaluating the trauma recovery degree of the trauma medicine-nursing monitoring data can accurately identify the trauma type and evaluate the trauma recovery situation. Conducting a trauma nursing relevance analysis on the trauma medicine-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data reveals the internal connection between trauma nursing and patient recovery. Classifying the trauma nursing correlation data into trauma nursing knowledge levels to generate trauma nursing knowledge level data provides a basis for constructing a systematic trauma nursing knowledge system. Performing hierarchical clustering on the trauma nursing knowledge level data can classify and integrate similar nursing knowledge to form a clearer and more systematic knowledge structure. Constructing a trauma nursing knowledge system from the hierarchical clustering data to generate a trauma nursing knowledge system provides systematic and structured knowledge support for the practice and research of trauma nursing, and improves the quality and efficiency of trauma nursing. Therefore, the present invention uses data processing technology, pattern recognition technology, and deep learning technology; realizes the fusion of trauma nursing medical data for trauma medical text information, image information, and nursing monitoring information, and realizes the division of relevant knowledge levels for the fused trauma nursing medical data; thus making the knowledge system in the field of trauma nursing medicine more comprehensively presented.

[0083] In the embodiments of the present invention, with reference to Figure 1 as shown, in this example, the knowledge mining method for the field of trauma care medicine includes the following steps:

[0084] Step S1: Obtain raw trauma medical data; perform data preprocessing on the raw trauma medical data to obtain trauma medical data; perform standardization processing on the trauma medical data to obtain standard trauma medical data;

[0085] In the embodiments of the present invention, the raw trauma medical data is obtained from the information systems of hospitals, such as the Electronic Health Record (EHR) system and the Hospital Information System (HIS). For example, through the interface in the HIS system, electronic medical records of patients, imaging data (such as CT, MRI scans), and laboratory test results are extracted. Data preprocessing includes steps such as data cleaning, format conversion, and missing value processing. For imaging data, image processing techniques are used for denoising and enhancement. For example, median filtering is used to remove noise in the image, and then the contrast of the image is enhanced through histogram equalization technology. For structured data, such as text data in electronic medical records, natural language processing (NLP) techniques are used for text cleaning and standardization, such as removing irrelevant symbols and stop words. The purpose of standardization processing is to make the data comparable and consistent among different systems and institutions. For text data, medical terms are mapped to a unified standard term system, such as the ICD-10 coding system. For numerical data, normalization processing is performed to unify its value range to between [0,1], for example, using the min-max normalization method. In addition, a unified data model is established to define data formats, field lengths, value ranges, etc., to ensure the standardization and normalization of the data.

[0086] Step S2: Obtain trauma patient care monitoring data; perform monitoring type classification on the trauma patient care monitoring data to obtain care monitoring type data; perform data fusion on the standard trauma medical data and the care monitoring type data to generate trauma medicine-care monitoring data;

[0087] In the embodiments of the present invention, the nursing monitoring data of trauma patients is obtained through the hospital's Electronic Health Record (EHR) system and Nursing Information System (NIS). For example, through the interface in the EHR system, the nursing records of patients are extracted, including vital sign monitoring data (such as heart rate, blood pressure, body temperature, etc.), nursing operation records (such as wound dressing change, infusion records, etc.), and the psychological state assessment of patients. The monitoring type classification is achieved through data classification techniques. First, different monitoring types are defined, such as vital sign monitoring, wound care monitoring, psychological state monitoring, etc. Then, classification algorithms in data mining, such as decision trees or Support Vector Machines (SVMs), are used to classify the nursing monitoring data. For example, for vital sign data, features such as heart rate and blood pressure are extracted, and the decision tree algorithm is used to classify them into categories such as normal and abnormal. Data fusion is achieved through data integration techniques. First, the standard trauma medical data and the nursing monitoring type data are aligned to ensure that the data is consistent in terms of timestamp and patient identification. Then, data fusion algorithms, such as weighted average or Bayesian fusion methods, are used to fuse the two types of data. For example, the imaging examination results of patients (such as CT scan results) are fused with the vital sign monitoring data to generate a comprehensive dataset that contains the imaging features and vital sign information of the patient.

[0088] Step S3: Determine the trauma type characteristics of the trauma medicine - nursing monitoring data to generate trauma type characteristic data; evaluate the trauma recovery degree of the trauma medicine - nursing monitoring data to generate trauma recovery degree data; perform trauma care relevance analysis on the trauma medicine - nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma care correlation data; perform trauma care knowledge level division on the trauma care correlation data to generate trauma care knowledge level data;

[0089] In the embodiments of the present invention, the determination of trauma type features is achieved through feature extraction and selection techniques in machine learning. First, features related to trauma type are extracted from trauma medicine - nursing monitoring data, such as the trauma location, trauma severity (e.g., RISS score), etc. Then, feature selection algorithms, such as mutual information or recursive feature elimination (RFE), are used to select the most representative feature combinations. For example, for the trauma location feature, the injury conditions of parts such as the head, chest, and abdomen are extracted, and combined with the trauma severity score to determine the trauma type feature dataset. The assessment of trauma recovery degree is achieved through data analysis and modeling techniques. Evaluation indicators for trauma recovery degree are defined, such as the stability of vital signs, wound healing conditions, etc. Then, regression analysis or time series analysis methods are used to model and predict the patient's recovery situation. For example, by analyzing the changing trends of the patient's vital sign data (such as heart rate, blood pressure) over time, a linear regression model is used to evaluate the recovery degree. The analysis of the relevance between trauma care is achieved through association rule mining techniques. The trauma type feature data and trauma recovery degree data are associated with the nursing monitoring data to construct an association dataset. The Apriori algorithm or FP - Growth algorithm is used to mine the association rules between trauma types and nursing measures. For example, it is found through analysis that there is a significant association between timely neurosurgical nursing measures and a higher recovery degree for patients with head trauma. The hierarchical division of trauma care knowledge is achieved through hierarchical clustering analysis techniques. First, a feature matrix is constructed according to the features in the trauma care association data, such as trauma type, nursing measures, recovery degree, etc. Then, a hierarchical clustering algorithm, such as the AGNES algorithm, is used to hierarchically divide the data to form different knowledge levels. For example, trauma care knowledge is divided into levels such as basic care, specialized care, and rehabilitation care, and each level contains corresponding nursing knowledge and measures.

