Early warning method for joint infection after cruciate ligament reconstruction and related equipment

Abstract

The invention provides an early warning method for joint infection after cruciate ligament reconstruction and related equipment, and is applied to the technical field of data processing. The method comprises the steps of obtaining a training data set and target electronic medical record information; preprocessing the training data set to generate a training data set with identification information; processing the training data set with the identification information based on a preset processing rule to generate a training set and a verification set; obtaining an initial postoperative infection early warning model matched with the identification information; training the initial postoperative infection early warning model based on the training set and the verification set to generate a target postoperative infection early warning model; the target electronic medical record information is preprocessed, attribute information of a target user is generated, and the attribute information of the target user comprises knee joint material parameter information of the target user; the attribute information of the target user is processed based on the target postoperative infection early warning model, and the knee joint infection probability of the target user is generated.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an early warning method for joint infection after cruciate ligament reconstruction surgery and related equipment. Background Art

[0002] Anterior cruciate ligament tear is a common knee injury among athletes. Especially in sports that involve sudden stops and starts, and changes of direction, such as basketball, football, skiing and other sports, anterior cruciate ligament tear will greatly affect the knee stability of such sports enthusiasts, so arthroscopic anterior cruciate ligament reconstruction is required. Surgical operations are accompanied by the risk of infection. The knee infection rate after arthroscopic anterior cruciate ligament reconstruction is about 1.4-18‰. Once postoperative knee infection occurs, the ligament graft and articular cartilage will be destroyed, and the patient's knee function will be adversely affected. In addition, unplanned secondary surgery and large-scale use of antibiotics will place a huge burden on patients and the medical system.

[0003] Joint infection after anterior cruciate ligament reconstruction is a rare but serious complication, with a reported incidence of 0.14% to 1.8%. It has a potential impact on the integrity of articular cartilage, graft ligamentization, and patient joint function. Although surgeons are very vigilant about postoperative infection, there is still a delay from the onset of symptoms to diagnosis and treatment. Therefore, how to efficiently diagnose and treat after symptoms appear, and even predict and prevent the occurrence of postoperative joint infection, has become a matter of great concern.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of this application is to provide an early warning method and related equipment for joint infection after cruciate ligament reconstruction, which at least to some extent overcomes the problems of the prior art, by collecting matching electronic medical record data, extracting key physiological factors, splitting training and validation sets, training and optimizing the initial model to generate an accurate target model. This model analyzes patient attribute information, predicts infection risk, assists clinical decision-making, and maintains its accuracy through continuous updates.

[0006] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present invention.

[0007] According to one aspect of the present application, a method for early warning of joint infection after cruciate ligament reconstruction surgery is provided, comprising: obtaining a training data set and target electronic medical record information, wherein the training data set is other electronic medical record information that matches the target electronic medical record information; preprocessing the training data set to generate a training data set with identification information, wherein the identification information is used to characterize physiological factors that affect postoperative joint infection; processing the training data set with identification information based on preset processing rules to generate a training set and a verification set; obtaining an initial postoperative infection early warning model that matches the identification information, wherein the initial postoperative infection early warning model is set based on the target electronic medical record information received within a preset time period; training the initial postoperative infection early warning model based on the training set and the verification set to generate a target postoperative infection early warning model; preprocessing the target electronic medical record information to generate attribute information of a target user, wherein the attribute information of the target user includes knee joint material parameter information of the target user; processing the attribute information of the target user based on the target postoperative infection early warning model to generate a knee joint infection probability of the target user.

[0008] In one embodiment of the present application, the preprocessing of the training data set to generate a training data set with identification information includes: extracting features from the training data set to determine an original feature library; dividing each feature data set according to the original feature library to generate a training data set and a verification set; using a classifier to predict each verification set divided from the original feature library to determine a prediction result; using a preset algorithm to train each training data set divided from the original feature library to obtain a verification set class prediction result; and generating a training sample with identification information based on the prediction result and the verification set class prediction result.

[0009] In one embodiment of the present application, the feature extraction of the training data set to determine the original feature library includes: processing the standard material information based on preset processing rules to generate standardized features, wherein the standardized features are features of a model with clear and known material composition; processing the standardized features based on preset feature screening and dimensionality reduction rules to generate original features; and generating an original feature library from a number of original features.

[0010] In one embodiment of the present application, the initial postoperative infection warning model is trained based on the training set and the validation set to generate a target postoperative infection warning model, including: extracting multiple data groups from the training set, wherein each data group contains a preset number of data samples, wherein at least one data sample includes identification information; training the initial postoperative infection warning model based on the data samples in the multiple data groups to generate a trained postoperative infection warning model; processing the trained postoperative infection warning model based on the validation set to generate a verification result; if the data sample containing the identification information in the verification result indicates that the physiological factors affecting postoperative joint infection are in an abnormal state, then the trained postoperative infection warning model is used as the target postoperative infection warning model.

[0011] In one embodiment of the present application, the target electronic medical record information is preprocessed to generate attribute information of the target user, including: performing data cleaning processing on the target electronic medical record information to generate target user information; processing the target user information based on a preset image extraction model to generate knee joint image information of the target user; processing the knee joint image information of the target user to generate knee joint material parameter information of the target user, wherein the knee joint material parameter information includes the concentration of chemical components of synovial fluid, the thickness of articular cartilage, and the density of tissue around the joint;

[0012] generating attribute information of the target user based on the knee joint material parameter information of the target user;

[0013] The method includes obtaining a calculation formula for the concentration of chemical components of joint fluid, and the calculation formula is:

[0014]

[0015] Where C is the concentration of the chemical component, S water is the water signal intensity in the joint fluid, S baseine is the baseline signal intensity, S ref is the signal intensity of the reference substance;

[0016] The method includes obtaining a calculation formula for the thickness of articular cartilage, wherein the calculation formula is:

[0017]

[0018] Where T is the average thickness of cartilage, D max is the maximum depth of the cartilage, D min is the minimum depth of the cartilage;

[0019] The method includes obtaining a calculation formula for the density of tissue around a joint, wherein the calculation formula is:

[0020]

[0021] Where D is the average density of the tissue, HU i is the Hounsfield unit value of the ith voxel, and n is the total number of voxels.

