Gout flare prediction method, system, device, and medium

Abstract

The application provides a gout recurrence prediction method, system, device and medium, comprising: obtaining clinical data of a gout comorbidity patient during hospitalization from an electronic health record system of a multicenter dataset; processing the clinical data according to a standardization, min-max normalization and Yeo-Johnson transformation method to obtain candidate clinical features; applying various feature selectors to screen the extracted candidate clinical features to screen out clinical features highly related to the gout comorbidity recurrence risk; applying various classifiers to construct a gout recurrence prediction candidate gout recurrence prediction model; screening the gout recurrence prediction candidate model to obtain a gout recurrence prediction model; obtaining clinical features of a patient; and predicting the gout recurrence risk of the patient according to the clinical features of the patient and the gout recurrence prediction model, which can accurately screen out clinical features highly related to the gout comorbidity recurrence risk and construct an efficient prediction model, thereby providing reliable auxiliary decision support for clinicians.

Description

Technical Field

[0001] The present application relates to the technical field of gout recurrence prediction, and in particular to a gout recurrence prediction method, system, electronic device and storage medium. Background Art

[0002] Gout comorbidity refers to a state in which gout patients suffer from one or more other diseases at the same time. Gout is an inflammatory arthritis caused by elevated serum uric acid (SUA) levels, which leads to the deposition of urate in and around the joints. This inflammatory arthritis is characterized by sudden onset of joint pain, swelling, and inflammation, and often recurs. Comorbidity (or multimorbidity) is a damaged life state in which multiple diseases coexist in the same human body, and it is also the general term for these diseases. Epidemiological studies have shown that the prevalence of comorbidities is high and may continue to rise in the future. For gout patients, they may face a higher risk of comorbidities due to long-term hyperuricemia and gout attacks. These comorbidities may include but are not limited to cardiovascular disease, diabetes, kidney disease, etc., and there is a complex interaction between these diseases and gout.

[0003] The management of patients with gout comorbidities is relatively complex because the treatment and care of multiple diseases must be considered simultaneously. However, there is currently a lack of predictive models for relapse in patients with gout comorbidities during hospitalization, which increases the difficulty of managing patients with gout comorbidities. Existing prediction models are mostly targeted at outpatients or patients with specific diseases. There is a lack of effective predictive tools for gout patients with complex conditions and multiple comorbidities during hospitalization. This makes it challenging for doctors to predict the risk of relapse in patients with gout comorbidities and difficult to develop personalized treatment plans. Summary of the Invention

[0004] In view of this, it is necessary to provide a gout recurrence prediction method, system, electronic device and storage medium that can overcome at least one of the above defects.

[0005] In a first aspect, embodiments of the present application provide a method for predicting gout recurrence, the method comprising:

[0006] Obtain clinical data of gout comorbidity patients during hospitalization from the electronic health record system of a multicenter data set, wherein the clinical data include basic information, medical history, laboratory test results, diagnosis and treatment process, medication use, length of hospitalization, and follow-up data after discharge of the gout comorbidity patients;

[0007] Processing the clinical data according to standardization, minimum-maximum normalization, and Yeo-Johnson transformation methods to obtain candidate clinical features, wherein the candidate clinical features are used to predict the risk of recurrence of gout comorbidities;

[0008] Applying multiple feature selectors to screen the extracted candidate clinical features to screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities;

[0009] Multiple classifiers were used to construct candidate gout recurrence prediction models;

[0010] screening the candidate models for predicting gout recurrence to obtain a gout recurrence prediction model, wherein the gout recurrence prediction model is a model constructed according to an iterative interpolation method and gradient enhancement based on minimum-maximum scaling and feature selection;

[0011] Obtain the patient's clinical characteristics;

[0012] The patient's gout recurrence risk is predicted based on the patient's clinical characteristics and the gout recurrence prediction model.

[0013] In one embodiment, the gout recurrence prediction method further comprises:

[0014] obtaining variables in the clinical data;

[0015] Excluding variables with missing values ​​higher than a threshold value to obtain the candidate clinical features;

[0016] Imputing missing values ​​in the candidate clinical characteristics according to iterative interpolation, mean interpolation, median interpolation, and KNN interpolation methods;

[0017] The candidate clinical features were subjected to collinear transformation according to standardization, min-max normalization and Yeo-Johnson transformation to improve the stability of the gout recurrence prediction model.

[0018] In one embodiment, screening the candidate gout recurrence prediction model to obtain a gout recurrence prediction model includes:

[0019] Obtain internal training set and internal validation set;

[0020] Obtaining the area under the receiver operating characteristic curve of the gout recurrence prediction candidate model based on the internal training set and the internal validation set;

[0021] Performing a preliminary screening of the candidate model for predicting gout recurrence according to the area under the receiver operating characteristic curve to obtain a preliminary screening result;

[0022] Performing a secondary screening on the primary screening results according to the DeLong test to obtain a secondary screening result;

[0023] Obtain external validation sets and forward-looking validation sets;

[0024] The gout recurrence prediction candidate model in the secondary screening results is verified according to the external validation set and the prospective validation set to obtain the gout recurrence prediction model.

[0025] In one embodiment, the gout recurrence prediction method further comprises:

[0026] The linear relationship between the candidate clinical features was evaluated based on the Pearson correlation coefficient and the variance inflation factor;

[0027] The candidate clinical features whose variance inflation factor or Pearson correlation coefficient does not meet the requirements are excluded.

[0028] In one embodiment, predicting the patient's gout recurrence risk based on the patient's clinical characteristics and the gout recurrence prediction model further includes:

[0029] Obtaining prediction scores of the gout recurrence prediction model for multiple instances;

[0030] Calculating the optimal classification threshold based on the Youden index and the prediction score;

[0031] comparing the prediction score to the optimal classification threshold;

[0032] If the prediction score is equal to or greater than the optimal threshold, the instance is classified as positive;

[0033] If the prediction score is less than the optimal threshold, the instance is classified as negative;

[0034] Generate classification decision results based on instance classification.