[0090] Step S4: Perform hierarchical clustering on the trauma care knowledge level data to obtain hierarchical clustering data; construct a trauma care knowledge system from the hierarchical clustering data to generate a trauma care knowledge system.

[0091] In the embodiments of the present invention, hierarchical clustering is implemented through the AGNES algorithm. First, each knowledge point in the hierarchical data of trauma care knowledge is regarded as an initial cluster. Then, the distances between each pair of clusters are calculated. Commonly used distance metrics include Euclidean distance or cosine similarity. Next, according to the selected merging strategy (such as average linkage or Ward's method), the closest clusters are gradually merged. For example, using Ward's method, the clusters to be merged are selected by minimizing the variance within the clusters, thereby forming a hierarchical clustering structure. Finally, a dendrogram is generated to show the merging process and hierarchical structure of the clusters. The construction of the trauma care knowledge system is achieved through knowledge graph technology. The knowledge points in the hierarchical clustering data are used as nodes, and the relationships between the nodes are determined according to the clustering results. Specifically, basic care, specialty care, and rehabilitation care are used as the main nodes, and the hierarchical relationships between these nodes are established according to the results of hierarchical clustering. Then, graph database technology (such as Neo4j) is used to store and manage the knowledge graph, and each node and its relationships are stored as a graph structure. In this way, a complete trauma care knowledge system is constructed to support the query and reasoning of nursing knowledge.

[0092] Preferably, step S1 includes the following steps:

[0093] Step S11: Obtain the original trauma medical data, where the original trauma medical data includes trauma patient imaging examination data, trauma patient biochemical test data, and trauma patient electronic medical record data;

[0094] Step S12: Perform image enhancement on the trauma patient imaging examination data to obtain enhanced trauma imaging data; perform data calibration on the trauma patient biochemical test data to obtain calibrated biochemical test data; perform text cleaning on the trauma patient electronic medical record data to obtain standardized electronic medical record data;

[0095] Step S13: Integrate the enhanced trauma imaging data, calibrated biochemical test data, and standardized electronic medical record data to obtain trauma medical data;

[0096] Step S14: Standardize the corresponding format of the trauma medical data to generate standardized medical format data; perform standardized coding on the trauma medical data to obtain standardized trauma medical data.

[0097] In the embodiments of the present invention, the original trauma medical data includes trauma patient imaging examination data, trauma patient biochemical test data, and trauma patient electronic medical record data. The imaging examination data is obtained through the hospital's Picture Archiving and Communication System (PACS), such as CT and MRI scan image files; the biochemical test data is obtained from the hospital's Laboratory Information System (LIS), such as numerical data of test results of blood, urine, etc.; the electronic medical record data is obtained through the hospital's Electronic Health Record (EHR) system, including text data such as the patient's medical history records and doctor's order information. Image enhancement processing is performed using image processing software such as SimpleITK or OpenCV. For example, contrast enhancement is performed on CT images, and the histogram equalization method is used. By adjusting the histogram distribution of the image, the contrast of the image becomes more obvious, thereby obtaining enhanced trauma image data. Data calibration is performed using statistical analysis software such as R or the NumPy library of Python. For example, for the blood glucose value in blood tests, if a systematic error is found, the relationship between the blood glucose value and the standard value is fitted through a linear regression model, and then all blood glucose data is calibrated according to the fitting result to obtain calibrated biochemical test data. Text cleaning is performed using natural language processing (NLP) tools such as NLTK or Spacy. For example, the medical history records in the electronic medical record are cleaned, removing irrelevant symbols and stop words (such as "of", "is", etc.), and performing stemming to obtain standardized electronic medical record data. The enhanced trauma image data, calibrated biochemical test data, and standardized electronic medical record data are integrated using data fusion techniques. A unified identifier is defined for each type of data, such as patient ID and timestamp, and data fusion algorithms such as weighted average or Bayesian fusion methods are used to fuse data from different sources. For example, the imaging feature data, biochemical test numerical data, and text feature data in the electronic medical record of the patient are fused. Format standardization of the trauma medical data is performed using data conversion tools such as ETL (Extract, Transform, Load) software. For example, the imaging data is converted to DICOM format, the biochemical test data is converted to HL7 format, and the electronic medical record data is converted to XML format to generate standardized medical format data. Standardized coding of the trauma medical data is performed using medical terminology standards such as ICD-10 or SNOMED CT. The names in the electronic medical record are mapped to ICD-10 codes, and the terms in the imaging report are mapped to SNOMED CT codes to obtain standard trauma medical data.

[0098] Preferably, step S2 includes the following steps:

[0099] Step S21: Monitor the wound healing status of the patient; obtain the patient's drug usage; record the movement of the traumatized limb of the patient;

[0100] Step S22: Identify the wound healing characteristics of the patient's wound healing status to generate the wound healing characteristic type; determine the wound medication situation of the patient's drug use to generate the wound medication situation type; classify the movement situation of the traumatized limb of the patient to generate the traumatized limb movement type;

[0101] Step S23: Classify the nursing monitoring data of the trauma patient according to the wound healing characteristic type, the wound medication situation type, and the traumatized limb movement type to obtain the nursing monitoring type data;

[0102] Step S24: Perform data fusion on the standard trauma medical data and the nursing monitoring type data to generate trauma medicine-nursing monitoring data.