[0022] In one embodiment of the present application, the target user's attribute information is processed based on the target postoperative infection warning model to generate the target user's knee joint infection probability, and the target postoperative infection warning model includes a calculation formula for obtaining a knee joint infection matching value, and the calculation formula is: Among them, yi represents the i-th label value of the current sample, pi represents the probability value of the i-th physiological feature in P, and k is the infection probability of k main nodes.

[0023] In one embodiment of the present application, the attribute information of the target user is processed based on the target postoperative infection warning model to generate a knee joint infection probability of the target user, including: processing the attribute information of the target user based on the target postoperative infection warning model to generate a knee joint image coding feature; processing the knee joint image coding feature based on the target postoperative infection warning model to generate a knee joint infection matching value; if the knee joint infection matching value is higher than a preset matching threshold, it indicates that the knee joint of the target user is in an abnormal state.

[0024] Another aspect of the present application is a warning device for joint infection after cruciate ligament reconstruction surgery, characterized in that it includes: an acquisition module, used to acquire a training data set and target electronic medical record information, wherein the training data set is other electronic medical record information that matches the target electronic medical record information; acquire an initial postoperative infection warning model that matches the identification information, wherein the initial postoperative infection warning model is set based on the target electronic medical record information received within a preset time period; a processing module, used to pre-process the training data set to generate a training data set with identification information, wherein the identification information is used to characterize physiological factors affecting postoperative joint infection; based on preset processing rules, the training data set with identification information is processed to generate a training set and a verification set; based on the training set and the verification set, the initial postoperative infection warning model is trained to generate a target postoperative infection warning model; the target electronic medical record information is pre-processed to generate attribute information of a target user, wherein the attribute information of the target user includes knee joint material parameter information of the target user; based on the target postoperative infection warning model, the attribute information of the target user is processed to generate a knee joint infection probability of the target user.

[0025] According to another aspect of the present application, an electronic device is characterized in that it includes: a first processor; and a memory for storing executable instructions of the first processor; wherein the first processor is configured to execute the above-mentioned early warning method for joint infection after cruciate ligament reconstruction surgery by executing the executable instructions.

[0026] According to another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a second processor, the above-mentioned early warning method for joint infection after cruciate ligament reconstruction surgery is implemented.

[0027] According to another aspect of the present application, a computer program product is provided, including a computer program, characterized in that when the computer program is executed by a third processor, the computer program implements the above-mentioned early warning method for joint infection after cruciate ligament reconstruction surgery.

[0028] The present application provides a method and related equipment for early warning of joint infection after cruciate ligament reconstruction. In the process of developing an early warning model for postoperative joint infection, firstly, a training data set matching the target electronic medical record information is obtained, and preprocessing is performed to generate a training data set with identification information. These identification information critically points out the physiological factors that may affect postoperative infection. Next, according to preset rules, the processed data set is divided into a training set and a validation set to lay the foundation for model training and evaluation. On this basis, an initial postoperative infection early warning model is selected, which is based on the electronic medical record information collected within a specific time period. The initial model is carefully trained and optimized through the training set and the validation set, and finally an accurate early warning model is generated, namely the target postoperative infection early warning model.

[0029] Subsequently, the target electronic medical record information is preprocessed to extract the target user's knee joint material parameter information and other attribute information. Using the target postoperative infection warning model, these attribute information are deeply analyzed to calculate the probability of knee joint infection of the target user. The purpose of the whole process is to provide a reliable tool to help medical professionals identify infection risks, optimize clinical decisions, and improve the quality of postoperative recovery of patients. The long-term effectiveness and adaptability of the model are ensured by continuously monitoring the model performance and updating it according to the latest medical data.

[0030] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A flow chart showing a method for early warning of joint infection after cruciate ligament reconstruction provided by an embodiment of the present application;

[0032] Figure 2 A schematic structural diagram of a device for early warning of joint infection after cruciate ligament reconstruction surgery provided by an embodiment of the present application is shown;

[0033] Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present application is shown;

[0034] Figure 4 A schematic diagram of a storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0036] Combine the following Figure 1 To describe the early warning method for joint infection after cruciate ligament reconstruction according to an exemplary embodiment of the present application. It should be noted that the following application scenarios are only shown to facilitate understanding of the spirit and principles of the present application, and the embodiments of the present application are not limited in this regard. On the contrary, the embodiments of the present application can be applied to any applicable scenario.

[0037] In one embodiment, the present application also proposes an early warning method and related equipment for joint infection after cruciate ligament reconstruction surgery. Figure 1 The flowchart of a method for early warning of joint infection after cruciate ligament reconstruction surgery according to an embodiment of the present application is schematically shown. Figure 1 As shown, the method is applied to a server, comprising:

[0038] S101, obtaining a training data set and target electronic medical record information.

[0039] In one implementation, electronic medical record data of the target user is collected, including the patient's basic information, clinical diagnosis, surgical records, treatment process, rehabilitation status, etc. The present invention is not limited to this, and the applicant can select and set it according to actual needs.

[0040] The training data set is other electronic medical record information that matches the target electronic medical record information. The target user group is determined, such as patients with anterior cruciate ligament tears. These users will be the focus of model training and analysis. Key information such as injury type, surgical details, postoperative recovery, complications, etc. are extracted from the target user's electronic medical record. Based on the target electronic medical record information, similar electronic medical record data is matched from a larger patient group to form a training data set.

[0041] S102: preprocess the training data set to generate a training data set with identification information.

[0042] In one embodiment, feature engineering is performed on the extracted electronic medical record information, including feature selection, feature conversion and feature scaling, and the training data set is annotated, especially for key clinical outcomes such as postoperative infection, to facilitate model learning. The methods for implementing feature scaling mainly include normalization and standardization. The method adopted in the present invention is to map the original feature distribution x to a normal distribution space with a mean of 0 and a variance of 1 by calculating the standard deviation and mean of each feature item (such as height, weight, BMI, etc.), and use this as the input of the neural network.