[0035] In one embodiment, the feature selector includes: conditional information maximization feature extraction, conditional mutual information maximization, dual-input symmetric correlation, interaction upper limit, joint mutual information, mutual information-based feature selection, mask image modeling, maximum correlation minimum redundancy, Fisher score, Laplace score, Relief-F algorithm, spectral feature selection, tracking ratio criterion, l2,1-norm minimization based on logical loss, l2,1-norm minimization based on least squares loss, multi-cluster feature selection, robust feature selection, unsupervised discriminant feature selection, correlation-based feature selection, ANOVA F-value selection, Gini index, ANOVA T-value selection, Alpha investment flow feature selection and minimum absolute shrinkage and selection operator.

[0036] In one embodiment, the classifiers include: logistic regression, K-nearest neighbor classifier, support vector machine, naive Bayes, decision tree, extreme random tree, random forest, adaptive boosting classifier, gradient boosting machine, extreme gradient boosting machine, lightweight gradient boosting machine, classification boosting classifier and artificial neural network.

[0037] In a second aspect, an embodiment of the present application provides a gout recurrence prediction system, which is applied to implement the gout recurrence prediction method as described in the first aspect, and the gout recurrence prediction system includes:

[0038] A clinical data acquisition module is used to obtain clinical data of gout comorbidity patients during hospitalization from the electronic health record system of a multi-center data set, wherein the clinical data includes the basic information, medical history, laboratory test results, diagnosis and treatment process, medication use, length of hospitalization, and follow-up data after discharge of the gout comorbidity patients;

[0039] a candidate clinical feature extraction module, configured to process the clinical data according to standardization, min-max normalization, and Yeo-Johnson transformation methods to obtain candidate clinical features, wherein the candidate clinical features are used to predict the recurrence risk of gout comorbidities;

[0040] A clinical feature screening module, configured to screen the extracted candidate clinical features according to a plurality of feature selectors to screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities;

[0041] A candidate model building module, used for building a candidate gout recurrence prediction model based on multiple classifiers;

[0042] a model screening module, configured to screen the candidate gout recurrence prediction models to obtain a gout recurrence prediction model, wherein the gout recurrence prediction model is a model constructed by an iterative interpolation method and gradient enhancement based on minimum-maximum scaling and feature selection;

[0043] Clinical feature acquisition module, used to obtain the patient's clinical features;

[0044] The gout recurrence prediction model is used to predict the patient's gout recurrence risk based on the patient's clinical characteristics and the gout recurrence prediction model.

[0045] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the gout recurrence prediction method described in the first aspect when executing the instructions.

[0046] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, wherein the instructions instruct a device to execute the gout recurrence prediction method as described in the first aspect.

[0047] The gout recurrence prediction method, system, electronic device, and storage medium provided in the embodiments of the present application can effectively address incomplete and skewed distribution problems in clinical data by comprehensively utilizing clinical data from multicenter datasets and various data processing techniques, such as missing value interpolation, standardization, minimum and maximum normalization, and Yeo-Johnson transformation, thereby improving the quality and stability of the data. By employing a variety of feature selectors, classifier combinations, and a collaborative optimization model selection process, the present method can accurately screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities and construct an efficient prediction model, thereby providing reliable auxiliary decision support for clinicians. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of a method for predicting gout recurrence provided in one embodiment of the present application.

[0049] Figure 2 Schematic diagram of evaluating feature importance using SHAP values ​​provided in one embodiment of the present application.

[0050] Figure 3 This is a schematic diagram of a gout recurrence prediction system module provided in one embodiment of the present application.

[0051] Figure 4 A schematic diagram of an electronic device provided in an embodiment of the present application.

[0052] Description of main component symbols

[0053] Gout Relapse Prediction System 10

[0054] Clinical Data Acquisition Module 11

[0055] Candidate clinical feature extraction module 12

[0056] Clinical feature screening module 13

[0057] Candidate model building module 14

[0058] Model screening module 15

[0059] Clinical feature acquisition module 16

[0060] Gout recurrence prediction model17

[0061] Electronic devices 20

[0062] Processor 21

[0063] Memory 22

[0064] Method steps S100-S700 DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0066] It should be noted that, in the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art in the art to which this application relates. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0067] It should be noted that, in the embodiments of the present application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying an order. Features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0068] Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0069] Gout comorbidity refers to a state in which gout patients suffer from one or more other diseases at the same time. Gout is an inflammatory arthritis caused by elevated serum uric acid (SUA) levels, which leads to the deposition of urate in and around the joints. This inflammatory arthritis is characterized by sudden onset of joint pain, swelling, and inflammation, and often recurs. Comorbidity (or multimorbidity) is a damaged life state in which multiple diseases coexist in the same human body, and it is also the general term for these diseases. Epidemiological studies have shown that the prevalence of comorbidities is high and may continue to rise in the future. For gout patients, they may face a higher risk of comorbidities due to long-term hyperuricemia and gout attacks. These comorbidities may include but are not limited to cardiovascular disease, diabetes, kidney disease, etc., and there is a complex interaction between these diseases and gout.

[0070] The management of patients with gout comorbidities is relatively complex because the treatment and care of multiple diseases must be considered simultaneously. However, no predictive models for relapse in patients with gout comorbidities during hospitalization have been reported, which increases the difficulty of managing patients with gout comorbidities. Existing prediction models are mostly targeted at outpatients or patients with specific diseases, and there is a lack of effective prediction tools for gout patients with complex conditions and multiple comorbidities during hospitalization. This makes it challenging for doctors to predict the risk of relapse in patients with gout comorbidities and makes it difficult to develop personalized treatment plans.