[0103] In the embodiment of the present invention, the optical coherence tomography (OCT) technology is used to non-invasively monitor the changes in blood vessels and structural characteristics during the wound healing process over time. For example, through OCT imaging, characteristics such as vasodilation around and immediately below the wound, and the migration of newly formed blood vessels into the wound are observed. The patient's drug use records, including information such as drug names, dosages, and usage frequencies, are extracted from the hospital's electronic health record (EHR) system. Wearable devices such as accelerometers and gyroscopes are used to real-time monitor the patient's limb movement data, such as the range of motion and movement frequency. Image recognition technology is used to process the wound pictures, and the proportion of each tissue type in the wound image is identified based on the wound color. For example, by analyzing the wound pictures, the wound healing characteristic type is obtained. The drug use records are analyzed to extract drug information related to the wound, thereby generating the wound medication situation type. Machine learning algorithms are used to classify the limb movement data. For example, the support vector machine (SVM) algorithm is adopted, and according to the movement data recorded by the accelerometer and gyroscope, the patient's limb movements are classified into types such as rest, slight activity, moderate activity, and vigorous activity. According to the wound healing characteristic type, the wound medication situation type, and the traumatized limb movement type, the nursing monitoring data of the trauma patient is classified. Data fusion is performed on the standard trauma medical data and the nursing monitoring type data, and data fusion technologies such as weighted average or Bayesian fusion methods are used. For example, the patient's imaging characteristic data (such as CT scan results), biochemical test numerical data (such as blood sugar, white blood cell count, etc.), and text characteristic data in the electronic medical record (such as the course record) are fused with the nursing monitoring type data to generate a trauma medicine-nursing monitoring dataset.

[0104] Preferably, step S24 includes the following steps:

[0105] Step S241: Identify the wound appearance features for the wound healing feature type to obtain wound appearance feature data; judge the wound healing process for the wound appearance feature data to generate wound healing process data;

[0106] Step S242: Correlate the wound healing status of the enhanced trauma image data with the wound appearance feature data and the wound healing process data to obtain image-healing correspondence data;

[0107] Step S243: Determine the wound medication types for the wound medication situation type to obtain wound medication type data; record the wound disinfection frequency for the wound medication type data to obtain the wound disinfection frequency; extract the medication dosage for the trauma patient's medication use situation based on the wound disinfection frequency to generate the wound medication dosage;

[0108] Step S244: Match and fuse the biochemical test calibration data with the wound medication type data, the wound disinfection frequency, and the wound medication dosage to obtain test-medication fusion data;

[0109] Step S245: Mark the limb joints for the trauma limb movement type to generate trauma limb joint data; determine the range of joint movement for the trauma limb joint data to obtain the range of joint movement data;

[0110] Step S246: Extract the trauma limb movement medical order requirements from the electronic medical record specification data to obtain trauma limb movement medical order data; correlate the trauma limb movement medical order data with the range of joint movement data to obtain medical record-limb movement data;

[0111] Step S247: Fuse the image-healing correspondence data, the test-medication fusion data, and the medical record-limb movement data to generate trauma medicine-nursing monitoring data.

[0112] In the embodiments of the present invention, image recognition technology is used to process wound pictures. By using a deep convolutional neural network model (such as ResNet), features of the image in the wound area are extracted to identify appearance features such as the contour and color distribution of the wound. Through the convolutional layer and pooling layer of the model, pixel features of the wound area are extracted to obtain wound appearance feature data. Based on the wound appearance feature data and in combination with a preset healing process standard, a judgment is made. Specifically, the evaluation indicators for the wound healing process are set as the reduction ratio of the wound area, the proportion of granulation tissue, etc. The wound area in the wound appearance feature data is compared with the initial area to calculate the reduction ratio; at the same time, the proportion of granulation tissue in the wound is counted, and these indicators are integrated to generate wound healing process data. The enhanced trauma image data is correlated with the wound appearance feature data and the wound healing process data. For example, the enhanced CT scan image is spatially registered with the wound contour in the wound appearance feature data to ensure that they are consistent in anatomical position. Then, the healing indicators in the wound healing process data are associated with the corresponding areas in the imaging data. For example, the reduction ratio of the wound area is corresponded to the size change of the wound area in the image to obtain image-healing corresponding data. The types of wound medication are analyzed to extract drug ingredient information. For example, ingredients such as antibiotics and painkillers are identified from the drug names to obtain wound medication type data. The number of times of wound disinfection of the patient is counted, and the time point of each disinfection is recorded. For example, by analyzing the disinfection operation records in the nursing records, the frequency of the patient receiving wound disinfection within a specific time period is extracted to obtain the wound disinfection frequency; according to the wound disinfection frequency, the drug dosage used at each disinfection is extracted from the drug use records. For example, if the patient used 10 ml of povidone iodine solution during a certain disinfection, this dosage information is associated with the corresponding disinfection time point to generate the wound medication dosage. The biochemical test calibration data is matched and fused with the wound medication type data, the wound disinfection frequency, and the wound medication dosage. For example, biochemical test indicators such as the patient's blood glucose and white blood cell count are associated with the wound medication type data to analyze the impact of drug use on the biochemical indicators; at the same time, in combination with the wound disinfection frequency and the medication dosage, the rationality and effectiveness of drug use are evaluated to obtain test-medication fusion data. The types of activities of the traumatized limb are analyzed to identify the involved joint parts. For example, by analyzing the video or image of the patient's limb movement, computer vision technology is used to mark key parts such as the shoulder joint and the knee joint to generate traumatized limb joint data; the range of motion angles of the joints is measured. For example, a wearable device such as a gyroscope is used to record the rotation angle of the joint during movement, and the maximum and minimum ranges of motion of the joint are calculated to obtain joint range of motion data; the requirements for the activities of the traumatized limb in the electronic medical record specification data are extracted to obtain traumatized limb activity order data. For example, the doctor's order for the patient's limb movement, such as "perform shoulder joint activities daily, and the range of motion is 0°-120°", is extracted from the medical record text.Then, the medical order data of the traumatized limb activities is corresponded with the range-of-joint-motion data to compare whether the patient's actual range of joint motion meets the requirements of the medical order, obtaining the medical record-limb activity data. The imaging-healing corresponding data, the detection-medication fusion data, and the medical record-limb activity data are fused. The Bayesian fusion method is adopted to calculate the fusion weights according to the credibility and relevance of each data source, and the data is weighted and fused. For example, the wound healing process in the imaging-healing corresponding data is associated with the drug use effect in the detection-medication fusion data, combined with the activity situation in the medical record-limb activity data, comprehensively evaluating the trauma recovery status of the patient, and generating a comprehensive trauma medicine-nursing monitoring dataset.