[0043] The identification information is used to characterize the physiological factors that affect postoperative joint infection, among which the physiological factors that affect postoperative joint infection include but are not limited to basic patient information (age, gender, socioeconomic level, height, weight, BMI); past history (smoking history, drinking history, previous knee surgery history, concurrent diseases, history of use of immunosuppressants); preoperative examination results (preoperative white blood cell count in routine blood tests, preoperative blood glucose concentration); surgery-related factors (time from injury to surgery, year of surgery, month of surgery, date of surgery, surgical side, single-bundle or double-bundle reconstruction, graft type, graft material, tibial fixation method, femoral fixation method, intraoperative concomitant operations, surgery start time, tourniquet time, number of drainage tubes, number of incisions, and preoperative prophylactic use of antibiotics).

[0044] The following conclusions can be drawn by performing PCA (principal component analysis) on the physiological factors affecting postoperative joint infection:

[0045] Generally speaking, if the cumulative contribution rate of the first m influencing factors exceeds 80%, it can be considered that selecting the first m principal components can well preserve the information of the sample. The m value presented in the experimental results is 7, and the first 7 principal components are arranged according to the principal component contribution rate as follows:

[0046]

[0047] In addition, the imbalance problem in the data will be dealt with, such as the possibility that the number of infected cases is smaller than that of non-infected cases. Balance will be achieved through methods such as oversampling or undersampling, and the training data set will be split into training and validation sets to evaluate the performance and generalization ability of the model.

[0048] In another embodiment, feature extraction is performed on the training data set to determine the original feature library, and key features such as basic patient information, surgical details, postoperative care measures, etc. are extracted from the training data set to construct the original feature library. The feature data sets are divided according to the original feature library to generate training data sets and validation sets; the classifier is used to predict the validation sets divided by the original feature library to determine the prediction results; the preset algorithm is used to train the training data sets divided by the original feature library to obtain the validation set class prediction results, and the trained model is applied to the validation set to generate class prediction results, and the accuracy and generalization ability of the model are evaluated. Based on the prediction results and the validation set class prediction results, training samples with identification information are generated, and the model parameters and algorithms are adjusted according to the prediction results and feedback, and the model is optimized to improve the prediction accuracy.

[0049] S103, processing the training data set with identification information based on preset processing rules to generate a training set and a verification set.

[0050] In one implementation, feature extraction is performed on a training data set with identification information to determine an original feature library; the original feature library is processed based on a neural network model to generate forged feature data, wherein the forged feature data is different from the feature data in the original feature library; the original feature library is divided to generate a training data set and a verification set, wherein the number of the training data set and the verification set is at least one, and the verification set includes the forged feature data. By generating forged feature data, preparation is made for subsequent verification of the initial postoperative infection warning model, that is, the forged feature data is actually abnormal data, but if the initial postoperative infection warning model does not identify the current data as abnormal, it indicates that the detection result of the current model is inaccurate.

[0051] S104: Acquire an initial postoperative infection warning model that matches the identification information.

[0052] In one embodiment, the initial postoperative infection warning model is set based on target electronic medical record information received within a preset time period.

[0053] Fully Connected Neural Network (FCNN) is a basic artificial neural network structure, also known as Multilayer Perceptron (MLP). In a fully connected neural network, each neuron is connected to all neurons in the previous and next layers to form a dense connection structure, and nonlinear factors are introduced through activation functions, so that the neural network can fit complex nonlinear relationships.

[0054] The structure of FCNN can be divided into input layer, hidden layer and output layer. The modeling strategy adopted by the present invention is as follows: the dimension of the input layer is set to 29, corresponding to each case data after feature scaling; the hidden layer has 2 layers, with dimensions of 300 and 100 respectively, which effectively reduce the bottleneck effect after buffering; the dimension of the output layer is set to 1, corresponding to the binary classification result of infection / non-infection. The activation function uses relu, the optimization function uses adam (it was found during the experiment that the use of SGD gradient descent method will lead to falling into the local minimum point, so the loss function is changed to adam), and the loss function uses cross entropy.

[0055] S105, training the initial postoperative infection warning model based on the training set and the validation set to generate a target postoperative infection warning model.

[0056] In one embodiment, multiple data groups are extracted from a training set, wherein each data group contains a preset number of data samples, wherein at least one data sample includes identification information, and an initial postoperative infection warning model is trained based on the data samples in the multiple data groups to generate a trained postoperative infection warning model. In the process of developing an accurate postoperative infection warning model, firstly, a data group containing a preset number of samples is extracted from the training set to ensure that each group contains at least one sample with key identification information. These identification information may indicate a patient's specific physiological state or infection risk factors. Then, the initial postoperative infection warning model is systematically trained using these data groups to form a trained model.

[0057] The trained postoperative infection warning model is processed based on the validation set to generate a validation result. Subsequently, the trained model is validated using an independent validation set to generate a validation result. When analyzing the validation results, special attention is paid to samples with identification information to determine whether the model can accurately identify the physiological factors that affect postoperative infection. If the model can effectively predict abnormal states, that is, samples with abnormal physiological factors, then the trained model is considered to be the target postoperative infection warning model. If the data sample containing identification information in the validation result indicates that the physiological factors that affect postoperative joint infection are in an abnormal state, the trained postoperative infection warning model is used as the target postoperative infection warning model.

[0058] Use training set data to train machine learning models, such as classification models, regression models, or deep learning models, evaluate the performance of the model on the validation set, adjust model parameters and structure, and optimize them. Use the trained model to predict the risk of complications such as postoperative infection for target users, and provide doctors with personalized medical advice, such as preventive measures and treatment plans, based on the model's prediction results.

