An intelligent analysis method, device, equipment and medium for trauma care data

Abstract

The present invention relates to the technical field of medical data processing, and provides an intelligent analysis method, device, equipment and medium for trauma care data, including preprocessing the trauma care data collected by medical sensors to obtain a data subset; acquiring a multi-source medical data set of other medical devices of a patient; processing various types of medical data through a trauma feature extraction model to obtain a feature data set; fusing the feature data; generating the severity and type of trauma assessment information based on the fused data and a trauma assessment model set; and generating a personalized trauma care plan in combination with the trauma care data, the assessment model set and the assessment information. The present invention can improve the accuracy and efficiency of trauma care.

Description

Technical Field

[0001] The present invention relates to the technical field of medical data processing, and more specifically, the present invention relates to an intelligent analysis method, device, equipment and medium for trauma care data. Background Art

[0002] In the modern medical field, trauma care is a crucial link, the purpose of which is to promote the recovery of patients and reduce complications through accurate assessment and effective care of the patient's trauma situation. With the continuous development of medical technology, medical sensors and a variety of medical devices are widely used in the monitoring and data collection of trauma patients. These devices can collect the patient's physiological parameters, trauma site information and relevant data during the treatment process in real time, providing rich information resources for medical staff. However, these data sources are extensive, in various formats and large in volume. How to effectively integrate and analyze these multi-source data to generate accurate trauma assessments and personalized care plans has become a major challenge in current medical informatization.

[0003] Existing trauma care data processing methods mainly rely on the analysis of a single data source, such as only physiological data collected by medical sensors or imaging examination results. Although these methods can reflect the patient's trauma situation to a certain extent, due to the lack of comprehensive analysis of multi-source data, they often cannot comprehensively evaluate the severity and complexity of the patient's trauma. In addition, traditional trauma assessment methods mainly rely on the experience and subjective judgment of medical staff, lacking objective and quantitative assessment criteria, which easily leads to inconsistencies and biases in assessment results. In the process of formulating trauma care plans, it is also often unable to provide targeted and personalized care measures due to the lack of accurate grasp of the patient's trauma situation.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: First, the prior art cannot effectively integrate multi-source data from different medical devices, resulting in low data utilization rate and unable to give full play to the advantages of multi-source data; Second, there is a lack of objective and quantitative methods for trauma assessment, relying on the experience judgment of medical staff, which easily leads to subjectivity and inconsistency in assessment results; Finally, the existing trauma care plan formulation process lacks accurate analysis of the patient's trauma situation, making it difficult to generate personalized and targeted care plans and unable to meet the actual needs of different patients. Summary of the Invention

[0005] The present invention provides an intelligent analysis method, device, equipment and medium for trauma care data.

[0006] In the first aspect of the present invention, there is provided an intelligent analysis method, device, equipment and medium for trauma care data, including:

[0007] Perform data preprocessing on the pre-collected trauma care data to obtain a data subset, where the trauma care data is data collected by a medical sensor for a patient's trauma situation and the data accuracy meets the first condition;

[0008] For each patient information in the patient information set corresponding to the data subset, obtain a multi-source medical data set collected by other medical devices for the patient corresponding to the patient information, where the data accuracy meets different second conditions;

[0009] For each multi-source medical data set in the obtained multi-source medical data set group, input various types of medical data in the multi-source medical data set into a pre-trained trauma feature information extraction model to obtain a feature data set;

[0010] For each feature data set in the obtained feature data set group, perform feature data fusion on each feature data in the feature data set to obtain fused feature data;

[0011] For each fused feature data in the obtained fused feature data set, generate the severity of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set according to the fused feature data and the trauma assessment model set;

[0012] Generate a trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group.

[0013] Further, the performing data preprocessing on the trauma care data to obtain a data subset includes: extracting key feature information of the trauma care data;

[0014] Perform preprocessing on the trauma care data according to the key feature information to obtain a preprocessed data set;

[0015] Screen out the preprocessed data that meets the third condition from the preprocessed data set as the data subset to obtain the data subset.

[0016] Further, generating the trauma care plan for the patient according to the severity of each trauma assessment information in the trauma assessment information set group obtained from the trauma care data, the trauma assessment model set, and the type information of each trauma assessment information in the trauma assessment information set group, includes: generating the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set corresponding to the trauma care data and the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set according to the trauma care data and the trauma assessment model set;

[0017] Generating the trauma care plan for the patient according to the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set, the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set, the severity of each trauma assessment information in the trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group.

[0018] Further, generating the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set corresponding to the trauma care data and the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set according to the trauma care data and the trauma assessment model set, includes: obtaining the priority of each trauma assessment model in the trauma assessment model set;

[0019] Selecting the trauma assessment models with priorities meeting the fourth condition from the trauma assessment model set as the target trauma assessment models;

[0020] Inputting the trauma care data into the pre-trained target trauma assessment model to obtain the severity of each first trauma assessment information in the first trauma assessment information set and the type information of each first trauma assessment information in the first trauma assessment information set;

[0021] Inputting the trauma care data into each trauma assessment model in at least one trauma assessment model in the trauma assessment model set to output the severity of each second trauma assessment information in the second trauma assessment information set and the type information of each second trauma assessment information in the second trauma assessment information set, and obtaining the severity of each second trauma assessment information in the second trauma assessment information set group and the type information of each second trauma assessment information in the second trauma assessment information set group, where the at least one trauma assessment model is each trauma assessment model in the trauma assessment model set excluding the target trauma assessment model;

[0022] For each severity level of each of the first trauma assessment information, perform the following determination steps: Determine whether there is a severity level in the severities of the respective second trauma assessment information that is the same as or close to the severity level of the first trauma assessment information;

[0023] In response to determining that there is, screen out from the severities of the respective second trauma assessment information the severity levels that are the same as or close to the severity level of the first trauma assessment information as the first target severity levels, obtaining at least one first target severity level;

[0024] Determine the first number of each of the first target severity levels corresponding to the at least one first target severity level;

[0025] In response to determining that the first number is greater than or equal to a predetermined threshold, determine the severity level of the first trauma assessment information as the severity level of the preliminary trauma assessment information, and determine the type information of the first trauma assessment information as the type information of the corresponding preliminary trauma assessment information.