[0113] As an example of the present invention, refer to Figure 2 As shown, in this example, step S3 includes:

[0114] Step S31: Identify the trauma characteristics of the trauma medicine-nursing monitoring data to obtain trauma characteristic data; extract the manifestation characteristics of the trauma characteristic data to generate trauma manifestation characteristic data;

[0115] Step S32: Perform characterization division on the trauma manifestation characteristic data to obtain trauma characterization division data; perform trauma type characteristic pairing based on the trauma characterization division data to generate trauma type characteristic data;

[0116] Step S33: Evaluate the degree of trauma recovery of the trauma medicine-nursing monitoring data to generate trauma recovery degree data;

[0117] Step S34: Perform trauma nursing relevance analysis on the trauma medicine-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma nursing correlation data;

[0118] Step S35: Perform trauma nursing knowledge level division on the trauma nursing correlation data to generate trauma nursing knowledge level data.

[0119] In the embodiments of the present invention, a machine learning algorithm is used to perform feature recognition on trauma medicine-nursing monitoring data. The support vector machine (SVM) algorithm is adopted, and image features (such as wound area, depth), biochemical test indicators (such as white blood cell count, blood glucose level), and nursing monitoring data (such as wound dressing change frequency, patient pain score) in the dataset are used as input features to train a model to identify trauma-related feature patterns and obtain trauma feature data. The trauma feature data is further processed to extract performance features. The color distribution feature of the wound is extracted from the image features, and the proportion of red and yellow pixels in the wound area is calculated using image processing technology; features related to the inflammatory response, such as the change trend of white blood cell count, are extracted from the biochemical test indicators; and the pain relief situation after wound dressing change is extracted from the nursing monitoring data, etc., to generate trauma performance feature data. Cluster analysis is performed on the trauma performance feature data to achieve characterization division. For example, the K-means clustering algorithm is used to divide the trauma performance feature data into different characterization categories according to the color distribution feature of the wound, inflammatory response indicators, and pain relief situation, etc. The degree of trauma recovery of the trauma medicine-nursing monitoring data is evaluated using the Bayesian fusion method. First, evaluation indicators for the degree of trauma recovery are defined, such as the wound healing area, the decline range of pain score, etc. Then, according to the credibility and relevance of each data source, the fusion weights are calculated to perform weighted fusion on the data. For example, the wound healing area in the image data is fused with the decline range of pain score in the nursing monitoring data to comprehensively evaluate the degree of trauma recovery of the patient and generate trauma recovery degree data. Based on the trauma type feature data and the trauma recovery degree data, correlation analysis is performed on the trauma medicine-nursing monitoring data. For example, the Apriori algorithm, an association rule mining algorithm, is used to analyze the association rules between trauma type features and nursing measures, find out the association rules between the two, and obtain trauma nursing association data. Hierarchical cluster analysis is performed on the trauma nursing association data to achieve hierarchical division of trauma nursing knowledge. For example, the AGNES hierarchical clustering algorithm is used to construct a feature matrix according to the features in the trauma nursing association data, such as trauma type, nursing measures, recovery degree, etc., and then perform hierarchical clustering to form different knowledge levels. The trauma nursing knowledge is divided into levels such as basic nursing, specialized nursing, and rehabilitation nursing, and each level contains corresponding nursing knowledge and measures to generate trauma nursing knowledge level data.

[0120] As an example of the present invention, refer to Figure 3 shown. In this example, step S33 includes:

[0121] Step S331: Determine the tissue structure of the trauma healing area for the image-healing corresponding data to obtain the tissue structure of the healing area; identify the integrity of tissue healing for the tissue structure of the healing area to generate a structural healing integrity index;

[0122] Step S332: Determine the medication cycle for the detection-medication fusion data to obtain the trauma medication cycle data; map the promoting effect of trauma healing drugs on the tissue structure of the healing area according to the trauma medication cycle data to generate a drug promoting effect index;

[0123] Step S333: Judge the movable range of the limb for the medical record-limb activity data to obtain the movable range data of the limb; determine the degree of limb function recovery for the movable range of the limb to generate a limb function recovery index;

[0124] Step S334: Calculate the trauma recovery degree index based on the structural healing integrity index, the drug promoting effect index, and the limb function recovery index to obtain the trauma recovery degree data.

[0125] In the embodiments of the present invention, image processing technology is used to analyze the image-healing corresponding data. For example, a deep convolutional neural network model (such as ResNet) constructed using a deep learning framework is used to extract features and perform semantic segmentation on the image data of the wound area, so as to identify different tissue structures within the wound healing area, such as the epidermis, dermis, granulation tissue, etc. Through the convolutional layer and pooling layer of the model, the pixel features of the tissue structure are extracted to obtain the tissue structure of the healing area. Further analysis is performed on the tissue structure of the healing area to identify the integrity of tissue healing. For example, image analysis algorithms are used to calculate the continuity and integrity indexes of the tissue structure of the healing area, such as the smoothness of the tissue edge and the size of the tissue gap. Through these indexes, a structural healing integrity index is generated to evaluate the quality of tissue healing. Time series analysis is performed on the detection-medication fusion data to determine the trauma medication cycle. For example, time series clustering algorithms, such as K-means clustering, are used to cluster the time points in the drug use records, and adjacent drug use time points are divided into a medication cycle. By analyzing the frequency and duration of drug use, trauma medication cycle data is obtained; according to the trauma medication cycle data, a drug promotion effect mapping is performed on the tissue structure of the healing area. For example, within the determined medication cycle, the association between drug use and tissue structure changes is analyzed, and a regression analysis model is used to evaluate the promotion effect of the drug on tissue healing. The time points of drug use are corresponded to the improvement of the tissue structure to generate a drug promotion effect index; the medical record-limb movement data is analyzed to judge the movable range of the limb. For example, non-contact measurement methods, such as a camera device and an angle ruler, are used to obtain the image data of the patient's limb movement. The movement angle range of the limb joint is calculated through image processing algorithms to obtain the movable range data of the limb; further analysis is performed on the movable range of the limb to determine the degree of limb function recovery. For example, the actual limb movement range is compared with the normal movement range to calculate the recovery ratio. Combining indexes such as the patient's pain score and activity quality, a limb function recovery index is generated to evaluate the recovery of the limb function; based on the structural healing integrity index, the drug promotion effect index, and the limb function recovery index, a trauma recovery degree index is calculated. For example, a weighted average method is adopted to perform weighted summation on the structural healing integrity index, the drug promotion effect index, and the limb function recovery index according to the importance weights of each index. Through the calculated comprehensive index, trauma recovery degree data is obtained to comprehensively evaluate the trauma recovery of the patient.