[0059] In addition, the optimization and iteration of the model is an ongoing process that requires continuous adjustment based on clinical feedback and verification results to improve the accuracy and practicality of the model. After the model is applied to clinical practice, its performance is continuously monitored and regularly evaluated to ensure that the model can adapt to changes in the medical environment. Throughout the process, it is crucial to maintain data privacy and ethical standards to ensure the security and compliance of patient data. Through this process, medical professionals can build an effective early warning system to help identify infection risks in advance, optimize preventive measures, improve the quality of postoperative recovery of patients, and reduce the burden on the medical system.

[0060] S106, pre-processing the target electronic medical record information to generate attribute information of the target user.

[0061] In one embodiment, the raw data in the electronic medical record is cleaned to ensure the accuracy and consistency of the data, including removing duplicate records, correcting incorrect input, formatting date and time, etc. The target user information is extracted from the cleaned data, including basic information (such as age, gender), medical history (such as previous illness, drug allergy history), surgical records (such as surgery type, surgery time, anesthesia type), etc.

[0062] Based on medical research and clinical guidelines, screen out risk factors that may affect postoperative joint infection. These factors are not limited to: Age: Older patients may have lower immunity and weakened healing ability; Gender: Some studies have shown that gender may be associated with infection risk; Smoking history: Smoking may affect blood circulation and wound healing; Operation time: Longer duration of surgery may increase the risk of infection; Graft type: Different types of grafts may have different infection risks. Use these factors as patient identification information to obtain relevant information in the surgical unit, nursing unit, etc. in the electronic medical record system. When the target user completes the operation, classify and convert the above physiological factors that affect postoperative joint infection. For example, classify the above risk factors (such as dividing age into young, middle-aged, and elderly), and convert them as needed (such as converting smoking history to smokers and non-smokers).

[0063] In addition, the target user information is processed based on the preset image extraction model to generate the target user's knee joint image information, the target user information is processed using the preset image extraction model, the knee joint image information is extracted from the medical imaging data, the extracted knee joint image is further analyzed, and the material parameters of the knee joint, such as articular cartilage thickness, ligament integrity, etc., are identified and quantified using image processing technology. Specifically, it includes denoising, contrast enhancement, edge detection, etc. of the image to improve the image quality in preparation for subsequent analysis, using segmentation algorithms to distinguish different tissues of the knee joint, such as cartilage, ligament, bone, etc., which is a key step in quantitative analysis, extracting knee joint features from the segmented image, such as cartilage thickness, joint space width, ligament size, etc., applying morphological operations, such as expansion, corrosion, opening operation, closing operation, etc., to improve the expression of image features, if the data set contains multiple two-dimensional images, synthesizing a three-dimensional model through three-dimensional reconstruction technology to more comprehensively evaluate the knee joint structure, using software tools to measure the extracted features, such as cartilage volume, ligament length and width, joint fluid volume, etc., analyzing the characteristics of knee joint tissues, such as cartilage signal intensity distribution, which helps to identify lesions and injuries.

[0064] Process the target user's knee joint image information to generate the target user's knee joint material parameter information, and generate the target user's detailed attribute information based on the knee joint material parameter information, which is essential for evaluating the knee joint condition and formulating a treatment plan. The knee joint material parameter information includes the chemical composition concentration of the synovial fluid, the thickness of the articular cartilage, and the density of the tissue around the joint;

[0065] The method includes obtaining a calculation formula for the concentration of chemical components of synovial fluid, and the specific calculation formula is:

[0066]

[0067] Where C is the concentration of the chemical component, S water is the water signal intensity in the joint fluid, S baseine is the baseline signal intensity, S ref is the signal intensity of the reference substance;

[0068] The method includes obtaining a calculation formula for the thickness of articular cartilage, and the specific calculation formula is:

[0069]

[0070] Where T is the average thickness of cartilage, D max is the maximum depth of the cartilage, D min is the minimum depth of the cartilage;

[0071] The method includes obtaining a calculation formula for the density of tissue around the joint, and the specific calculation formula is:

[0072]

[0073] Where D is the average density of the tissue, HU i is the Hounsfield unit value of the ith voxel, and n is the total number of voxels.

[0074] The above provides a method to calculate the values ​​of knee joint material parameters, including the concentration of chemical components of joint fluid, thickness of articular cartilage, and density of periarticular tissue. The calculation of these parameters is very important in evaluating the health of the knee joint, especially in diagnosing and treating knee joint diseases. The following is a detailed explanation of each parameter:

[0075] The concentration of chemical components in joint fluid is measured by magnetic resonance spectroscopy (MRS) technology, which can help understand the concentration of specific chemical components in joint fluid.

[0076] The C in the formula represents the concentration of the chemical component, S water is the signal intensity of water molecules in the joint fluid, S baseine is the baseline signal intensity, used for correction, and S ref is the signal intensity of a reference substance of known concentration. Through this formula, the concentration of a specific chemical component in the joint fluid can be obtained, which is helpful in diagnosing inflammation or other pathological conditions.

[0077] Articular cartilage thickness. Articular cartilage is the smooth tissue that covers the ends of bones and helps reduce friction during joint movement. MRI technology can measure the thickness of cartilage, which is very useful for evaluating cartilage degeneration or damage.

[0078] T in the formula represents the average thickness of cartilage, D max and D min Respectively represent the maximum and minimum thickness of cartilage measured on MRI images. This parameter helps detect cartilage wear, which is common in diseases such as osteoarthritis.

[0079] Density of tissue around joints. Tissue density can be measured by CT scan. The Hounsfield Unit (HU) value is a unit used to quantify tissue density.

[0080] The D in the formula represents the average density of the tissue, HU i The Hounsfield Unit value obtained from a CT scan can help assess fractures, osteoporosis, or other changes in bone density. Combining these parameters, doctors can obtain a comprehensive health assessment of the knee joint, which is essential for developing a treatment plan and monitoring disease progression.