[0026] Further, generating the severity levels of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set according to the fused feature data and the trauma assessment model set includes: inputting the fused feature data into a pre-trained target trauma assessment model to obtain the severity levels of each third trauma assessment information in a third trauma assessment information set and the type information of each third trauma assessment information in the third trauma assessment information set;

[0027] Input the fused feature data into each trauma assessment model in the at least one trauma assessment model to output the severity levels of each fourth trauma assessment information in a fourth trauma assessment information set and the type information of each fourth trauma assessment information in the fourth trauma assessment information set, obtaining the severity levels of each fourth trauma assessment information in a fourth trauma assessment information set group and the type information of each fourth trauma assessment information in the fourth trauma assessment information set group;

[0028] For each severity level of each of the third trauma assessment information, perform the following determination steps: Determine whether there is a severity level in the severities of the respective fourth trauma assessment information that is the same as or close to the severity level of the third trauma assessment information;

[0029] In response to determining that there is, screen out from the severities of the respective fourth trauma assessment information the severity levels that are the same as or close to the severity level of each third trauma assessment information as the second target severity levels, obtaining at least one second target severity level;

[0030] Determine the second number of each second target severity corresponding to the at least one second target severity;

[0031] In response to determining that the second number is greater than or equal to the predetermined threshold, determine the severity of the third trauma assessment information as the severity of the trauma assessment information, and determine the type information of the third trauma assessment information as the type information of the corresponding trauma assessment information.

[0032] Further, generating the trauma care plan for the patient according to the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set, the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set, the severity of each trauma assessment information in the trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group, includes: for each preliminary trauma assessment information in the respective preliminary trauma assessment information, perform the following generation steps to generate the care measures corresponding to the preliminary trauma assessment information: Determine the type information of the preliminary trauma assessment information;

[0033] Determine the trauma site or trauma type corresponding to the type information;

[0034] Determine the trauma assessment information set in the trauma assessment information set group corresponding to the trauma site or trauma type as the target trauma assessment information set;

[0035] Determine whether there is a trauma assessment information in the target trauma assessment information set that has the same or related trauma site or trauma type as the preliminary trauma assessment information;

[0036] In response to determining the existence, generate care measures applicable to the patient's trauma;

[0037] Generate the trauma care plan for the patient according to the obtained respective care measures.

[0038] Further, the method further includes:

[0039] Obtain the severity of each trauma assessment information corresponding to the trauma care plan;

[0040] Determine the traumas that need to be processed first in each trauma according to the severity of each trauma assessment information to obtain a priority list;

[0041] Optimize the trauma care plan according to the priority list.

[0042] In a second aspect of the present invention, there is provided a trauma care data intelligent analysis device, including:

[0043] A preprocessing unit, configured to perform data preprocessing on pre-collected trauma care data to obtain a data subset, wherein the trauma care data is data collected by a medical sensor for a patient's trauma situation and satisfies a first condition in terms of data accuracy;

[0044] An acquisition unit, configured to obtain, for each patient information in a patient information set corresponding to the data subset, a multi-source medical data set collected by other medical devices for the patient corresponding to the patient information and satisfying different second conditions in terms of data accuracy;

[0045] An input unit, configured to input various types of medical data in the multi-source medical data set into a pre-trained trauma feature information extraction model respectively for each multi-source medical data set in the obtained multi-source medical data set group to obtain a feature data set;

[0046] A fusion unit, configured to perform feature data fusion on each feature data in the obtained feature data set group to obtain fused feature data;

[0047] A first generation unit, configured to generate the severity of each trauma assessment information in a trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set according to the fused feature data and a trauma assessment model set for each fused feature data in the obtained fused feature data set;

[0048] A second generation unit, configured to generate a trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group.

[0049] In a third aspect of the present invention, an electronic device is provided, which includes: at least one processor, a memory, and an input / output unit; wherein, the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the method according to any one of the first aspect.

[0050] In a fourth aspect of the present invention, a computer-readable storage medium is provided, which includes instructions that, when running on a computer, cause the computer to execute the method according to any one of the first aspect.

[0051] The above embodiments of the present invention have at least the following beneficial effects: The present invention can integrate the trauma care data collected by medical sensors with multi-source medical data from other medical devices, and through data preprocessing, feature extraction and fusion processing, construct more comprehensive patient trauma feature information. Based on the multi-model collaborative analysis of the trauma assessment model set, the severity and type of trauma assessment information can be accurately generated, providing a reliable basis for formulating subsequent care plans.

[0052] This method can combine the preliminary trauma assessment information with the assessment results after multi-source data fusion to dynamically generate personalized trauma care plans. Through the multi-model priority screening and result verification mechanism, the accuracy of trauma assessment can be improved, while ensuring the scientificity and pertinence of care plans, and ultimately optimizing the clinical decision-making efficiency and patient rehabilitation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of example and not limitation, wherein:

[0054] Figure 1 is a schematic flowchart of a method for intelligent analysis of trauma care data provided by an embodiment of the present invention;

[0055] Figure 2 is a schematic structural diagram of a device for intelligent analysis of trauma care data provided by an embodiment of the present invention;

[0056] Figure 3 schematically shows a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0057] The principles and spirit of the present invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and then implement the present invention, and not to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to be able to fully convey the scope of the present invention to those skilled in the art.