[0126] Preferably, step S34 includes the following steps:

[0127] Step S341: Based on the trauma type feature data, identify the healing feature nursing mode for the image-healing corresponding data to obtain the healing feature nursing mode;

[0128] Step S342: Correlate the drug usage of the detection-medication fusion data according to the healing feature care model to obtain drug usage correlation data;

[0129] Step S343: Extract the recovery contribution degree of the trauma care model from the medical record-limb movement data based on the trauma recovery degree data to obtain the care model contribution degree;

[0130] Step S344: Sort the contribution degrees of the care model to generate a recovery contribution degree sorting value; Correlate the trauma medicine-nursing monitoring data according to the recovery contribution degree sorting value to obtain trauma care effect data;

[0131] Step S345: Combine the healing feature care model, the drug usage correlation data, and the trauma care effect data through trauma care correlation to obtain trauma care correlation data.

[0132] In the embodiments of the present invention, based on the trauma type feature data, the image-healing corresponding data is used to identify the healing feature nursing mode. The convolutional neural network (CNN) model in deep learning is used. The trauma type feature data is used as the input of the model. Combining information such as the tissue structure of the wound healing area and the structural healing integrity index in the image-healing corresponding data, the model is trained to identify the healing feature nursing mode under different trauma types. Through the convolutional layer and pooling layer of the model, the nursing mode features related to the healing features are extracted to obtain the healing feature nursing mode; according to the healing feature nursing mode, the drug use situation in the detection-medication fusion data is associated. The association rule mining algorithm, such as the Apriori algorithm, is used to perform an association analysis on the key features in the healing feature nursing mode and the drug use records in the detection-medication fusion data. The drug use pattern related to the wound healing effect under a specific healing feature nursing mode is found, such as the association between the use frequency of antibiotics and the growth rate of granulation tissue, to obtain the drug use association data; based on the trauma recovery degree data, the contribution degree of the trauma nursing mode to the recovery of the medical record-limb movement data is extracted. The regression analysis method is used. The trauma recovery degree data is used as the dependent variable, and the nursing modes (such as joint movement training, muscle strength training, etc.) in the medical record-limb movement data are used as independent variables to establish a regression model. Through model analysis, the contribution degree of different nursing modes to the trauma recovery degree is calculated to obtain the nursing mode contribution degree; the nursing mode contribution degrees are sorted. The sorting algorithm, such as quicksort or merge sort, is used to sort the nursing modes according to the numerical values of the contribution degrees to generate the recovery contribution degree sorting value; according to the recovery contribution degree sorting value, the nursing effect of the trauma medicine-nursing monitoring data is associated. The nursing modes with higher rankings are associated with the relevant indicators (such as wound healing time, pain score, etc.) in the trauma medicine-nursing monitoring data to find out the specific impact of the nursing mode on the nursing effect, to obtain the trauma nursing effect data; the healing feature nursing mode, the drug use association data, and the trauma nursing effect data are merged, and data fusion techniques, such as weighted average or Bayesian fusion method, are used. According to the credibility and relevance of each data source, the fusion weights are calculated, and the nursing mode features in the healing feature nursing mode, the drug use patterns in the drug use association data, and the nursing effect indicators in the trauma nursing effect data are weighted and fused to generate the trauma nursing association data.

[0133] Preferably, step S35 includes the following steps:

[0134] Step S351: Identify the healing feature stage for the trauma nursing association data to obtain the healing feature stage; divide the trauma nursing mode hierarchy for the healing feature nursing mode according to the healing feature stage to generate the trauma nursing mode hierarchy;

[0135] Step S352: Identify the types of nursing drugs for the trauma care - related data to obtain the types of nursing drugs; extract the dosages of nursing drugs from the trauma care - related data to obtain the dosages of nursing drugs; determine the nursing medication cycle for the trauma care - related data to obtain the nursing medication cycle;

[0136] Step S353: Classify the trauma care drugs hierarchically based on the types of nursing drugs, dosages of nursing drugs, and nursing medication cycle to generate the trauma care drug hierarchy;

[0137] Step S354: Classify the trauma care effect hierarchically for the trauma care - related data to generate the trauma care effect hierarchy;

[0138] Step S355: Classify the trauma care model hierarchy, trauma care drug hierarchy, and trauma care effect hierarchy to obtain the trauma care knowledge hierarchy data.