[0081] In addition, the thickness, surface smoothness, and signal intensity of the cartilage are analyzed to determine whether there is wear, degeneration, or damage; the structural integrity and functional status of the cruciate ligaments, collateral ligaments, and other tendons around the joints are evaluated; the width of the joint space is measured; the stability of the joint and the presence of inflammation or degenerative changes are evaluated; the morphology, density, and presence of fractures, bone spurs, or other bony changes of the bones are evaluated; and the synovium is observed to see if there is thickening and fluid accumulation in the joint capsule, which may indicate the presence of inflammation.

[0082] Analyze the muscles, fat pads and other soft tissues around the knee joint to check for swelling or damage. Evaluate the range of motion and functional limitations of the joint through dynamic images or patient activity data. Use image data for biomechanical analysis to understand the mechanical properties and load distribution of the joint during movement. Identify and quantify any pathological features, such as cysts, osteophytes or other abnormal structures. Combine the patient's subjective pain score and functional status with the image parameter values ​​for correlation analysis. Use the knee joint material parameter information to predict the patient's possible response to different treatment options. Develop a personalized treatment plan based on the detailed property information of the knee joint, including surgery, physical therapy or drug therapy.

[0083] S107: Process the attribute information of the target user based on the target postoperative infection warning model to generate a knee joint infection probability of the target user.

[0084] In one embodiment, an infection risk score is generated for each patient based on the above risk factors based on the target postoperative infection warning model, which may involve algorithms such as logistic regression, decision trees, and random forests to generate a preliminary infection prediction for each target user. The output result can be a numerical value of infection risk, or a classification of low, medium, and high risks. The infection risk value can be obtained by referring to the following calculation formula: Where K is the infection risk value, B is the number of decision trees, and t b (x) is the prediction result of the bth tree for feature x. The calculated infection risk value is compared with the preset threshold value to complete the classification of the corresponding risk. This application does not limit the specific preset threshold value and the corresponding classification method, and the applicant can set it according to actual needs.

[0085] If the infection risk value is higher than the preset threshold, the target user's attribute information is processed based on the target postoperative infection warning model to generate knee joint image coding features. The target postoperative infection warning model is used to analyze the target user's attribute information, including knee joint material parameter information and other relevant clinical data, and the knee joint image information is converted into coding features. These features can represent key information in the image, such as cartilage status, ligament integrity, etc.

[0086] Based on the target postoperative infection warning model, the coded features of the knee joint image are processed to generate the knee joint infection matching value. The coded features are further processed to extract the potential patterns and trends related to postoperative infection. The processed coded features are combined with the warning model to calculate the knee joint infection matching value, which quantifies the similarity between the knee joint image and the known infection features.

[0087] If the knee joint infection matching value is higher than the preset matching threshold, it indicates that the target user's knee joint is in an abnormal state. The knee joint infection matching value is compared with the preset matching threshold. The threshold is determined based on clinical experience and statistical analysis. If the knee joint infection matching value is higher than the preset threshold, it indicates that the target user's knee joint may be in an abnormal state and there is a high risk of infection.

[0088] In addition, if the knee joint is in an abnormal state, an early warning signal is generated, prompting medical professionals to conduct further evaluation and monitoring of the target user. Based on the early warning results, corresponding preventive or therapeutic intervention measures are formulated, such as antibiotic treatment, surgical intervention or other medical measures. Users identified as high-risk are continuously monitored to ensure that any signs of infection are detected and treated in a timely manner. Based on clinical feedback and new data, the early warning model is continuously optimized to improve its predictive accuracy and reliability.

[0089] This application uses the target postoperative infection warning model to predict infection based on the patient's basic information and surgery-related information. The data is highly timely and the infection probability can be output after the operation is completed. If the patient's probability of postoperative joint infection is high, a warning message will be generated to remind clinicians to pay attention and recommend a series of preventive measures, such as regular knee joint imaging examinations, processing, analyzing, and extracting parameters after obtaining images, and comparing them with imaging images of infected knee joints to automatically diagnose whether there is infection.

[0090] In another embodiment, the target postoperative infection warning model includes a calculation formula for obtaining a knee joint infection matching value, and the calculation formula is: Among them, yi represents the i-th label value of the current sample, which is usually a binary value (0 or 1), where 1 represents the presence of infection and 0 represents the absence of infection. pi represents the probability value of the i-th physiological feature in P, k is the infection probability of k main nodes, k is the number of main nodes, representing the main physiological features or risk factors considered by the model, and P is the knee joint infection matching value, which is used to measure the possibility of knee joint infection. This formula calculates a weighted entropy value based on the sample label and the probability of the physiological feature, where the weight is determined by the label value y i OK. If y i is 1, indicating that the feature is related to infection, and its corresponding entropy value will be included in the total; if y iIf is 0, the entropy value of this feature is not taken into account, which means it is not related to infection. In this way, the model is able to assess the likelihood of a sample having a knee infection.

[0091] In this application, the server obtains a training data set and target electronic medical record information, wherein the training data set is other electronic medical record information that matches the target electronic medical record information; preprocesses the training data set to generate a training data set with identification information, wherein the identification information is used to characterize the physiological factors that affect postoperative joint infection; processes the training data set with identification information based on preset processing rules to generate a training set and a validation set; obtains an initial postoperative infection warning model that matches the identification information, wherein the initial postoperative infection warning model is set based on the target electronic medical record information received within a preset time period; trains the initial postoperative infection warning model based on the training set and the validation set to generate a target postoperative infection warning model; preprocesses the target electronic medical record information to generate the target user's attribute information, wherein the target user's attribute information includes the target user's knee joint material parameter information; processes the target user's attribute information based on the target postoperative infection warning model to generate the target user's knee joint infection probability. Collect matching electronic medical record data, extract key physiological factors, split the training and validation sets, train and optimize the initial model to generate an accurate target model. This model analyzes patient attribute information, predicts infection risk, assists clinical decision-making, and maintains its accuracy through continuous updates.