[0058] Those skilled in the art know that the embodiments of the present invention can be implemented as a device, equipment, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0059] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0060] The following refers to Figure 1 , Figure 1 a schematic flowchart of a method for intelligent analysis of trauma care data provided by an embodiment of the present invention. As Figure 1 shown, a method, device, equipment and medium for intelligent analysis of trauma care data include:

[0061] S1 Perform data preprocessing on the pre-collected trauma care data to obtain a data subset. Among them, the trauma care data is data collected by a medical sensor for the patient's trauma situation and the data accuracy meets the first condition;

[0062] S2 For each patient information in the patient information set corresponding to the data subset, obtain a multi-source medical data set collected by other medical devices for the patient corresponding to the patient information and the data accuracy meets different second conditions;

[0063] S3 For each multi-source medical data set in the obtained multi-source medical data set group, input various types of medical data in the multi-source medical data set into a pre-trained trauma feature information extraction model respectively to obtain a feature data set;

[0064] S4 For each feature data set in the obtained feature data set group, perform feature data fusion on each feature data in the feature data set to obtain fused feature data;

[0065] S5 For each fused feature data in the obtained fused feature data set, according to the fused feature data and the trauma assessment model set, generate the severity of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set;

[0066] S6 Generate a trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group and the type information of each trauma assessment information in the trauma assessment information set group.

[0067] It should be noted that the present invention proposes a method, device, equipment and medium for intelligent analysis of trauma care data. The core lies in performing data preprocessing on the pre-collected trauma care data to obtain a data subset. Here, the trauma care data refers to data collected by a medical sensor for the patient's trauma situation, and the accuracy of these data needs to meet certain conditions to ensure its reliability and effectiveness. The purpose of data preprocessing is to extract useful information from the original data, remove noise and redundant data, so as to improve the efficiency and accuracy of subsequent analysis. In this way, a high-quality data basis can be provided for subsequent multi-source medical data fusion and trauma assessment.

[0068] Specifically, a data subset refers to a set of data selected from the preprocessed data that meets specific conditions. These conditions can be set according to actual needs, such as data integrity, relevance, etc. For each patient's information, it is also necessary to obtain multi-source medical data sets collected by other medical devices. The accuracy of these data may vary depending on the device type and collection purpose. The multi-source medical data sets include, but are not limited to, physiological parameters, imaging data, laboratory test results, etc. These data can reflect the patient's trauma situation from different perspectives. By inputting these multi-source medical data into a pre-trained trauma feature information extraction model respectively, a feature data set can be obtained. The role of this model is to extract trauma-related feature information from complex medical data for more accurate analysis and evaluation in the follow-up.

[0069] Preferably, the construction of the trauma feature information extraction model can be achieved through machine learning or deep learning algorithms, such as convolutional neural network (CNN) or long short-term memory network (LSTM). These models can automatically learn the feature patterns in the data. In terms of input parameters, the model needs to receive preprocessed multi-source medical data, which may include time series data (such as physiological parameters like heart rate, blood pressure, etc.) and static data (such as imaging examination results). During the data preprocessing process, the data can be normalized to eliminate the dimensional differences between different data sources, and at the same time, outliers and missing values can be removed to ensure the integrity and consistency of the data. In addition, methods such as weighted average and principal component analysis (PCA) can be used in the feature data fusion step to effectively integrate feature data from different sources and generate fused feature data, providing more comprehensive input information for trauma assessment.

[0070] More specifically, the first condition refers to a specific standard that the trauma care data collected by medical sensors need to meet in terms of accuracy, which is used to measure the reliability and effectiveness of the data. In practical applications, it can be set as the physiological parameter data collected by medical sensors, such as heart rate, blood pressure, etc. The measurement error needs to be controlled within a certain range. For example, the heart rate measurement error does not exceed ±2 beats / minute, and the blood pressure measurement error for systolic pressure does not exceed ±5 mmHg and for diastolic pressure does not exceed ±3 mmHg. The first condition is to ensure that the subsequent analysis and evaluation based on these data can accurately reflect the patient's trauma situation. If the data accuracy does not meet the standard, it may lead to deviations in trauma assessment and affect the formulation of subsequent care plans.

[0071] The second condition is the requirement for the data accuracy of the multi-source medical data set collected by other medical devices. Due to different device types and collection purposes, the data accuracy requirements are also different from the first condition for the data collected by medical sensors. Taking imaging devices as an example, for X-ray images of fracture trauma, the image resolution needs to reach the level where key information such as fracture lines and the positions of bone fragments can be clearly displayed. For example, the resolution is not less than 300 dpi, and the gray values of the images need to accurately reflect the density differences between bones and surrounding tissues. For laboratory test data, such as white blood cell count in a blood routine, the detection error needs to be controlled within the allowable range. For example, the error does not exceed ±0.5×10 9 / L. Because the functions and uses of different medical devices vary, only by meeting the corresponding data accuracy can valuable information be provided for multi-source data fusion and trauma assessment.

[0072] In some embodiments, the data preprocessing of the trauma care data to obtain a data subset includes: extracting key feature information of the trauma care data;

[0073] Preprocessing the trauma care data according to the key feature information to obtain a preprocessed data set;

[0074] Screening out the preprocessed data that meets the third condition from the preprocessed data set as the data subset to obtain the data subset.

[0075] It should be noted that the present invention further refines the process of data preprocessing for trauma care data. Specifically, the goal of data preprocessing is to extract key feature information from the original trauma care data and screen and process the data based on these key features, ultimately obtaining a data subset that meets specific conditions. Here, the key feature information refers to the core data that can effectively reflect the patient's trauma situation, such as the trauma site, trauma degree, changes in physiological parameters, etc. By extracting and analyzing these key features, data valuable for trauma assessment can be more accurately screened, thereby improving the efficiency and accuracy of the entire analysis method.

[0076] Specifically, extracting the key feature information of trauma care data refers to identifying and extracting data features directly related to trauma assessment from the original data. These features may include, but are not limited to, physiological parameters of the trauma site (such as blood loss, pain level), trauma types (such as lacerations, fractures), and the patient's vital signs (such as heart rate, blood pressure). Next, preprocessing the trauma care data based on the key feature information means performing operations such as data cleaning, normalization, and noise reduction on the data based on the extracted key features to remove irrelevant information and noisy data and retain the useful part for trauma assessment. Finally, screening out the preprocessed data that meets the third condition from the preprocessed dataset as a data subset means further screening out high-quality data subsets from the preprocessed data according to preset screening conditions (such as data integrity, feature relevance, etc.) to provide a basis for subsequent multi-source data fusion and trauma assessment.