[0071] Therefore, the embodiments of the present application provide a gout recurrence prediction method, system, electronic device and storage medium, which can effectively predict the recurrence risk of patients with gout comorbidities during hospitalization. This method comprehensively considers a variety of clinical data such as the patient's basic information, medical history, laboratory test results, and drug use, and uses advanced feature selection and classification algorithms to accurately screen out clinical features that are highly correlated with recurrence risk. Through this method, doctors can identify high-risk patients in a timely manner during hospitalization and take personalized intervention measures to reduce the recurrence rate and improve the patient's treatment effect and prognosis.

[0072] In addition, the gout recurrence prediction system and electronic equipment provided by this application can obtain and process the patient's clinical data in real time in clinical applications, automatically generate prediction results, reduce the workload of medical staff, and improve work efficiency. At the same time, the system supports cross-hospital and multi-center data sharing and verification, ensuring the wide applicability and reliability of the prediction results. Through verification in different hospitals and different populations, the versatility of the model has been further improved, providing a more scientific and accurate treatment plan for patients with gout comorbidities, thereby effectively promoting the development of precision medicine.

[0073] The following describes some embodiments of the application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0074] Figure 1 This is a flow chart of a method for predicting gout recurrence provided in one embodiment of the present application. Figure 1The gout recurrence prediction method shown includes at least the following steps: S100: obtaining clinical data of gout comorbidity patients during hospitalization from the electronic health record system of a multi-center data set; S200: processing the clinical data according to standardization, minimum-maximum normalization and Yeo-Johnson transformation methods to obtain candidate clinical features; S300: applying multiple feature selectors to screen the extracted candidate clinical features to screen out clinical features that are highly correlated with the recurrence risk of gout comorbidity; S400: applying multiple classifiers to construct a gout recurrence prediction candidate model; S500: screening the gout recurrence prediction candidate model to obtain a gout recurrence prediction model; S600: obtaining the patient's clinical characteristics; S700: predicting the patient's gout recurrence risk based on the patient's clinical characteristics and the gout recurrence prediction model.

[0075] S100: Clinical data of patients with gout comorbidities during hospitalization were obtained from the electronic health record system of a multicenter dataset.

[0076] In an embodiment of the present application, a gout recurrence prediction method is applied to the prediction of gout recurrence in patients with gout comorbidity. The gout recurrence prediction method includes, in step S100, obtaining clinical data of patients with gout comorbidity during hospitalization from an electronic health record system of a multi-center data set.

[0077] Specifically, clinical data include basic information, medical history, laboratory test results, diagnosis and treatment process, drug use, length of hospital stay and follow-up data after discharge of patients with gout comorbidity. In step S100, hospitalization data of patients with gout comorbidity are first collected from the electronic health record (EHR) systems of multiple hospitals. These data come from a multi-center data set, which includes medical records from multiple hospitals, and can provide the model with sufficient diverse patient samples to increase the applicability and generalization ability of the model. Clinical data include the patient's basic information (such as age, gender, etc.), medical history (such as whether he has cardiovascular disease, diabetes and other comorbidities), laboratory test results (such as uric acid level, renal function, etc.), diagnosis and treatment process (such as hospitalization, surgical records, etc.), drug use (such as gout treatment drugs, other drugs, etc.), length of hospital stay and follow-up data after the patient is discharged. These data provide the basis for subsequent feature processing, screening, modeling and other steps.

[0078]

[0079]

[0080] Table 1: Clinical data classification statistics

[0081] Please also refer to Table 1, which is a statistical table of clinical data classification provided in the examples of this application. As shown in Table 1, there are a total of 82 variables involved in this application. It is understandable that the above variables are finally included after excluding variables with missing values ​​exceeding 25%.

[0082] Understandably, the core of this step is to obtain multicenter data. This data not only contains basic patient information but also covers comprehensive clinical indicators, including various factors that may affect gout recurrence. The use of multicenter datasets ensures the applicability of the model in different regions and hospital conditions, thereby improving its accuracy and robustness.

[0083] S200: Process the clinical data according to standardization, min-max normalization and Yeo-Johnson transformation methods to obtain candidate clinical features.

[0084] In an embodiment of the present application, the gout recurrence prediction method includes in step S200 processing the clinical data according to standardization, minimum-maximum normalization and Yeo-Johnson transformation methods to obtain candidate clinical features, wherein the candidate clinical features are used to predict the risk of recurrence of gout comorbidities.

[0085] Specifically, in step S200, preprocessing is performed on different types of clinical data. First, for continuous variables, such as the patient's age, weight, laboratory test results, etc., the data is scaled to the same scale through standardization so that the mean of each variable is 0 and the standard deviation is 1, so as to avoid the influence of certain features on model training due to differences in different dimensions. Secondly, for those features with different distributions, the minimum and maximum normalization method (Min-MaxScaling) is used to convert the data to the [0,1] interval to ensure that the numerical range of each feature is consistent. In addition, for features with skewed distributions (such as blood sugar, uric acid, etc.), the Yeo-Johnson transformation is used to transform the data to make it more consistent with the normal distribution, thereby improving the training effect of the model.

[0086] Understandably, clinical data preprocessing is fundamental to ensuring the accuracy of subsequent analysis. These processing methods can eliminate dimensional and distributional differences between features, ensuring that each plays a fair role in model training. This also improves the model's adaptability to skewed data and further enhances its predictive capabilities.

[0087] S300: Apply multiple feature selectors to screen the extracted candidate clinical features to identify clinical features that are highly correlated with the risk of recurrence of gout comorbidities.

[0088] In an embodiment of the present application, the gout recurrence prediction method includes applying multiple feature selectors to screen the extracted candidate clinical features in step S300 to screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities.