[0092] Optionally, in another embodiment of the method of the present application, extracting features from the training data set to determine an original feature library includes:

[0093] Processing the standard substance information based on preset processing rules to generate standardized features, wherein the standardized features are features of a model with clear and known material composition;

[0094] Processing the standardized features based on preset feature screening and dimension reduction rules to generate original features;

[0095] Generate a primitive feature library from several primitive features.

[0096] In one embodiment, in the research and clinical application of the knee joint, data processing and feature library construction are key steps to improve the diagnosis and treatment effect. First, medical images and clinical data of the knee joint, including MRI, X-rays and patient medical records, are collected to provide a basis for analysis. Then, according to the preset processing rules, the morphological features of the knee joint, such as cartilage thickness and ligament integrity, are extracted from the images and these features are standardized.

[0097] Furthermore, through feature screening and dimensionality reduction technology, the most representative features are selected from the standardized features to construct an original feature library, which can fully reflect the status of the knee joint and provide support for the training of machine learning models. Appropriate evaluation indicators such as accuracy, sensitivity, and specificity are used to evaluate the diagnostic and predictive capabilities of the model. Through training, these models can predict the type of knee injury or postoperative recovery time, assisting doctors in making more accurate diagnoses and treatment plans.

[0098] Model evaluation is an important part of ensuring prediction accuracy, and clinical data is used to verify the diagnostic and predictive capabilities of the model. With the accumulation of new data and feedback from model evaluation results, the original feature library will be continuously updated to improve its timeliness and accuracy. Ultimately, these validated models and feature libraries can be integrated into clinical decision support systems to provide doctors with a scientific basis and optimize the patient's treatment process.

[0099] Through this process, medical professionals in the knee joint field can have a deeper understanding of the pathological state of the knee joint, provide patients with personalized treatment plans, and improve the success rate of surgery and treatment. This not only helps to improve the treatment effect and satisfaction of patients, but also provides valuable data support for the research of knee joint diseases.

[0100] By applying the above technical solution, the server obtains a training data set and target electronic medical record information, wherein the training data set is other electronic medical record information that matches the target electronic medical record information; based on the preset processing rules, the standard material information is processed to generate standardized features, wherein the standardized features are features of a model with clear and known material composition; based on the preset feature screening and dimensionality reduction rules, the standardized features are processed to generate original features; a number of original features are used to generate an original feature library; each feature data set is divided according to the original feature library to generate a training data set and a verification set; a classifier is used to predict each verification set divided from the original feature library to determine the prediction result; a preset algorithm is used to divide each training data set in the original feature library for training to obtain a verification set class prediction result; based on the prediction result and the verification set class prediction result, a training sample with identification information is generated, wherein the identification information is used to characterize the physiological factors affecting postoperative joint infection; based on the preset processing rules, the training data set with identification information is processed to generate a training set and a verification set.

[0101] An initial postoperative infection warning model matching the identification information is obtained, wherein the initial postoperative infection warning model is set based on target electronic medical record information received within a preset time period; multiple data groups are extracted from a training set, wherein each data group contains a preset number of data samples, wherein at least one data sample includes identification information; the initial postoperative infection warning model is trained based on the data samples in the multiple data groups to generate a trained postoperative infection warning model; the trained postoperative infection warning model is processed based on a validation set to generate a validation result; if the data sample containing the identification information in the validation result indicates that the physiological factors affecting postoperative joint infection are in an abnormal state, the trained postoperative infection warning model is used as the target postoperative infection warning model; data cleaning is performed on the target electronic medical record information to generate target user information; the target user information is processed based on a preset image extraction model to generate knee joint image information of the target user; the knee joint image information of the target user is processed to generate knee joint material parameter information of the target user.

[0102] The target user's attribute information is generated based on the target user's knee joint material parameter information, wherein the target user's attribute information includes the target user's knee joint material parameter information; the target user's attribute information is processed based on the target postoperative infection warning model to generate a knee joint image coding feature; the knee joint image coding feature is processed based on the target postoperative infection warning model to generate a knee joint infection matching value; the target postoperative infection warning model includes a calculation formula for obtaining a knee joint infection matching value, and the calculation formula is: Among them, yi represents the i-th label value of the current sample, pi represents the probability value of the i-th physiological feature in P, and k is the infection probability of k main nodes; if the knee joint infection matching value is higher than the preset matching threshold, it indicates that the target user's knee joint is in an abnormal state. By collecting matching electronic medical record data, extracting key physiological factors, splitting training and validation sets, training and optimizing the initial model to generate an accurate target model. This model analyzes patient attribute information, predicts infection risk, assists clinical decision-making, and maintains its accuracy through continuous updates.

[0103] In one embodiment, if Figure 2 As shown, the present application also provides an early warning device for joint infection after cruciate ligament reconstruction, comprising:

[0104] The acquisition module 201 is used to acquire a training data set and target electronic medical record information, wherein the training data set is other electronic medical record information that matches the target electronic medical record information; acquire an initial postoperative infection warning model that matches the identification information, wherein the initial postoperative infection warning model is set based on the target electronic medical record information received within a preset time period;

[0105] The processing module 202 is used to preprocess the training data set to generate a training data set with identification information, wherein the identification information is used to characterize the physiological factors affecting postoperative joint infection; process the training data set with identification information based on preset processing rules to generate a training set and a verification set; train the initial postoperative infection warning model based on the training set and the verification set to generate a target postoperative infection warning model; preprocess the target electronic medical record information to generate attribute information of the target user, wherein the attribute information of the target user includes knee joint material parameter information of the target user; process the attribute information of the target user based on the target postoperative infection warning model to generate the knee joint infection probability of the target user.

[0106] In another embodiment of the present application, the processing module 202 is configured to pre-process the training data set to generate a training data set with identification information, including:

[0107] Extracting features from the training data set to determine an original feature library;

[0108] Divide each feature data set according to the original feature library to generate a training data set and a verification set;

[0109] Use the classifier to divide the original feature library into various verification sets for prediction and determine the prediction results;

[0110] Use the preset algorithm to divide each training data set in the original feature library for training, and obtain the prediction results of the validation set class;

[0111] Based on the prediction results and the prediction results of the validation set, training samples with identification information are generated.