[0139] In the embodiments of the present invention, time series analysis technology is used to analyze the image-healing corresponding data in the trauma care-related data. For example, the image data during the wound healing process is arranged in chronological order, and different stages of wound healing are identified through the dynamic time warping (DTW) algorithm; according to the identified healing characteristic stages, hierarchical division of the healing characteristic care modes is carried out. For example, the care mode during the inflammatory phase is divided into the first-level care mode, which mainly focuses on wound cleaning and anti-infection measures; the care mode during the proliferation phase is divided into the second-level care mode, with the emphasis on promoting the growth of granulation tissue and wound contraction. Text mining is performed on the trauma care-related data to extract the type information of care drugs. For example, the named entity recognition (NER) algorithm in natural language processing (NLP) technology is used to identify drug names from the care record text. The drug dosage information is extracted from the care drug usage records. For example, by parsing the dosage description in the drug usage record, such as "100 mg each time", the dosage information is associated with the drug type to obtain the care drug dosage; the time stamp of the drug usage record is analyzed to determine the care medication cycle. The time series clustering algorithm is used to divide the time period of continuous use of a certain drug into a medication cycle; according to the care drug type, care drug dosage, and care medication cycle, hierarchical division of the trauma care drugs is carried out. For example, antibiotic drugs are divided into first-level drugs (such as low dose, short cycle) and second-level drugs (such as high dose, long cycle) according to different dosages and medication cycles, forming a trauma care drug hierarchy. The trauma care-related data is analyzed to extract trauma care effect indicators, such as wound healing time, pain score, etc., and hierarchical division is carried out according to the numerical range of the indicators. For example, the wound healing time is divided into three levels: short, medium, and long, and the pain score is divided into three levels: mild, medium, and severe, generating a trauma care effect hierarchy. The trauma care mode hierarchy, trauma care drug hierarchy, and trauma care effect hierarchy are integrated to form trauma care knowledge hierarchy data. For example, the first-level care mode is associated with the corresponding drug hierarchy and care effect hierarchy to construct a comprehensive knowledge hierarchy structure including care mode, drug use, and effect evaluation.

[0140] Preferably, step S4 includes the following steps:

[0141] Step S41: Perform inter-layer similarity measurement on the trauma care knowledge hierarchy data to obtain an inter-layer similarity measurement value; extract the inter-layer association strength from the inter-layer similarity measurement value to obtain the inter-layer association strength;

[0142] Step S42: Determine the inter-layer similarity coefficient for the trauma care knowledge hierarchy data according to the inter-layer association strength to generate a hierarchical similarity coefficient; perform similar layer marking on the trauma care knowledge hierarchy data based on the hierarchical similarity coefficient to obtain similar knowledge layers; cluster and associate the similar knowledge layers to form hierarchical clustering data;

[0143] Step S43: Extract knowledge nodes from the hierarchical clustering data to obtain trauma care knowledge nodes; determine the hierarchical structure of the trauma care knowledge nodes to obtain node hierarchical structure data; determine the logical relationships of the trauma care knowledge nodes to obtain node logical relationships.

[0144] Step S44: Based on the node hierarchical structure data and node logical relationships, construct an association relationship for the hierarchical clustering data to obtain a trauma care knowledge system.

[0145] In the embodiment of the present invention, a network similarity comparison method based on high-order information is used to measure the similarity of different levels in the trauma care knowledge hierarchical data. Calculate the node high-order clustering coefficient distribution and node distance distribution between each level, and then according to the similarity calculation formula, obtain the inter-level similarity measurement value; extract the association strength from the inter-level similarity measurement value. For example, set a strength threshold, such as the strength threshold is 10; when the similarity measurement value is higher than this threshold, it is considered that there is a strong association strength between the two levels, so as to obtain the inter-level association strength. According to the inter-level association strength, calculate the hierarchical similarity coefficient. For example, use methods such as cosine similarity or Pearson correlation coefficient to convert the inter-level association strength into a similarity coefficient to generate a hierarchical similarity coefficient; based on the hierarchical similarity coefficient, perform similar layer marking on the trauma care knowledge hierarchical data. Mark the levels with higher similarity coefficients as similar knowledge layers, and then use a hierarchical clustering algorithm (such as the AGNES algorithm), according to the similarity coefficients between the similar layers, cluster and associate them to form hierarchical clustering data. Analyze the hierarchical clustering data to extract trauma care knowledge nodes. For example, take the key knowledge points in each cluster as knowledge nodes, such as "wound cleaning", "antibiotic use", etc.; according to the positional relationship of the knowledge nodes in the hierarchical clustering data, determine the node hierarchical structure data. For example, take the "wound cleaning" node as a node at the basic care level and the "antibiotic use" node as a node at the drug care level; analyze the logical relationships between the knowledge nodes. For example, use a directed graph to represent the causal relationship or the sequence relationship between the nodes, such as the "wound cleaning" node pointing to the "antibiotic use" node, indicating that wound cleaning is a prerequisite for antibiotic use, to obtain the node logical relationship; based on the node hierarchical structure data and node logical relationships, construct an association relationship for the hierarchical clustering data. For example, associate the nodes with the same hierarchical structure to form a hierarchical knowledge structure; at the same time, according to the node logical relationship, connect the nodes with causal relationships or sequence relationships to construct a complete trauma care knowledge system.

[0146] In this specification, a knowledge mining system for the field of trauma care medicine is provided, which is used to execute the above-mentioned knowledge mining method for the field of trauma care medicine. The knowledge mining system for the field of trauma care medicine includes:

[0147] A trauma medicine data acquisition module, which is used to obtain original trauma medical data; perform data preprocessing on the original trauma medical data to obtain trauma medical data; perform standardization processing on the trauma medical data to obtain standard trauma medical data;

[0148] A trauma medicine-nursing monitoring fusion module, which is used to obtain trauma patient nursing monitoring data; perform monitoring type classification on the trauma patient nursing monitoring data to obtain nursing monitoring type data; perform data fusion on the standard trauma medical data and the nursing monitoring type data to generate trauma medicine-nursing monitoring data;

[0149] A trauma nursing knowledge level division module, which is used to determine trauma type characteristics for the trauma medicine-nursing monitoring data to generate trauma type characteristic data; evaluate the degree of trauma recovery for the trauma medicine-nursing monitoring data to generate trauma recovery degree data; perform trauma nursing relevance analysis on the trauma medicine-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma nursing association data; perform trauma nursing knowledge level division on the trauma nursing association data to generate trauma nursing knowledge level data;

[0150] A trauma nursing knowledge system construction module, which is used to perform hierarchical clustering on the trauma nursing knowledge level data to obtain hierarchical clustering data; construct a trauma nursing knowledge system from the hierarchical clustering data to generate a trauma nursing knowledge system.