[0077] The third condition is the screening criterion for screening out data subsets from the preprocessed dataset, which can be set that the data integrity should reach more than 90%, that is, the proportion of missing values in the dataset does not exceed 10%; at the same time, the correlation coefficient of the key features is greater than 0.8. For example, the correlation coefficient between the physiological parameters of the trauma site and the trauma severity should meet this requirement. When screening data, those data with high integrity and key features closely related to trauma assessment are preferably retained.

[0078] Preferably, the extraction of key feature information can be achieved through feature selection algorithms, such as methods based on information gain, mutual information, etc., to screen out the most valuable features for trauma assessment from a large amount of original data. During the preprocessing process, the data can be normalized to unify data with different dimensions to the same range. For example, all physiological parameters are normalized to the interval [0, 1] for subsequent analysis. At the same time, a filtering algorithm can be used to remove noise from the data, such as using a low-pass filter to remove high-frequency noise. The setting of the screening conditions can be flexibly adjusted according to actual needs. For example, it is required that the data integrity reaches more than 90% and the correlation coefficient of the key features is greater than 0.8, so as to ensure that the selected data subsets can effectively support subsequent trauma assessment and the generation of care plans.

[0079] In some embodiments, generating the patient's trauma care plan according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group includes: generating the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set corresponding to the trauma care data and the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set according to the trauma care data and the trauma assessment model set;

[0080] Generate a trauma care plan for the patient based on the severity of each piece of preliminary trauma assessment information in the preliminary trauma assessment information set, the type information of each piece of preliminary trauma assessment information in the preliminary trauma assessment information set, the severity of each piece of trauma assessment information in the trauma assessment information set group, and the type information of each piece of trauma assessment information in the trauma assessment information set group.

[0081] It should be noted that the present invention further clarifies how to generate a trauma care plan for a patient based on trauma care data and a trauma assessment model set. Specifically, this process includes two main steps: First, generate preliminary trauma assessment information based on trauma care data and a trauma assessment model set, including the severity and type information of the trauma; Second, comprehensively generate a trauma care plan for the patient by combining the preliminary trauma assessment information and the trauma assessment information obtained through multi-source medical data fusion. Here, the preliminary trauma assessment information refers to the trauma assessment results initially obtained based on a single data source (such as trauma care data), while the trauma assessment information set group refers to the more comprehensive trauma assessment results obtained through multi-source data fusion. By combining these two parts of information, a more accurate and personalized trauma care plan can be generated.

[0082] Specifically, trauma care data refers to the data related to the patient's trauma collected by medical sensors, and these data can provide basic information for trauma assessment after preprocessing. The trauma assessment model set is a group of pre-trained models used to evaluate the severity and type of trauma. These models may include models based on statistical analysis, machine learning models, or deep learning models, etc. When generating preliminary trauma assessment information, the trauma care data will be input into the trauma assessment model set to obtain the preliminary trauma severity and type information. The trauma assessment information set group is the more comprehensive trauma assessment results obtained through multi-source medical data fusion, and these results include the feature information extracted from multi-source data from different medical devices. By comprehensively considering the preliminary trauma assessment information and the trauma assessment information after multi-source data fusion, a trauma care plan that better conforms to the actual situation of the patient can be generated.

[0083] Preferably, when generating the preliminary trauma assessment information, the models in the trauma assessment model set can be prioritized, and the model with the highest priority is selected as the target trauma assessment model. For example, the priority can be determined according to performance indicators such as the accuracy rate and recall rate of the model. After inputting the trauma care data into the target trauma assessment model, the first trauma assessment information is obtained, including the severity and type information of the trauma. At the same time, the trauma care data is input into other models in the trauma assessment model set to obtain a set of second trauma assessment information. By comparing the first trauma assessment information with the information in the set of second trauma assessment information, the results with the same or similar severity as the first trauma assessment information are screened out, and the quantity is counted. If the quantity reaches a predetermined threshold, the severity and type information of the first trauma assessment information are determined as the preliminary trauma assessment information. When generating the trauma care plan, the trauma site or trauma type can be determined according to the type information of the preliminary trauma assessment information, and then the relevant trauma assessment information set can be found. If there is trauma assessment information with the same or related trauma site or trauma type as the preliminary trauma assessment information in this information set, the care measures applicable to the patient are generated. Finally, the trauma care plan for the patient is generated based on all the care measures.

[0084] In some embodiments, generating the severity of each preliminary trauma assessment information and the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set corresponding to the trauma care data according to the trauma care data and the trauma assessment model set includes: obtaining the priority of each trauma assessment model in the trauma assessment model set;

[0085] Selecting a trauma assessment model whose priority meets the fourth condition from the trauma assessment model set as the target trauma assessment model;

[0086] Inputting the trauma care data into the pre-trained target trauma assessment model to obtain the severity of each first trauma assessment information and the type information of each first trauma assessment information in the first trauma assessment information set;

[0087] Inputting the trauma care data into each trauma assessment model in at least one trauma assessment model in the trauma assessment model set to output the severity of each second trauma assessment information and the type information of each second trauma assessment information in the second trauma assessment information set, and obtaining the severity of each second trauma assessment information in the set of second trauma assessment information and the type information of each second trauma assessment information in the set of second trauma assessment information, where the at least one trauma assessment model is each trauma assessment model in the trauma assessment model set excluding the target trauma assessment model;

[0088] For each severity level of each of the first trauma assessment information, perform the following determination steps: Determine whether there is a severity level in the severity levels of the respective second trauma assessment information that is the same as or similar to the severity level of the first trauma assessment information;

[0089] In response to determining that there is, screen out from the severity levels of the respective second trauma assessment information the severity levels that are the same as or similar to the severity level of the first trauma assessment information as the first target severity levels, obtaining at least one first target severity level;

[0090] Determine the first number of each of the first target severity levels corresponding to the at least one first target severity level;

[0091] In response to determining that the first number is greater than or equal to a predetermined threshold, determine the severity level of the first trauma assessment information as the severity level of the preliminary trauma assessment information, and determine the type information of the first trauma assessment information as the type information of the corresponding preliminary trauma assessment information.