[0112] In another embodiment of the present application, the processing module 202 is configured to extract features from the training data set to determine an original feature library, including:

[0113] Processing the standard substance information based on preset processing rules to generate standardized features, wherein the standardized features are features of a model with clear and known material composition;

[0114] Processing the standardized features based on preset feature screening and dimension reduction rules to generate original features;

[0115] Generate a primitive feature library from several primitive features.

[0116] In another embodiment of the present application, the processing module 202 is configured to train the initial postoperative infection warning model based on the training set and the validation set to generate a target postoperative infection warning model, including:

[0117] Extracting a plurality of data groups from the training set, wherein each data group includes a preset number of data samples, wherein at least one data sample includes identification information;

[0118] Training the initial postoperative infection warning model based on data samples in the plurality of data groups to generate a trained postoperative infection warning model;

[0119] Processing the trained postoperative infection early warning model based on the verification set to generate a verification result;

[0120] If the data sample containing identification information in the verification result indicates that the physiological factors affecting postoperative joint infection are in an abnormal state, the trained postoperative infection warning model is used as the target postoperative infection warning model.

[0121] In another embodiment of the present application, the processing module 202 is configured to pre-process the target electronic medical record information to generate attribute information of the target user, including:

[0122] Performing data cleaning on the target electronic medical record information to generate target user information;

[0123] Processing the target user information based on a preset image extraction model to generate knee joint image information of the target user;

[0124] Processing the target user's knee joint image information to generate the target user's knee joint material parameter information, wherein the knee joint material parameter information includes the chemical component concentration of synovial fluid, the thickness of articular cartilage and the density of periarticular tissue;

[0125] generating attribute information of the target user based on the knee joint material parameter information of the target user;

[0126] The method includes obtaining a calculation formula for the concentration of chemical components of joint fluid, and the calculation formula is:

[0127]

[0128] Where C is the concentration of the chemical component, S water is the water signal intensity in the joint fluid, S baseine is the baseline signal intensity, S ref is the signal intensity of the reference substance;

[0129] The method includes obtaining a calculation formula for the thickness of articular cartilage, wherein the calculation formula is:

[0130]

[0131] Where T is the average thickness of cartilage, D max is the maximum depth of the cartilage, D min is the minimum depth of the cartilage;

[0132] The method includes obtaining a calculation formula for the density of tissue around a joint, wherein the calculation formula is:

[0133]

[0134] Where D is the average density of the tissue, HU i is the Hounsfield unit value of the ith voxel, and n is the total number of voxels.

[0135] In another embodiment of the present application, the processing module 202 is configured to process the attribute information of the target user based on the target postoperative infection warning model to generate a knee joint infection probability of the target user, including:

[0136] Processing the attribute information of the target user based on the target postoperative infection early warning model to generate knee joint image coding features;

[0137] Processing the knee joint image coding features based on the target postoperative infection early warning model to generate a knee joint infection matching value;

[0138] If the knee joint infection matching value is higher than a preset matching threshold, it indicates that the knee joint of the target user is in an abnormal state.

[0139] In another embodiment of the present application, the processing module 202 is configured to process the attribute information of the target user based on the target postoperative infection warning model to generate the knee joint infection probability of the target user, and further includes:

[0140] The target postoperative infection early warning model includes a calculation formula for obtaining a knee joint infection matching value, and the calculation formula is:

[0141]

[0142] Among them, yi represents the i-th label value of the current sample, pi represents the probability value of the i-th physiological feature in P, and k is the infection probability of k main nodes.

[0143] The present application embodiment provides an electronic device, such as Figure 3As shown, the electronic device 3 includes a first processor 300, a memory 301, a bus 302 and a communication interface 303, and the first processor 300, the communication interface 303 and the memory 301 are connected via the bus 302; the memory 301 stores a computer program that can be run on the first processor 300, and when the first processor 300 runs the computer program, it executes the early warning method for joint infection after cruciate ligament reconstruction surgery provided in any of the aforementioned embodiments of the present application.

[0144] The memory 301 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 303 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0145] The bus 302 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 301 is used to store a program, and the first processor 300 executes the program after receiving an execution instruction. The early warning method for joint infection after cruciate ligament reconstruction surgery disclosed in any implementation of the above-mentioned embodiment of the present application may be applied to the first processor 300, or implemented by the first processor 300.

[0146] The first processor 300 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the first processor 300. The above-mentioned first processor 300 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a readily available programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 301, and the first processor 300 reads the information in the memory 301 and completes the steps of the above method in combination with its hardware.

[0147] The electronic device provided in the above-mentioned embodiments of the present application and the early warning method for joint infection after cruciate ligament reconstruction surgery provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0148] The present application embodiment provides a computer-readable storage medium, such as Figure 4 As shown, the computer-readable storage medium 401 stores a computer program, and when the computer program is read and executed by the second processor 402, the aforementioned early warning method for joint infection after cruciate ligament reconstruction surgery is implemented.

[0149] The technical solution of the embodiment of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling an electronic device (which may be an air conditioner, a refrigeration device, a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the method described in the embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

[0150] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the early warning method for joint infection after cruciate ligament reconstruction surgery provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0151] An embodiment of the present application provides a computer program product, including a computer program, wherein the computer program is executed by a third processor to implement the method described above.

[0152] The computer program product provided in the above-mentioned embodiments of the present application and the early warning method for joint infection after cruciate ligament reconstruction surgery provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application programs stored therein.

[0153] It should be noted that, in this application, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0154] Each embodiment in the present application is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the early warning method, electronic device, electronic device, and readable storage medium embodiment for evaluating joint infection after cruciate ligament reconstruction surgery, since they are basically similar to the above-mentioned early warning method for joint infection after cruciate ligament reconstruction surgery, the description is relatively simple, and the relevant parts can be referred to the partial description of the embodiment of the early warning method for joint infection after cruciate ligament reconstruction surgery.