[0151] Through the trauma medical data acquisition module, the original trauma medical data is obtained and subjected to data preprocessing and standardization processing, which can ensure the accuracy and consistency of trauma medical data. This lays a solid foundation for subsequent data analysis and applications, and improves the usability and reliability of the data. Through the trauma medicine-nursing monitoring integration module, the nursing monitoring data of trauma patients is obtained and the monitoring types are classified, which can effectively classify and organize the nursing monitoring data. The standard trauma medical data and the nursing monitoring type data are fused to generate trauma medicine-nursing monitoring data, realizing the organic combination of medical data and nursing monitoring data. Through the trauma nursing knowledge level division module, the trauma type characteristics are determined and the trauma recovery degree is evaluated for the trauma medicine-nursing monitoring data, which can accurately identify the trauma type and evaluate the trauma recovery situation. Based on the trauma type characteristic data and the trauma recovery degree data, the trauma nursing relevance analysis is carried out on the trauma medicine-nursing monitoring data, revealing the internal connection between trauma nursing and patient recovery. The trauma nursing relevance data is divided into trauma nursing knowledge levels to generate trauma nursing knowledge level data, providing a basis for constructing a systematic trauma nursing knowledge system. Through the trauma nursing knowledge system construction module, hierarchical clustering is performed on the trauma nursing knowledge level data, which can classify and integrate similar nursing knowledge to form a clearer and more systematic knowledge structure. The hierarchical clustering data is used to construct a trauma nursing knowledge system to generate a trauma nursing knowledge system, providing systematic and structured knowledge support for the practice and research of trauma nursing, and improving the quality and efficiency of trauma nursing. Therefore, the present invention realizes the fusion of trauma nursing medical data for trauma medical text information, image information, and nursing monitoring information through data processing technology, pattern recognition technology, and deep learning technology; and realizes the division of relevant knowledge levels of the fused trauma nursing medical data; thus making the knowledge system in the field of trauma nursing medicine more comprehensively presented.

[0152] Therefore, in any aspect, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application document are intended to be included in the present invention.

[0153] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A knowledge mining method for the field of trauma care medicine, characterized in that It includes the following steps: Step S1: Obtain the original trauma medical data; perform data preprocessing on the original trauma medical data to obtain trauma medical data; Perform standardization processing on the trauma medical data to obtain standard trauma medical data; Step S2: Obtain the nursing monitoring data of trauma patients; perform monitoring type classification on the nursing monitoring data of trauma patients to obtain nursing monitoring type data; Fuse the standard trauma medical data and the nursing monitoring type data to generate trauma medical-nursing monitoring data; Step S3: Determine the trauma type characteristics of the trauma medical-nursing monitoring data to generate trauma type characteristic data; evaluate the trauma recovery degree of the trauma medical-nursing monitoring data to generate trauma recovery degree data; perform trauma nursing relevance analysis on the trauma medical-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma nursing correlation data; perform trauma nursing knowledge level classification on the trauma nursing correlation data to generate trauma nursing knowledge level data; among them, the evaluation of the trauma recovery degree of the trauma medical-nursing monitoring data includes the following steps: Step S331: Determine the tissue structure of the trauma healing area for the image-healing corresponding data to obtain the tissue structure of the healing area; perform tissue healing integrity identification on the tissue structure of the healing area to generate a structural healing integrity index; Step S332: Determine the medication cycle for the detection-medication fusion data to obtain trauma medication cycle data; map the promoting effect of trauma healing drugs on the tissue structure of the healing area according to the trauma medication cycle data to generate a drug promoting effect index; Step S333: Judge the range of limb movement for the medical record-limb movement data to obtain limb movement range data; determine the degree of limb function recovery for the range of limb movement to generate a limb function recovery index; Step S334: Calculate the trauma recovery degree index based on the structural healing integrity index, the drug promoting effect index and the limb function recovery index to obtain trauma recovery degree data; Among them, the trauma nursing relevance analysis of the trauma medical-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data includes the following steps: Step S341: Identify the healing characteristic nursing mode for the image-healing corresponding data based on the trauma type characteristic data to obtain the healing characteristic nursing mode; Step S342: Correlate the drug usage situation for the detection-medication fusion data according to the healing characteristic nursing mode to obtain drug usage correlation data; Step S343: Extract the contribution degree of the trauma nursing mode recovery for the medical record-limb movement data based on the trauma recovery degree data to obtain the nursing mode contribution degree; Step S344: Sort the contribution degrees of the nursing mode contribution degree to generate a recovery contribution degree sorting value; correlate the nursing effect for the trauma medical-nursing monitoring data according to the recovery contribution degree sorting value to obtain trauma nursing effect data; Step S345: Combine the healing characteristic nursing mode, the drug usage correlation data and the trauma nursing effect data for trauma nursing correlation to obtain trauma nursing correlation data; Step S4: Hierarchically cluster the trauma care knowledge level data to obtain hierarchically clustered data; construct a trauma care knowledge system from the hierarchically clustered data to generate a trauma care knowledge system.

2. The knowledge mining method for the field of trauma care medicine according to claim 1, wherein Step S1 includes the following steps: Step S11: Obtain the original trauma medical data, where the original trauma medical data includes trauma patient imaging examination data, trauma patient biochemical test data, and trauma patient electronic medical record data; Step S12: Enhance the images of the trauma patient imaging examination data to obtain enhanced trauma imaging data; calibrate the trauma patient biochemical test data to obtain calibrated biochemical test data; clean the text of the trauma patient electronic medical record data to obtain standardized electronic medical record data; Step S13: Integrate the enhanced trauma imaging data, calibrated biochemical test data, and standardized electronic medical record data to obtain trauma medical data; Step S14: Standardize the corresponding format of the trauma medical data to generate standardized medical format data; perform standardized coding on the trauma medical data to obtain standard trauma medical data.

3. The knowledge mining method for the field of trauma care medicine according to claim 1, wherein Step S2 includes the following steps: Step S21: Monitor the wound healing status of the patient; obtain the patient's medication usage; record the movement of the patient's trauma-affected limb; Step S22: Identify the wound healing characteristics of the patient's wound healing status to generate wound healing characteristic types; determine the wound medication situation of the patient's medication usage to generate wound medication situation types; classify the movement of the patient's trauma-affected limb to generate trauma limb movement types; Step S23: Divide the trauma patient care monitoring data according to the wound healing characteristic types, wound medication situation types, and trauma limb movement types to obtain care monitoring type data; Step S24: Integrate the standard trauma medical data with the care monitoring type data to generate trauma medical-care monitoring data.