[0092] It should be noted that the present invention further refines the process of generating preliminary trauma assessment information based on trauma care data and a trauma assessment model set. By obtaining the priorities of each model in the trauma assessment model set and selecting the model whose priority meets specific conditions as the target trauma assessment model, the severity and type of trauma can be evaluated more accurately. This method not only considers the performance differences of different models, but also further verifies and optimizes the accuracy of the evaluation results by comparing the output results of multiple models. Finally, by screening and counting the evaluation results that meet the conditions, the preliminary trauma assessment information is determined, providing a reliable basis for the generation of subsequent trauma care plans.

[0093] Specifically, a trauma assessment model set refers to a group of pre-trained models used to evaluate the severity and type of trauma. These models may be based on different algorithms, such as decision trees, support vector machines (SVMs), neural networks, etc. Each model has its unique performance characteristics and applicable scenarios. Priority refers to the result of ranking models according to their accuracy, reliability, or other performance metrics. Models with higher priority are generally considered more reliable when evaluating trauma. The target trauma assessment model is selected from the trauma assessment model set and has a priority that meets specific conditions (such as the highest priority) and is used to preliminarily evaluate the severity and type of trauma. After inputting trauma care data into the target trauma assessment model, the first trauma assessment information is obtained, including the severity and type information of the trauma. At the same time, the trauma care data is input into other models in the trauma assessment model set (i.e., at least one trauma assessment model), and a second set of trauma assessment information is obtained. By comparing the severity in the first trauma assessment information and the second set of trauma assessment information, results with the same or similar severity as the first trauma assessment information are screened out, and the number thereof (i.e., the first number) is counted. If the first number is greater than or equal to a predetermined threshold, the severity and type information of the first trauma assessment information are determined as the preliminary trauma assessment information.

[0094] Preferably, the trauma assessment model set can be constructed by collecting a large amount of historical trauma data and training using machine learning or deep learning algorithms. For example, convolutional neural networks (CNNs) can be used to process imaging data, or recurrent neural networks (RNNs) can be used to process time series data. During the model training process, the input parameters can include the patient's physiological parameters (such as heart rate, blood pressure), image data of the trauma site, laboratory test results, etc. For the setting of model priority, models can be ranked according to performance metrics such as accuracy and recall rate on the validation set. For example, the model with the highest priority may be the one with the highest accuracy. During the screening and counting process, a similarity threshold can be set. For example, a difference in severity within a certain range (such as ±10%) is considered the same or similar. The predetermined threshold can be set according to actual needs. For example, it is set to 3, indicating that when the evaluation results of at least 3 models are consistent with the results of the target model, the result is determined as the preliminary trauma assessment information.

[0095] The fourth condition is used to select a target trauma assessment model from a set of trauma assessment models based on the priority setting of the models. It can be defined as selecting the model with the highest priority from the set of trauma assessment models as the target trauma assessment model. The model priority can be determined by sorting according to performance indicators such as the accuracy rate and recall rate of the model on a large number of historical trauma data validation sets. For example, in the validation of 1000 trauma cases, the models with an accuracy rate of more than 90% and a recall rate of more than 85% are arranged in descending order, and the first-ranked model is selected as the target trauma assessment model that meets the fourth condition.

[0096] In some embodiments, generating the severity of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set according to the fused feature data and the set of trauma assessment models includes: inputting the fused feature data into a pre-trained target trauma assessment model to obtain the severity of each third trauma assessment information in the third trauma assessment information set and the type information of each third trauma assessment information in the third trauma assessment information set;

[0097] Inputting the fused feature data into each trauma assessment model in the at least one trauma assessment model to output the severity of each fourth trauma assessment information in the fourth trauma assessment information set and the type information of each fourth trauma assessment information in the fourth trauma assessment information set, and obtaining the severity of each fourth trauma assessment information in the fourth trauma assessment information set group and the type information of each fourth trauma assessment information in the fourth trauma assessment information set group;

[0098] For each severity of the third trauma assessment information among the severities of the respective third trauma assessment information, the following determination steps are performed: determining whether there is a severity in the severities of the respective fourth trauma assessment information that is the same as or close to the severity of the third trauma assessment information;

[0099] In response to determining the existence, screening out the severities that are the same as or close to the severity of each third trauma assessment information from the severities of the respective fourth trauma assessment information as the second target severity, to obtain at least one second target severity;

[0100] Determining the second number of the respective second target severities corresponding to the at least one second target severity;

[0101] In response to determining that the second number is greater than or equal to the predetermined threshold, determining the severity of the third trauma assessment information as the severity of the trauma assessment information, and determining the type information of the third trauma assessment information as the type information of the corresponding trauma assessment information.

[0102] It should be noted that the present invention further refines the process of generating trauma assessment information based on the fused feature data and the trauma assessment model set. By inputting the fused feature data into a pre-trained trauma assessment model, more accurate trauma assessment results can be obtained, including the severity and type information of the trauma. In addition, by comparing the output results of multiple models, the accuracy of the assessment results is further verified and optimized. This method not only makes full use of the advantages of multi-source data fusion, but also improves the reliability of the assessment results by comparing multiple models, providing a more accurate basis for generating subsequent trauma care plans.