[0155] Although the present application is disclosed as above, the present application is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, so the protection scope of the present application shall be subject to the scope defined by the claims.

Claims

1. A method for early warning of joint infection after cruciate ligament reconstruction, characterized in that: include: Acquire a training data set and target electronic medical record information, wherein the training data set is other electronic medical record information that matches the target electronic medical record information; Preprocessing the training data set to generate a training data set with identification information, wherein the identification information is used to characterize physiological factors that affect postoperative joint infection; Processing the training data set with identification information based on preset processing rules to generate a training set and a verification set; Acquire an initial postoperative infection warning model that matches the identification information, wherein the initial postoperative infection warning model is set based on target electronic medical record information received within a preset time period; Training the initial postoperative infection early warning model based on the training set and the validation set to generate a target postoperative infection early warning model; Preprocessing the target electronic medical record information to generate attribute information of a target user, wherein the attribute information of the target user includes knee joint material parameter information of the target user; The attribute information of the target user is processed based on the target postoperative infection warning model to generate a knee joint infection probability of the target user.

2. The method according to claim 1, characterized in that The preprocessing of the training data set to generate a training data set with identification information includes: Extracting features from the training data set to determine an original feature library; Divide each feature data set according to the original feature library to generate a training data set and a verification set; Use the classifier to divide the original feature library into various verification sets for prediction and determine the prediction results; Use the preset algorithm to divide each training data set in the original feature library for training, and obtain the prediction results of the validation set class; Based on the prediction results and the prediction results of the validation set, training samples with identification information are generated.

3. The method according to claim 2, characterized in that The step of extracting features from the training data set to determine an original feature library includes: Processing the standard substance information based on preset processing rules to generate standardized features, wherein the standardized features are features of a model with clear and known material composition; Processing the standardized features based on preset feature screening and dimension reduction rules to generate original features; Generate a primitive feature library from several primitive features.

4. The method according to claim 2, characterized in that The training of the initial postoperative infection early warning model based on the training set and the validation set to generate a target postoperative infection early warning model includes: Extracting a plurality of data groups from the training set, wherein each data group includes a preset number of data samples, wherein at least one data sample includes identification information; Training the initial postoperative infection warning model based on data samples in the plurality of data groups to generate a trained postoperative infection warning model; Processing the trained postoperative infection early warning model based on the verification set to generate a verification result; If the data sample containing identification information in the verification result indicates that the physiological factors affecting postoperative joint infection are in an abnormal state, the trained postoperative infection warning model is used as the target postoperative infection warning model.

5. The method according to claim 1, characterized in that Preprocess the target electronic medical record information to generate attribute information of the target user, including: Performing data cleaning on the target electronic medical record information to generate target user information; Processing the target user information based on a preset image extraction model to generate knee joint image information of the target user; Processing the target user's knee joint image information to generate the target user's knee joint material parameter information, wherein the knee joint material parameter information includes the chemical component concentration of synovial fluid, the thickness of articular cartilage and the density of periarticular tissue; generating attribute information of the target user based on the knee joint material parameter information of the target user; The method includes obtaining a calculation formula for the concentration of chemical components of joint fluid, and the calculation formula is: Where C is the concentration of the chemical component, S water is the water signal intensity in the joint fluid, S baseine is the baseline signal intensity, S ref is the signal intensity of the reference substance; The method includes obtaining a calculation formula for the thickness of articular cartilage, wherein the calculation formula is: Where T is the average thickness of cartilage, D max is the maximum depth of the cartilage, D min is the minimum depth of the cartilage; The method includes obtaining a calculation formula for the density of tissue around a joint, wherein the calculation formula is: Where D is the average density of the tissue, HU i is the Hounsfield unit value of the ith voxel, and n is the total number of voxels.

6. The method according to claim 5, characterized in that The target user's attribute information is processed based on the target postoperative infection early warning model to generate the target user's knee joint infection probability, including: Processing the attribute information of the target user based on the target postoperative infection early warning model to generate knee joint image coding features; Processing the knee joint image coding features based on the target postoperative infection early warning model to generate a knee joint infection matching value; If the knee joint infection matching value is higher than a preset matching threshold, it indicates that the knee joint of the target user is in an abnormal state.

7. The method according to claim 6, characterized in that The target user's attribute information is processed based on the target postoperative infection early warning model to generate the target user's knee joint infection probability, further comprising: The target postoperative infection early warning model includes a calculation formula for obtaining a knee joint infection matching value, and the calculation formula is: Among them, y i represents the i-th label value of the current sample, p i represents the probability value of the i-th physiological feature in P, and k is the infection probability of k main nodes.

8. A device for early warning of joint infection after cruciate ligament reconstruction, characterized in that: The device comprises: An acquisition module is used to acquire a training data set and target electronic medical record information, wherein the training data set is other electronic medical record information that matches the target electronic medical record information; acquire an initial postoperative infection warning model that matches the identification information, wherein the initial postoperative infection warning model is set based on the target electronic medical record information received within a preset time period; A processing module is used to preprocess the training data set to generate a training data set with identification information, wherein the identification information is used to characterize physiological factors that affect postoperative joint infection; process the training data set with identification information based on preset processing rules to generate a training set and a verification set; train the initial postoperative infection warning model based on the training set and the verification set to generate a target postoperative infection warning model; preprocess the target electronic medical record information to generate attribute information of a target user, wherein the attribute information of the target user includes knee joint material parameter information of the target user; process the attribute information of the target user based on the target postoperative infection warning model to generate a knee joint infection probability of the target user.

9. An electronic device, characterized in that: include: a first processor; and a memory for storing executable instructions of the first processor; Wherein, the first processor is configured to execute the early warning method for joint infection after cruciate ligament reconstruction surgery as described in any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the second processor, the method for early warning of joint infection after cruciate ligament reconstruction surgery as claimed in any one of claims 1 to 7 is implemented.

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