[0089] Specifically, in step S300, a variety of feature selectors are applied to screen candidate clinical features. These feature selectors include statistical methods (such as chi-square test, analysis of variance) and model-based methods (such as Lasso regression, decision tree feature importance evaluation, etc.). Through these methods, the correlation between each clinical feature and gout recurrence can be evaluated, and features that contribute more to recurrence prediction can be screened out. For example, features with high correlation or that can significantly distinguish recurrence from non-recurrence will be retained, while redundant or irrelevant features will be eliminated. This can effectively reduce the feature space, reduce computational complexity, and improve the stability and accuracy of the model.

[0090] Please also refer to Figure 2 , Figure 2 Schematic diagram of the SHAP value evaluation feature importance provided by an embodiment of the present application. Figure 2 As shown, length of hospital stay (LOS) was identified as the most influential factor, followed by glucocorticoids (GCs), uric acid (UA), tophi (Tophus), neutrophils (NEUT), basophils percentage (Basophils%), fibrinogen (Fibrinogen), plateletcrit (Plateletcrit), C-reactive protein (CRP), stroke (Stroke), D-dimer (DD), sodium bicarbonate (NaHCO3), prothrombin time (Prothrombin time), lymphocyte percentage (Lymphocyte percentage), systemic immune-inflammatory index (SII), activated partial thromboplastin time (APTT), global immune-inflammatory value (PIV), diuretics (Diuretic), weight change (Weight Changed), and albumin / globulin ratio (AGR). Figure 2 The relationships and interactions between key features are shown.

[0091] It is understandable that feature selection not only helps reduce overfitting but also improves the computational efficiency of the model. By combining multiple feature selectors, the most predictive features can be extracted from large amounts of clinical data, making the final model more accurate and reliable in practical applications.

[0092] S400: Apply multiple classifiers to construct a candidate model for predicting gout recurrence.

[0093] In an embodiment of the present application, the gout recurrence prediction method includes applying multiple classifiers to construct a gout recurrence prediction candidate gout recurrence prediction model in step S400.

[0094] Specifically, in step S400, a group of appropriate classifiers, such as logistic regression, support vector machine (SVM), random forest, and gradient boosting, are selected to train the processed and filtered clinical features. These classifiers use different algorithms to learn and train the data to construct multiple candidate models that capture the advantages of different features and classifiers. These models are trained on the same dataset to generate multiple candidate recurrence prediction models, providing multiple alternatives for subsequent model screening and optimization.

[0095] It is understandable that the use of multiple classifiers can improve the robustness of the model. Different classifiers may have different advantages and disadvantages when processing data. By combining multiple algorithms, it is possible to mine information from different perspectives in the data, thereby improving prediction accuracy.

[0096] S500: Screening candidate models for predicting gout recurrence to obtain a gout recurrence prediction model.

[0097] In the embodiment of the present application, the gout recurrence prediction method includes screening the gout recurrence prediction candidate model in step S500 to obtain a gout recurrence prediction model, which is a model constructed by the iterative interpolation method and gradient enhancement based on minimum and maximum scaling and feature selection.

[0098] Specifically, in step S500, multiple candidate models obtained through training are compared in the screening phase, and the optimal recurrence prediction model is finally selected. The model selection criteria include: AUC value (area under the curve), accuracy, sensitivity, specificity, F1 score and other evaluation indicators. By combining these indicators, the model with the best performance is screened out. The final recurrence prediction model is constructed based on minimum and maximum scaling, iterative interpolation method of feature selection and gradient enhancement to ensure that the prediction effect of the model has good stability and accuracy on different data sets.

[0099] Understandably, this screening process is crucial for model optimization. By comprehensively evaluating the performance of each candidate model, we can select the model that performs best in different situations, ensuring high predictive accuracy and stability in practical applications.

[0100] S600: Obtain clinical characteristics of the patient.

[0101] In the embodiments of the present application, the gout recurrence prediction method comprises obtaining the clinical characteristics of the patient in step S600.

[0102] Specifically, in step S600, the clinical characteristics of the patient to be predicted are obtained. These characteristics include the basic information of the patient (such as age, gender), medical history (such as whether there is hyperglycemia, cardiovascular disease, etc.), current laboratory examination results (such as uric acid level, renal function indicators, etc.), drug use (such as whether long-term use of uric acid-lowering drugs, etc.), and treatment regimen and clinical course during hospitalization, etc. Through these characteristics, the necessary input information for the prediction model can be provided.

[0103] It can be understood that obtaining the clinical characteristics of the patient is the premise of the prediction analysis, which ensures that the gout recurrence prediction method can accurately predict the recurrence according to the real-time patient information. These characteristics are the input of the prediction model, which affects the accuracy of the model output.

[0104] S700: predicting the risk of gout recurrence of the patient according to the clinical characteristics of the patient and the gout recurrence prediction model.

[0105] In the embodiments of the present application, the gout recurrence prediction method comprises predicting the risk of gout recurrence of the patient according to the clinical characteristics of the patient and the gout recurrence prediction model in step S700.

[0106] Specifically, in step S700, the patient's clinical characteristics obtained in the previous step are input into the gout recurrence prediction model constructed in the previous step. The model predicts according to the input characteristics and obtains the probability score of recurrence. According to the probability output by the model, it can be determined whether the patient has the risk of recurrence, and decision support is provided for doctors to help them develop more appropriate treatment plans.

[0107] It can be understood that this step provides a quantitative recurrence risk prediction by combining the clinical characteristics of the patient with the prediction model. Doctors can judge the trend of the patient's condition according to the prediction result and make timely intervention, thereby improving the prognosis of the patient.