[0103] Specifically, the fused feature data refers to the data after feature data fusion processing, which integrates the feature information of multi-source data from different medical devices and can more comprehensively reflect the trauma situation of the patient. The trauma assessment model set is a group of pre-trained models for assessing the severity and type of trauma. These models may be based on different algorithms, such as decision trees, support vector machines (SVMs), neural networks, etc., and each model has its unique performance characteristics and applicable scenarios. The target trauma assessment model is selected from the trauma assessment model set and meets specific conditions in terms of priority, and is used to initially assess the severity and type of trauma. After inputting the fused feature data into the target trauma assessment model, the third trauma assessment information is obtained, including the severity and type information of the trauma. At the same time, the fused feature data is input into other models in the trauma assessment model set (i.e., at least one trauma assessment model), and the fourth trauma assessment information set group is obtained. By comparing the severity in the third trauma assessment information and the fourth trauma assessment information set group, the results with the same or similar severity as the third trauma assessment information are screened out, and the number thereof (i.e., the second number) is counted. If the second number is greater than or equal to a predetermined threshold, the severity and type information of the third trauma assessment information are determined as the final trauma assessment information.

[0104] Preferably, the construction of the trauma assessment model set can be achieved by collecting a large amount of historical trauma data and training using machine learning or deep learning algorithms. For example, convolutional neural networks (CNNs) can be used to process imaging data, or recurrent neural networks (RNNs) can be used to process time series data. During model training, the input parameters can include the patient's physiological parameters (such as heart rate, blood pressure), image data of the trauma site, laboratory test results, etc. For the setting of model priorities, they can be sorted according to performance metrics such as accuracy and recall rate on the validation set. For example, the model with the highest priority may be the one with the highest accuracy. During the screening and statistical process, a similarity threshold can be set. For example, a difference in severity within a certain range (such as ±10%) is considered the same or similar. The predetermined threshold can be set according to actual needs. For example, it can be set to 3, indicating that the result is determined as the final trauma assessment information only when the evaluation results of at least 3 models are consistent with the results of the target model. In addition, during the feature data fusion process, methods such as weighted average and principal component analysis (PCA) can be used to effectively integrate feature data from different sources to generate fused feature data, providing more comprehensive input information for trauma assessment.

[0105] In some embodiments, generating the trauma care plan for the patient according to the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set, the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set, the severity of each trauma assessment information in the trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group includes: for each preliminary trauma assessment information in the preliminary trauma assessment information, performing the following generating steps to generate the care measures corresponding to the preliminary trauma assessment information: determining the type information of the preliminary trauma assessment information;

[0106] determining the trauma site or trauma type corresponding to the type information;

[0107] determining the trauma assessment information set in the trauma assessment information set group corresponding to the trauma site or trauma type as the target trauma assessment information set;

[0108] determining whether there is a trauma assessment information in the target trauma assessment information set that has the same or related trauma site or trauma type as the preliminary trauma assessment information;

[0109] in response to determining the existence, generating care measures applicable to the patient's trauma;

[0110] generating the trauma care plan for the patient according to the obtained respective care measures.

[0111] It should be noted that the present invention further clarifies how to generate a trauma care plan for a patient based on the preliminary trauma assessment information and the trauma assessment information after multi-source data fusion. The core of this process lies in comprehensively considering the preliminary trauma assessment information and the trauma assessment information after multi-source data fusion, and generating targeted and personalized nursing measures by analyzing the trauma type, trauma site, and relevant assessment information. This method can make full use of the advantages of multi-source data, ensure the scientificity and effectiveness of the care plan, and thus better meet the rehabilitation needs of patients.

[0112] Specifically, the preliminary trauma assessment information refers to the trauma severity and type information initially generated based on trauma care data and a set of trauma assessment models. These information provide a basic basis for formulating the trauma care plan. The trauma assessment information set group refers to the more comprehensive trauma assessment results obtained through multi-source medical data fusion. These results include the feature information extracted from multi-source data of different medical devices and can more accurately reflect the patient's trauma situation. The target trauma assessment information set refers to the set of trauma assessment information related to the preliminary trauma assessment information. By comparing and screening, trauma assessment information with the same or related trauma site or trauma type as the preliminary trauma assessment information can be found. Based on this information, nursing measures applicable to the patient can be generated, and finally a complete trauma care plan can be formed.

[0113] Preferably, during the process of generating the trauma care plan, the nursing measures corresponding to each preliminary trauma assessment information can be further refined. First, determine the trauma site or trauma type according to the type information of the preliminary trauma assessment information. For example, if the preliminary trauma assessment information shows a fracture, then determine the trauma site as the bone. Then, screen out the trauma assessment information related to this trauma site or trauma type from the trauma assessment information set group as the target trauma assessment information set. For example, if the trauma site is the leg, the target trauma assessment information set may include the leg imaging examination results, relevant physiological parameters, etc. Then, analyze whether there is trauma assessment information in the target trauma assessment information set with the same or related trauma site or trauma type as the preliminary trauma assessment information. For example, if there is an imaging examination result of a leg fracture in the target trauma assessment information set that is consistent with the preliminary trauma assessment information, nursing measures applicable to the patient, such as fixation, pain relief, rehabilitation training, etc., are generated. Finally, a trauma care plan for the patient is generated based on all the nursing measures to ensure the pertinence and personalization of the care plan.

[0114] In some embodiments, the method further includes:

[0115] Obtaining the severity of each trauma assessment information corresponding to the trauma care plan;

[0116] Determine the traumas that need to be prioritized among the various traumas according to the severity of the respective trauma assessment information, and obtain a priority list;

[0117] Optimize the trauma care plan according to the priority list.

[0118] It should be noted that the present invention further expands the optimization steps after generating the trauma care plan. After generating the trauma care plan, by obtaining the severity of the trauma assessment information and determining the traumas that need to be prioritized among the various traumas according to these severities, a priority list is formed. Subsequently, the trauma care plan is optimized according to the priority list to ensure that serious traumas can be processed more efficiently during the actual care process, improving the care effect and the patient's recovery speed. This optimization process reflects the emphasis on the dynamic assessment of the patient's trauma situation and personalized care, further enhancing the scientificity and effectiveness of trauma care.