4. The knowledge mining method for the field of trauma care medicine according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Identify the wound appearance characteristics of the wound healing characteristic types to obtain wound appearance characteristic data; judge the wound healing process based on the wound appearance characteristic data to generate wound healing process data; Step S242: Correlate the enhanced trauma imaging data with the wound appearance characteristic data and the wound healing process data to obtain imaging-healing corresponding data; Step S243: Determine the types of wound medications for the wound medication situation types to obtain wound medication type data; record the wound disinfection frequency based on the wound medication type data to obtain the wound disinfection frequency; extract the medication dosage for the trauma patient's medication usage based on the wound disinfection frequency to generate the wound medication dosage; Step S244: Match and integrate the calibrated biochemical test data with the wound medication type data, wound disinfection frequency, and wound medication dosage to obtain test-medication integration data; Step S245: Mark the limb joints for the trauma limb movement types to generate trauma limb joint data; determine the range of joint movement based on the trauma limb joint data to obtain joint movement range data; Step S246: Extract the order requirements for limb activities of trauma from the standardized electronic medical record data to obtain the order data for limb activities of trauma; Correlate the order data for limb activities of trauma with the joint range of motion data to obtain the medical record-limb activity data; Step S247: Integrate the imaging-healing correspondence data, the test-medication fusion data, and the medical record-limb activity data to generate the trauma medicine-nursing monitoring data.

5. The knowledge mining method for the field of trauma care medicine according to claim 4, characterized in that, Step S3 includes the following steps: Step S31: Identify the trauma characteristics from the trauma medicine-nursing monitoring data to obtain the trauma characteristic data; Extract the manifestation characteristics from the trauma characteristic data to generate the trauma manifestation characteristic data; Step S32: Divide the trauma manifestation characteristic data to obtain the trauma division data; Pair the trauma type characteristics based on the trauma division data to generate the trauma type characteristic data; Step S33: Evaluate the degree of trauma recovery from the trauma medicine-nursing monitoring data to generate the trauma recovery degree data; Step S34: Analyze the correlation between trauma care and the trauma medicine-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain the trauma care correlation data; Step S35: Divide the trauma care correlation data into levels of trauma care knowledge to generate the trauma care knowledge level data.

6. The knowledge mining method for the field of trauma care medicine according to claim 5, characterized in that, Step S35 includes the following steps: Step S351: Identify the healing characteristic stage from the trauma care correlation data to obtain the healing characteristic stage; Divide the trauma care mode into levels according to the healing characteristic stage to generate the trauma care mode level; Step S352: Identify the types of nursing drugs from the trauma care correlation data to obtain the types of nursing drugs; Extract the dosage of nursing drugs from the trauma care correlation data to obtain the dosage of nursing drugs; Determine the nursing medication cycle from the trauma care correlation data to obtain the nursing medication cycle; Step S353: Divide the trauma nursing drugs into levels according to the types of nursing drugs, the dosage of nursing drugs, and the nursing medication cycle to generate the trauma nursing drug level; Step S354: Divide the trauma care correlation data into levels of trauma care effect to generate the trauma care effect level; Step S355: Divide the trauma care mode level, the trauma nursing drug level, and the trauma care effect level into levels of trauma care knowledge to obtain the trauma care knowledge level data.

7. The knowledge mining method for the field of trauma care medicine according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Measure the inter-layer similarity of the trauma care knowledge level data to obtain the inter-layer similarity measurement value; Extract the inter-layer association strength from the inter-layer similarity measurement value to obtain the inter-layer association strength; Step S42: Determine the inter-layer similarity coefficient for the trauma care knowledge level data based on the inter-layer association strength to generate the hierarchical similarity coefficient; Mark the similar knowledge layers for the trauma care knowledge level data based on the hierarchical similarity coefficient to obtain the similar knowledge layers; Cluster and associate the similar knowledge layers to form the hierarchical clustering data; Step S43: Extract knowledge nodes from the hierarchical clustering data to obtain trauma care knowledge nodes; determine the hierarchical structure of the trauma care knowledge nodes to obtain node hierarchical structure data; determine the logical relationships of the trauma care knowledge nodes to obtain node logical relationships; Step S44: Construct association relationships for the hierarchical clustering data based on the node hierarchical structure data and the node logical relationships to obtain a trauma care knowledge system.

8. A knowledge mining system for the field of trauma care medicine, characterized in that, A knowledge mining system for the field of trauma care medicine for implementing the knowledge mining method for the field of trauma care medicine as described in claim 1, the knowledge mining system for the field of trauma care medicine comprising: A trauma medicine data acquisition module, configured to obtain original trauma medical data; perform data preprocessing on the original trauma medical data to obtain trauma medical data; perform standardization processing on the trauma medical data to obtain standard trauma medical data; A trauma medicine-nursing monitoring fusion module, configured to obtain trauma patient nursing monitoring data; perform monitoring type classification on the trauma patient nursing monitoring data to obtain nursing monitoring type data; perform data fusion on the standard trauma medical data and the nursing monitoring type data to generate trauma medicine-nursing monitoring data; A trauma care knowledge level division module, configured to determine trauma type characteristics for the trauma medicine-nursing monitoring data to generate trauma type characteristic data; evaluate the degree of trauma recovery for the trauma medicine-nursing monitoring data to generate trauma recovery degree data; perform trauma care relevance analysis on the trauma medicine-nursing monitoring data based on the trauma type characteristic data and the trauma recovery degree data to obtain trauma care association data; perform trauma care knowledge level division on the trauma care association data to generate trauma care knowledge level data; A trauma care knowledge system construction module, configured to perform hierarchical clustering on the trauma care knowledge level data to obtain hierarchical clustering data; perform trauma care knowledge system construction on the hierarchical clustering data to generate a trauma care knowledge system.

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