[0108] In the embodiments of the present application, the gout recurrence prediction method further comprises obtaining variables in the clinical data. Variables with missing values higher than a threshold are excluded to obtain candidate clinical characteristics. The missing values in the candidate clinical characteristics are imputed according to the iterative imputation method, the mean imputation method, the median imputation method and the KNN imputation method. The candidate clinical characteristics are subjected to collinearity transformation according to standardization, minimum-maximum normalization and Yeo-Johnson transformation to improve the stability of the gout recurrence prediction model.

[0109] Specifically, in the embodiments of the present application, the implementation of the gout recurrence prediction method also includes extracting a plurality of variables from the clinical data, which include the basic information of the patient, clinical symptoms, disease history, drug use, laboratory examination results, etc. In order to ensure the integrity and effectiveness of the data, first, the missing values will be processed, and the variables with missing value proportion exceeding the set threshold will be excluded. Subsequently, an interpolation method (such as iterative interpolation, mean interpolation, median interpolation, and KNN interpolation) is used to fill in these missing values, ensuring that each feature variable can provide sufficient information for subsequent analysis. For the processed candidate features, collinearity conversion will also be performed to reduce the problem of multiple collinearity between different variables, thereby improving the stability and accuracy of the model prediction. Standardization, min-max normalization, and Yeo-Johnson transformation methods are used to balance the scale difference between variables and handle the skew distribution of the data, ensuring the effectiveness of the model training.

[0110]

[0111]

[0112]

[0113] Table 2: Variable linearization representation results

[0114] Please refer to Table 2, which is the variable linearization representation results provided by the embodiments of the present application. O represents the features retained after solving the collinearity.

[0115] It can be understood that by using the above interpolation method and data conversion means, a candidate feature set with high quality and stability can be generated. These features are used for subsequent model training to ensure that the prediction model constructed can accurately reflect the risk of gout comorbidity patient recurrence and can effectively deal with the missing data and collinearity problems in the data.

[0116] In the embodiments of the present application, the gout recurrence prediction candidate model is screened to obtain the gout recurrence prediction model, which includes obtaining an internal training set and an internal validation set. The area under the receiver operating characteristic curve of the gout recurrence prediction candidate model is obtained according to the internal training set and the internal validation set. The gout recurrence prediction candidate model is preliminarily screened according to the area under the receiver operating characteristic curve to obtain a preliminary screening result. The preliminary screening result is secondary screened according to the DeLong test to obtain a secondary screening result. An external validation set and a prospective validation set are obtained. The gout recurrence prediction candidate model in the secondary screening result is verified according to the external validation set and the prospective validation set to obtain the gout recurrence prediction model.

[0117] Specifically, in an embodiment of the present application, the gout recurrence prediction method further involves screening out the best prediction model. First, the most suitable model is selected from a plurality of candidate models. This process includes extracting the area under the receiver operating characteristic curve (ROC) (AUC) from the internal training set and the internal validation set, evaluating the classification effect of the model by AUC, and performing a preliminary screening. Then, the screening results are statistically tested using the DeLong test to further confirm the validity of the model. On this basis, an external validation set and a prospective validation set are also obtained to verify the candidate models that have undergone secondary screening to ultimately determine the best prediction model.

[0118] It is understandable that the core purpose of this step is to ensure that the selected prediction model has wide applicability and high generalization ability through evaluation of different datasets (including internal and external validation sets), thereby improving the accuracy of gout recurrence prediction.

[0119] In an embodiment of the present application, the gout recurrence prediction method further includes evaluating the linear relationship between candidate clinical features based on the Pearson correlation coefficient and the variance inflation factor, and excluding candidate clinical features whose variance inflation factor or Pearson correlation coefficient does not meet the requirements.

[0120] Specifically, the present application also involves optimizing model training by calculating the correlation between candidate clinical features. The linear relationship between candidate features is evaluated using the Pearson correlation coefficient (PCC) and variance inflation factor (VIF), and features that do not meet the requirements are excluded. If the correlation coefficient of some candidate features is too high (such as PCC>0.8) or the VIF value is too large (such as VIF>10), it means that these features have multicollinearity, which may cause model instability, so these features need to be excluded from the analysis.

[0121] It can be understood that this feature screening method can eliminate the negative impact of multicollinearity on model training, ensure that the selected features have stronger independence and predictive ability, and thus improve the prediction effect and stability of the model.

[0122] In one embodiment, the risk of gout recurrence is predicted based on the patient's clinical characteristics and a gout recurrence prediction model, further comprising obtaining prediction scores for multiple instances of the gout recurrence prediction model. An optimal classification threshold is calculated based on the Youden Index and the prediction score. The prediction score is compared with the optimal classification threshold. If the prediction score is equal to or greater than the optimal threshold, the instance is classified as positive. If the prediction score is less than the optimal threshold, the instance is classified as negative. A classification decision result is generated based on the instance classification.

[0123] Specifically, in some embodiments, based on the patient's clinical characteristics and the gout recurrence prediction model, the system will calculate the prediction scores of multiple instances. The optimal classification threshold is calculated based on the Youden's index, and the prediction score is compared with the optimal threshold. If the prediction score is greater than or equal to the optimal threshold, the system classifies the instance as positive; otherwise, it is classified as negative. In this way, the system can generate specific classification decision results.

[0124] It is understandable that by comparing the prediction score with the optimal threshold, it is possible to ensure that the classification decision maximizes the balance between the true positive rate and the true negative rate, thereby improving the clinical application value of the model and enabling doctors to more accurately judge the recurrence risk of patients with gout comorbidities and formulate personalized treatment plans accordingly.

[0125] In one embodiment, the feature selector includes: conditional information maximization feature extraction, conditional mutual information maximization, dual-input symmetric correlation, interaction upper limit, joint mutual information, mutual information-based feature selection, mask image modeling, maximum correlation minimum redundancy, Fisher score, Laplace score, Relief-F algorithm, spectral feature selection, tracking ratio criterion, l2,1-norm minimization based on logical loss, l2,1-norm minimization based on least squares loss, multi-cluster feature selection, robust feature selection, unsupervised discriminant feature selection, correlation-based feature selection, ANOVAF value selection, Gini index, ANOVAT value selection, Alpha investment flow feature selection and minimum absolute shrinkage and selection operator.