[0119] Specifically, the severity of the trauma assessment information refers to the quantitative indicators of the patient's trauma severity obtained through the trauma assessment model. These indicators can be numerical values, grades, or other forms of representation methods, and are used to measure the urgency and severity of the trauma. The priority list is a sorted list generated according to the severity of the trauma assessment information, which clarifies which traumas need to be prioritized. For example, the trauma with the highest severity will be ranked at the top of the list. When optimizing the trauma care plan, the order of care measures and resource allocation will be adjusted according to the priority list to ensure that the most serious traumas are processed first. For example, for traumas with high severity, surgery or emergency treatment may be arranged first, while for traumas with lower severity, subsequent rehabilitation treatment or observation may be arranged.

[0120] Preferably, when generating the priority list, a severity threshold can be set. For example, the severity is divided into three grades: high, medium, and low. For traumas with high severity, they are directly listed at the top of the priority list; for traumas with medium severity, they are further sorted according to specific circumstances; while for traumas with low severity, they are arranged in the subsequent care plan. When optimizing the trauma care plan, the allocation of care resources can be adjusted according to the priority list. For example, for traumas with high priority, more medical staff and medical equipment can be allocated to ensure that the patient can receive timely and effective treatment. At the same time, for traumas with lower priority, the care time and resources can be reasonably arranged to avoid waste of resources. In addition, the priority list and the care plan can be dynamically adjusted according to the actual situation of the patient and the availability of care resources to adapt to the changing care needs.

[0121] The above embodiments of the present invention have the following beneficial effects: The present invention can perform fusion analysis by combining the trauma care data collected by medical sensors with the data of multi-source medical devices, thereby improving the comprehensiveness and accuracy of trauma assessment. Through data preprocessing, feature extraction, and multi-model collaborative verification mechanisms, the error risk of a single data source can be effectively reduced, while ensuring that key trauma feature information is not omitted, providing a more reliable basis for subsequent nursing decisions.

[0122] This method can dynamically generate personalized trauma care plans, significantly improving the credibility of the evaluation results through priority screening and cross-verification of multi-model results. By combining the preliminary evaluation with the comprehensive judgment after multi-source data fusion, the pertinence and adaptability of nursing measures can be optimized, ultimately achieving more precise clinical intervention while improving the utilization efficiency of medical resources.

[0123] As Figure 2 shown, a trauma care data intelligent analysis device according to some embodiments, the device includes:

[0124] A preprocessing unit 201, configured to perform data preprocessing on the pre-collected trauma care data to obtain a data subset, wherein the trauma care data is data collected by a medical sensor for a patient's trauma situation and satisfies a first condition in terms of data accuracy;

[0125] An acquisition unit 202, configured to obtain, for each patient information in the patient information set corresponding to the data subset, a multi-source medical data set collected by other medical devices for the patient corresponding to the patient information and satisfying different second conditions in terms of data accuracy;

[0126] An input unit 203, configured to input each type of medical data in the multi-source medical data set into a pre-trained trauma feature information extraction model for each multi-source medical data set in the obtained multi-source medical data set group to obtain a feature data set;

[0127] A fusion unit 204, configured to perform feature data fusion on each feature data in the feature data set for each feature data set in the obtained feature data set group to obtain fused feature data;

[0128] A first generation unit 205, configured to generate, for each fused feature data in the obtained fused feature data set, the severity of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set;

[0129] A second generation unit 206, configured to generate a trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group.

[0130] It can be understood that the various modules described in this trauma care data intelligent analysis device correspond to the respective steps in the trauma care data intelligent analysis method described in the reference Figure 1 Therefore, the operations, features, and beneficial effects described above for the trauma care data intelligent analysis method also apply to the trauma care data intelligent analysis device and the modules included therein, and will not be elaborated here.

[0131] Next, referring to Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not impose any limitations on the functions and usage scopes of the embodiments of the present invention.

[0132] As Figure 3 shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage device 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0133] Generally, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 can allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3An electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or have all the shown devices. Instead, more or fewer devices may be implemented or had. Figure 3 Each box shown in Figure 3 may represent a device or, as needed, multiple devices.

[0134] Furthermore, the storage medium of the embodiments of the present application stores program instructions capable of implementing all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. And the aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets.

[0135] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A method for intelligent analysis of trauma care data, characterized in that: include: Performing data preprocessing on the pre-collected trauma care data to obtain a data subset, wherein the trauma care data is data collected by a medical sensor based on the patient's trauma condition and the data accuracy meets the first condition; For each piece of patient information in the patient information set corresponding to the data subset, obtaining a multi-source medical data set collected by other medical devices for the patient corresponding to the patient information and having data accuracy that meets different second conditions; For each multi-source medical data set in the obtained multi-source medical data set group, each type of medical data in the multi-source medical data set is input into a pre-trained trauma feature information extraction model to obtain a feature data set; For each feature data set in the obtained feature data set group, performing feature data fusion on each feature data in the feature data set to obtain fused feature data; For each fused feature data in the obtained fused feature data set, generating, according to the fused feature data and the trauma assessment model set, the severity of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set; Generate a trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group; wherein, generating a trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group comprises: generating, according to the trauma care data and the trauma assessment model set, the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set corresponding to the trauma care data and the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set; A trauma care plan for the patient is generated based on the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set, the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set, the severity of each trauma assessment information in the trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group.

2. The method according to claim 1, characterized in that The preprocessing of the wound care data to obtain a data subset includes: extracting key feature information of the wound care data; Preprocessing the trauma care data according to the key feature information to obtain a preprocessed data set; Preprocessed data satisfying a third condition is screened out from the preprocessed data set as a data subset to obtain the data subset.