[0126] Specifically, in the embodiments of the present application, the feature selector includes a variety of information theory methods, such as conditional information maximization, conditional mutual information maximization, maximum correlation and minimum redundancy, etc., which can identify the most predictive features. In addition, methods based on statistical analysis (such as Fisher score, ANOVAF value selection, etc.) and machine learning (such as Relief-F algorithm, l2,1-norm minimization based on least squares loss, etc.) are also used to enhance the effect of feature selection.

[0127] It is understandable that through the combined use of multiple feature selection methods, features that are highly correlated with the risk of recurrence of gout comorbidities can be effectively extracted, the predictive performance and explanatory ability of the model can be improved, and the final model can be ensured to accurately reflect the impact of clinical characteristics on the risk of recurrence.

[0128] In one embodiment, the classifiers include: logistic regression, K-nearest neighbor classifier, support vector machine, naive Bayes, decision tree, extreme random tree, random forest, adaptive boosting classifier, gradient boosting machine, extreme gradient boosting machine, lightweight gradient boosting machine, classification boosting classifier and artificial neural network.

[0129] Specifically, in the embodiments of the present application, the classifier includes common machine learning algorithms, such as logistic regression, K-nearest neighbor classifier, support vector machine (SVM), naive Bayes, decision tree, random forest, etc. In addition, a variety of reinforcement learning methods (such as gradient boosting machine, extreme gradient boosting machine, lightweight gradient boosting machine, etc.) and artificial neural networks are also used to improve the prediction accuracy of the model.

[0130] It can be understood that by using different types of classifiers, the recurrence risk of patients can be evaluated from multiple perspectives, further improving the robustness and generalization ability of the model, thereby ensuring the effectiveness of the model in clinical applications.

[0131] The following is an exemplary embodiment to describe the process of constructing a gout recurrence prediction model in the gout recurrence prediction method provided in this application.

[0132] This example aims to build an accurate gout recurrence prediction model by integrating multiple data processing methods (interpolation, feature transformation, feature selection, and classifiers). The model uses a multicenter dataset from Nanfang Hospital, Ganzhou People's Hospital, Southern Medical University Hospital of Integrated Traditional Chinese and Western Medicine, Taishan People's Hospital, and Dongguan Hospital of Traditional Chinese Medicine. Through combined feature selection and classifier training, a recurrence prediction model with high generalization ability was ultimately constructed.

[0133] First, clinical data from hospitalized patients with gout and comorbidities were obtained from the electronic health record system, including patient information, medical history, laboratory test results, medication use, length of stay, and follow-up data. Continuous variables were standardized, min-max normalized, and Yeo-Johnson transformed to address skewed distributions and ensure data quality.

[0134] Subsequently, 24 feature selectors were used to screen candidate clinical features. After all features were screened, features with strong collinearity (PCC > 0.8 or VIF > 10) were removed to reduce the impact of multicollinearity on the model.

[0135] Next, missing values ​​were imputed using four imputation methods (Iterative Imputer, Mean Imputer, Median Imputer, and KNN Imputer). The data was further processed by combining three feature transformation methods (Min-Max Scaling, Standardization, and Yeo-Johnson Transformation). A total of 3,744 models were constructed using a combination of 13 classifiers (such as Logistic Regression, KNN, and Support Vector Machine) and 24 feature selectors.

[0136] Next, after screening the internal validation set, overfit models (models with an AUC difference greater than 10%) were removed, and 1,664 models were screened. Based on the AUC value and DeLong test results, the top 20 models were further screened, and then 5 models were screened for the final screening. Ultimately, the IterImp_MM_FS_GB model performed best on all datasets, especially on the external dataset and prospective dataset, with AUC values ​​exceeding 0.74, and high accuracy and stability in predicting recurrence. Therefore, the model constructed by the iterative interpolation method based on minimum and maximum scaling and feature selection and gradient boosting was selected as the best model.

[0137]

[0138]

[0139]

[0140] Table 3: Parameters of the top five models

[0141] Please also refer to Table 3, which shows the parameters of the top five models. As shown in Table 3, the model built with iterative interpolation based on min-max scaling and feature selection and gradient boosting performs best.

[0142] Ultimately, on the training dataset, the model constructed using iterative interpolation with min-max scaling and feature selection and gradient boosting achieved an AUC of 0.832, an accuracy of 0.745, an NPV of 0.876, and an F1 score of 0.630. On the external validation set, the model achieved an AUC of 0.742, an accuracy of 0.681, an NPV of 0.867, and an F1 score of 0.588. On the prospective dataset, the model achieved an AUC of 0.744, an accuracy of 0.749, an NPV of 0.917, and an F1 score of 0.383. The model combining iterative interpolation with gradient boosting demonstrated excellent discriminative power and high stability.

[0143] Figure 3 This is a gout recurrence prediction system provided by an embodiment of the present application. Figure 3 The gout recurrence prediction system 1O shown includes at least the following parts: a clinical data acquisition module 11, a candidate clinical feature extraction module 12, a clinical feature screening module 13, a candidate model construction module 14, a model screening module 15, a clinical feature acquisition module 16 and a gout recurrence prediction model 17.

[0144] In the embodiment of the present application, the clinical data acquisition module 11 is used to obtain clinical data of gout comorbidity patients during hospitalization from the electronic health record system of the multi-center data set. The clinical data includes the basic information, medical history, laboratory test results, diagnosis and treatment process, drug use, length of hospitalization and follow-up data after discharge of the gout comorbidity patients. For specific acquisition methods, please refer to Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0145] In the embodiment of the present application, the candidate clinical feature extraction module 12 is used to process the clinical data according to the standardization, minimum maximum normalization and Yeo-Johnson transformation method to obtain candidate clinical features, which are used to predict the recurrence risk of gout comorbidity. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0146] In the embodiment of the present application, the clinical feature screening module 13 is used to screen the extracted candidate clinical features according to a variety of feature selectors to screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0147] In the embodiment of the present application, the candidate model construction module 14 is used to construct a candidate gout recurrence prediction model based on multiple classifiers. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0148] In the embodiment of the present application, the model screening module 15 is used to screen the candidate models for predicting gout recurrence to obtain a gout recurrence prediction model. The gout recurrence prediction model is a model constructed by an iterative interpolation method and gradient enhancement based on minimum and maximum scaling and feature selection. For specific screening methods, please refer to Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0149] In the embodiment of the present application, the clinical characteristics acquisition module 16 is used to obtain the clinical characteristics of the patient. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0150] In the embodiment of the present application, the gout recurrence prediction model 17 is used to predict the patient's gout recurrence risk based on the patient's clinical characteristics and the gout recurrence prediction model. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0151] The gout recurrence prediction system 10 provided in the embodiment of the present application ensures the good generalization ability of the prediction model on different data sets through multiple rounds of verification and tuning of the model, including the use of internal training sets, validation sets, external validation sets and prospective validation sets. By combining the Youden index to optimize the classification threshold, it is possible to accurately distinguish between high and low recurrence risks, provide more accurate recurrence predictions, and help doctors develop personalized treatment plans. Ultimately, this system can provide a scientific, accurate, and clinically feasible solution for predicting the recurrence risk of patients with gout comorbidities, significantly improving clinical management and patient prognosis.

[0152] Figure 4 This is an electronic device 20 provided by an embodiment of the present application. Figure 4 As shown, the electronic device 20 includes at least the following parts: a processor 21 and a memory 22.

[0153] In the embodiment of the present application, the memory 22 is used to store instructions executable by the processor 21. The processor 21 is configured to execute the instructions to implement the following Figure 1 The method shown is a method for predicting gout recurrence.

[0154] In an embodiment of the present application, a computer-readable storage medium includes instructions, and the instructions instruct a device to execute the method of the first aspect. For example, the instructions instruct the device to execute Figure 1 The gout recurrence prediction method shown in steps S100 to S700.

[0155] The program running in the electronic device 20 involved in one embodiment of the present application can be a program that controls a central processing unit (CPU) and the like to implement the functions of the above-mentioned embodiment involved in one embodiment of the present invention (a program that causes a computer to function). Then, the information processed by these devices is temporarily stored in a random access memory (RAM) during processing, and then stored in various ROMs such as read-only memory (Flash ROM) and a hard disk drive (HDD), and is read, modified, and written by the CPU as needed.

[0156] It should be noted that a portion of the electronic device 20 of the above embodiment may also be implemented by a computer. In this case, a program for implementing the control function may be recorded on a computer-readable recording medium and implemented by reading the program recorded on the recording medium into a computer system and executing it.

[0157] It should be noted that the "computer system" mentioned here refers to a computer system built into the electronic device 20, and is a computer system comprising hardware such as an operating system and peripheral devices. Furthermore, "computer-readable recording medium" refers to removable media such as floppy disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into the computer system.

[0158] Furthermore, "computer-readable recording media" may include: media that dynamically store programs for a short period of time, such as communication lines when transmitting programs via networks such as the Internet or communication lines such as telephone lines; and media that store programs for a fixed period of time, such as volatile memory within computer systems serving as servers or clients in this context. Furthermore, the aforementioned program may be a program for implementing a portion of the aforementioned functions, or a program that can achieve the aforementioned functions by combining with programs already stored in a computer system.

[0159] Furthermore, the electronic device 20 in the above-described embodiment can also be implemented as a collection (device group) composed of multiple devices. Each device constituting the device group may have a portion or all of the functions or functional blocks of the electronic device 20 in the above-described embodiment. A device group only needs to have all the functions or functional blocks of the electronic device 20.

[0160] It is understandable that the gout recurrence prediction method, system 10, electronic device 20 and storage medium provided in the embodiments of the present application can effectively handle the incomplete and skewed distribution problems in clinical data by comprehensively using clinical data from multi-center data sets and various data processing techniques, such as missing value interpolation, standardization, minimum and maximum normalization, Yeo-Johnson transformation, etc., thereby improving the quality and stability of the data. By adopting a variety of feature selectors, classifier combinations and collaborative optimization model selection processes, this method can accurately screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities and construct an efficient prediction model, thereby providing reliable auxiliary decision support for clinicians.

[0161] In addition, the embodiment of the present application also ensures the good generalization ability of the prediction model on different data sets through multiple rounds of verification and tuning of the model, including the use of internal training sets, validation sets, external validation sets and prospective validation sets. By combining the Youden index to optimize the classification threshold, this method can accurately distinguish between high and low recurrence risks, provide more accurate recurrence predictions, and help doctors develop personalized treatment plans. Ultimately, this system can provide a scientific, accurate, and clinically feasible solution for predicting the recurrence risk of patients with gout comorbidities, significantly improving clinical management and patient prognosis.

[0162] Those skilled in the art should recognize that the above embodiments are merely intended to illustrate the present application and are not intended to limit the present application. As long as they are within the spirit of the present application, appropriate changes and modifications to the above embodiments are within the scope of protection claimed in the present application.