3. The method according to claim 2, characterized in that The step of generating, based on the trauma care data and the trauma assessment model set, the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set corresponding to the trauma care data and the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set comprises: obtaining the priority of each trauma assessment model in the trauma assessment model set; Selecting a trauma assessment model whose priority satisfies a fourth condition from the trauma assessment model set as a target trauma assessment model; Inputting the trauma care data into a pre-trained target trauma assessment model to obtain severity of each first trauma assessment information in the first trauma assessment information set and type information of each first trauma assessment information in the first trauma assessment information set; Inputting the trauma care data into each trauma assessment model in at least one trauma assessment model in the trauma assessment model set to output the severity of each second trauma assessment information in the second trauma assessment information set and the type information of each second trauma assessment information in the second trauma assessment information set, thereby obtaining the severity of each second trauma assessment information in the second trauma assessment information set group and the type information of each second trauma assessment information in the second trauma assessment information set group, wherein the at least one trauma assessment model is each trauma assessment model in the trauma assessment model set with the target trauma assessment model removed; For each severity level of the first trauma assessment information, the following determination step is performed: determining whether there is a severity level that is the same as or similar to the severity level of the first trauma assessment information among the severity levels of the second trauma assessment information; In response to determining that there is, selecting a severity that is the same as or close to the severity of the first trauma assessment information from the severity of each of the second trauma assessment information as a first target severity, thereby obtaining at least one first target severity; determining a first number of first target severities corresponding to the at least one first target severity; In response to determining that the first number is greater than or equal to a predetermined threshold, the severity of the first trauma assessment information is determined as the severity of preliminary trauma assessment information, and the type information of the first trauma assessment information is determined as the type information corresponding to the preliminary trauma assessment information.

4. The method according to claim 3, characterized in that The step of generating the severity of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and the type information of each trauma assessment information in the trauma assessment information set according to the fused feature data and the trauma assessment model set comprises: inputting the fused feature data into a pre-trained target trauma assessment model to obtain the severity of each third trauma assessment information in the third trauma assessment information set and the type information of each third trauma assessment information in the third trauma assessment information set; Inputting the fused feature data into each trauma assessment model in the at least one trauma assessment model to output the severity of each fourth trauma assessment information in the fourth trauma assessment information set and the type information of each fourth trauma assessment information in the fourth trauma assessment information set, and obtaining the severity of each fourth trauma assessment information in the fourth trauma assessment information set group and the type information of each fourth trauma assessment information in the fourth trauma assessment information set group; For each severity level of the third trauma assessment information, the following determination step is performed: determining whether there is a severity level that is the same as or similar to the severity level of the third trauma assessment information among the severity levels of the fourth trauma assessment information; In response to determining that there is, selecting a severity that is the same as or close to the severity of each third trauma assessment information from the severity of each fourth trauma assessment information as a second target severity, thereby obtaining at least one second target severity; determining a second number of respective second target severities corresponding to the at least one second target severity; In response to determining that the second number is greater than or equal to the predetermined threshold, the severity of the third trauma assessment information is determined as the severity of the trauma assessment information, and the type information of the third trauma assessment information is determined as the type information of the corresponding trauma assessment information.

5. The method according to claim 4, characterized in that The step of generating a trauma care plan for the patient according to the severity of each piece of preliminary trauma assessment information in the preliminary trauma assessment information set, the type information of each piece of preliminary trauma assessment information in the preliminary trauma assessment information set, the severity of each piece of trauma assessment information in the trauma assessment information set group, and the type information of each piece of trauma assessment information in the trauma assessment information set group comprises: for each piece of preliminary trauma assessment information in the preliminary trauma assessment information, performing the following generating steps to generate a nursing measure corresponding to the preliminary trauma assessment information: determining the type information of the preliminary trauma assessment information; Determining the wound site or wound type corresponding to the type information; Determine a trauma assessment information set in the trauma assessment information set group corresponding to the trauma site or trauma type as a target trauma assessment information set; Determining whether there is trauma assessment information in the target trauma assessment information set having the same or related trauma site or trauma type as the preliminary trauma assessment information; In response to determining presence, generating a care measure applicable to the patient's injury; A trauma care plan for the patient is generated based on the various nursing measures obtained.

6. The method according to claim 1, characterized in that The method further comprises: Obtaining the severity of each trauma assessment information corresponding to the trauma care plan; According to the severity of each trauma assessment information, determine the trauma that needs to be treated first among the traumas to obtain a priority list; Based on the priority list, the wound care regimen is optimized.

7. A trauma care data intelligent analysis device, characterized in that: include: A preprocessing unit is configured to perform data preprocessing on the pre-collected wound care data to obtain a data subset, wherein the wound care data is data collected by a medical sensor according to a patient's wound condition and the data accuracy meets a first condition; The acquisition unit is configured to acquire, for each patient information in the patient information set corresponding to the data subset, a multi-source medical data set acquired by other medical devices for the patient corresponding to the patient information and having data accuracy meeting different second conditions; An input unit is configured to input each type of medical data in the multi-source medical data set into a pre-trained trauma feature information extraction model for each multi-source medical data set in the obtained multi-source medical data set group, to obtain a feature data set; A fusion unit is configured to perform feature data fusion on each feature data set in the obtained feature data set group to obtain fused feature data; A first generating unit is configured to generate, for each fused feature data in the obtained fused feature data set, severity of each trauma assessment information in the trauma assessment information set corresponding to the fused feature data and type information of each trauma assessment information in the trauma assessment information set according to the fused feature data and the trauma assessment model set; The second generating unit is configured to generate a trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group; wherein, the generating of the trauma care plan for the patient according to the trauma care data, the trauma assessment model set, the severity of each trauma assessment information in the obtained trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group comprises: generating, according to the trauma care data and the trauma assessment model set, the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set corresponding to the trauma care data and the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set; A trauma care plan for the patient is generated based on the severity of each preliminary trauma assessment information in the preliminary trauma assessment information set, the type information of each preliminary trauma assessment information in the preliminary trauma assessment information set, the severity of each trauma assessment information in the trauma assessment information set group, and the type information of each trauma assessment information in the trauma assessment information set group.

8. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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