Claims

1. A gout recurrence prediction method, applied to gout recurrence prediction in patients with gout comorbidities during hospitalization, characterized in that: The gout recurrence prediction method comprises: Obtain clinical data of gout comorbidity patients during hospitalization from the electronic health record system of a multicenter data set, wherein the clinical data include basic information, medical history, laboratory test results, diagnosis and treatment process, medication use, length of hospitalization, and follow-up data after discharge of the gout comorbidity patients; obtaining variables in the clinical data; The clinical data are processed according to standardization, minimum-maximum normalization, and Yeo-Johnson transformation methods, and variables with missing values ​​higher than a threshold are excluded to obtain candidate clinical features, wherein the candidate clinical features are used to predict the recurrence risk of gout comorbidities; Imputing missing values ​​in the candidate clinical characteristics according to iterative interpolation, mean interpolation, median interpolation, and KNN interpolation methods; performing collinearity transformation on the candidate clinical features according to standardization, min-max normalization, and Yeo-Johnson transformation to improve the stability of the gout recurrence prediction model; Applying multiple feature selectors to screen the extracted candidate clinical features to screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities; Multiple classifiers were used to construct candidate gout recurrence prediction models; Screen the gout recurrence prediction candidate model to obtain a gout recurrence prediction model, obtain an internal training set and an internal validation set; obtain the area under the receiver operating characteristic curve of the gout recurrence prediction candidate model based on the internal training set and the internal validation set; perform a primary screening of the gout recurrence prediction candidate model based on the area under the receiver operating characteristic curve to obtain a primary screening result; perform a secondary screening of the primary screening result based on the DeLong test to obtain a secondary screening result; obtain an external validation set and a prospective validation set; verify the gout recurrence prediction candidate model in the secondary screening result based on the external validation set and the prospective validation set to obtain the gout recurrence prediction model; the gout recurrence prediction model is a model constructed according to the iterative interpolation method and gradient enhancement of minimum and maximum scaling and feature selection; The clinical characteristics of the patients were obtained, including: length of hospital stay, glucocorticoids, uric acid, tophi, neutrophil and basophil percentages, fibrinogen, platelet count, C-reactive protein, stroke, D-dimer, sodium bicarbonate, prothrombin time, lymphocyte percentage, systemic immune and inflammatory index, activated partial thromboplastin time, global immune and inflammatory value, diuretics, weight change, and albumin to globulin ratio; The patient's gout recurrence risk is predicted based on the patient's clinical characteristics and the gout recurrence prediction model.

2. The method for predicting gout recurrence according to claim 1, wherein The gout recurrence prediction method further comprises: The linear relationship between the candidate clinical features was evaluated based on the Pearson correlation coefficient and the variance inflation factor; The candidate clinical features whose variance inflation factor or Pearson correlation coefficient does not meet the requirements are excluded.

3. The method for predicting gout recurrence according to claim 1, wherein: The method of predicting the patient's gout recurrence risk based on the patient's clinical characteristics and the gout recurrence prediction model further includes: Obtaining prediction scores of the gout recurrence prediction model for multiple instances; Calculating the optimal classification threshold based on the Youden index and the prediction score; comparing the prediction score to the optimal classification threshold; If the prediction score is equal to or greater than the optimal threshold, the instance is classified as positive; If the prediction score is less than the optimal threshold, the instance is classified as negative; Generate classification decision results based on instance classification.

4. The method for predicting gout recurrence according to any one of claims 1 to 3, characterized in that: The feature selector includes: conditional information maximization feature extraction, conditional mutual information maximization, dual-input symmetric correlation, interaction upper limit, joint mutual information, feature selection based on mutual information, mask image modeling, maximum correlation minimum redundancy, Fisher score, Laplace score, Relief-F algorithm, spectral feature selection, tracking ratio criterion, l2,1-norm minimization based on logical loss, l2,1-norm minimization based on least squares loss, multi-cluster feature selection, robust feature selection, unsupervised discriminant feature selection, correlation-based feature selection, ANOVA F-value selection, Gini index, ANOVA T-value selection, Alpha investment flow feature selection and minimum absolute shrinkage and selection operator.

5. The method for predicting gout recurrence according to any one of claims 1 to 3, characterized in that: The classifiers include: logistic regression, K-nearest neighbor classifier, support vector machine, naive Bayes, decision tree, extreme random tree, random forest, adaptive boosting classifier, gradient boosting machine, extreme gradient boosting machine, lightweight gradient boosting machine, classification boosting classifier and artificial neural network.

6. A gout recurrence prediction system, used to implement the gout recurrence prediction method according to any one of claims 1 to 5, characterized in that: The gout recurrence prediction system comprises: A clinical data acquisition module is used to obtain clinical data of gout comorbidity patients during hospitalization from the electronic health record system of a multi-center data set, wherein the clinical data includes the basic information, medical history, laboratory test results, diagnosis and treatment process, medication use, length of hospitalization, and follow-up data after discharge of the gout comorbidity patients; a candidate clinical feature extraction module, configured to process the clinical data according to standardization, min-max normalization, and Yeo-Johnson transformation methods to obtain candidate clinical features, wherein the candidate clinical features are used to predict the recurrence risk of gout comorbidities; A clinical feature screening module, configured to screen the extracted candidate clinical features according to a plurality of feature selectors to screen out clinical features that are highly correlated with the risk of recurrence of gout comorbidities; A candidate model building module, used for building a candidate gout recurrence prediction model based on multiple classifiers; a model screening module, configured to screen the candidate gout recurrence prediction models to obtain a gout recurrence prediction model, wherein the gout recurrence prediction model is a model constructed by an iterative interpolation method and gradient enhancement based on minimum-maximum scaling and feature selection; Clinical feature acquisition module, used to obtain the patient's clinical features; The gout recurrence prediction model is used to predict the patient's gout recurrence risk based on the patient's clinical characteristics and the gout recurrence prediction model.

7. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the gout recurrence prediction method according to any one of claims 1 to 5 when executing the instructions.

8. A computer-readable storage medium, characterized in that The method comprises instructions for instructing a device to execute the gout recurrence prediction method according to any one of claims 1 to 5.

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