68 results about "Recurrence prediction" patented technology

The invention provides a prostate cancer biochemical recurrence prediction system based on a deep learning image and clinical information multi-mode model. The prostate cancer biochemical recurrence prediction system comprises an input module, a preprocessing module, a feature selection module, a fusion module, a prediction module, a data management and storage module and a result output and decision support module. According to the method, bpMRI image data and clinical information of a patient are fully utilized, multi-mode information is fully fused through a deep learning technology, and accurate prediction of biochemical recurrence is achieved. And the robustness of the model is further enhanced by adopting 5-fold cross validation and a diversified data enhancement strategy. Experimental results show that the method can assist in making more accurate patient layering and personalized follow-up visit schemes, and potentially reduces the over-treatment and missed diagnosis risks. The invention provides an efficient and reliable AI solution for prostatic cancer prognosis evaluation, and provides powerful support for promoting precise medical treatment and making personalized treatment strategies.

The invention discloses a vocal cord problem recognition and feedback system for the ophthalmology and otorhinolaryngology department. The vocal cord problem recognition and feedback system comprises a sound collection module, an image collection module, a biological feedback module, a data processing module, an AI diagnosis module and a rehabilitation guidance module. The sound acquisition module comprises a microphone array and a self-adaptive noise reduction unit; the image acquisition module is provided with an endoscope camera and an image enhancement processor; the biological feedback module integrates a laryngeal myoelectricity sensor and a three-dimensional motion simulator; the data processing module executes multi-modal feature extraction and fusion; through mutual cooperation of the sound acquisition module, the image acquisition module, the biological feedback module and the data processing module, data can be accurately acquired, the early canceration detection rate is improved and the misdiagnosis rate is reduced through multi-modal fusion, the data acquired by the multi-modal structure is analyzed through the data processing module, the model is combined with a weekly updated disease map, and the early canceration detection rate is improved. And the recurrence prediction accuracy is improved.

The invention provides an atrial fibrillation postoperative recurrence prediction method fusing electrocardiosignals and clinical characteristics. The method comprises the following steps: acquiring data of a patient before an ablation operation, and carrying out resampling, denoising and normalization preprocessing and data segment segmentation on an electrocardiosignal; extracting spatio-temporal features by using a deep network containing a residual convolutional block and a long and short term memory module; screening high-discrimination clinical baseline features through statistical analysis and a machine learning model; extracting time-frequency domain and nonlinear features of short-time heart rate variability; designing a cross-modal attention fusion module to carry out feature adaptive weighted fusion; and outputting a recurrence probability through a multi-layer perceptron based on the fusion features. The method improves the prediction precision through feature complementarity, facilitates the recognition of high-recurrence-risk patients, is suitable for sinus heart rhythm signals or atrial flutter and atrial fibrillation signals, and has a certain application value in the field of cardiovascular precision medical treatment. The method can be popularized to all prediction researches based on the electrophysiological signals.

The invention discloses a rainfall intensity rapid identification method and system based on multi-sensor data fusion, and relates to the technical field of hydro-meteorological monitoring and urban drainage scheduling, and the method comprises the steps: obtaining a historical rainfall data set, drawing a peak curve, and extracting an evolution sequence feature before a peak value; correlation analysis is carried out on the data and complete rainfall process characteristics to establish a historical rule model; rainfall intensity data are collected in real time through multiple sensors, and space-time dimension features are fused; matching the fusion features with historical features, and screening target historical events; constructing a rainfall reproduction prediction model to reconstruct a current rainfall evolution process; and finally, calculating an inflow load based on a reconstruction result, and generating a dynamic drainage scheduling scheme. According to the invention, accurate prediction of the rainfall process and intelligent scheduling of the drainage system are realized, and the urban waterlogging prevention capability is improved.

The invention discloses a lumbar disc herniation postoperative recurrence prediction system based on a multi-modal medical image, and belongs to the field of medical images. The lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is trained by using the lumbar vertebra image set, the feature true value corresponding to each image and the recurrence prediction label corresponding to each patient, and in the training process, model parameters are updated by using a stochastic gradient descent algorithm; and a trained lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is obtained. According to the method and the system, due to rich image features provided by the multi-modal image, the prediction network trained based on the multi-modal data shows higher accuracy in postoperative recurrence prediction of the lumbar disc herniation.

The invention provides a prostate cancer biochemical recurrence prediction system based on pathological sections and a construction method. Based on a high-definition panoramic pathological section scanning image of a pathological section after a radical operation, a multi-instance algorithm based on a cyclic cross attention module and a pseudo-packet strategy is adopted, deep features of tumor images with different objective lens multiples are extracted firstly, and then feature representation of a panoramic pathological section scanning image level is generated in a weak supervision network; postoperative biochemical recurrence risk prediction results under different multiples are obtained; and then integrating model results under different scales, and predicting whether the patient finally has biochemical recurrence or not through multi-center verification. According to the system, multi-center, multi-slice and multi-scale panoramic pathological section scanning images are creatively used for training and verification, the risk of prostate cancer recurrence of a patient is predicted through pathological sections after radical treatment, the risk of recurrence of the patient within 3 years and longer time after the radical treatment is accurately predicted, and more personalized treatment is achieved.

The invention relates to the technical field of medical health data management, in particular to a method for constructing a cross-institution atrial fibrillation recurrence prediction model based on federal learning. According to the method, a hierarchical federated framework is established, each mechanism node locally performs standardized preprocessing on multi-source heterogeneous data, a multi-task basic model fusing an atrial fibrillation recurrence prediction main task and a data quality evaluation auxiliary task is constructed and distributed, a federated distillation mechanism is adopted, and local training is performed in combination with an encrypted confrontation sample. Minimizing teacher-student model prediction difference and calculating parameter update quantity, dynamically calculating aggregation weight based on data quality score and effective sample quantity, updating a global model by adopting weighted average, and stopping iteration when convergence conditions are met by synchronously monitoring three indexes of global loss value, prediction accuracy stability and parameter consistency. And finally, a self-adaptive cross-mechanism prediction model is generated, so that the robustness and prediction accuracy of the model under multi-center heterogeneous data are improved.

The invention relates to the technical field of biological medicine, in particular to an ulcerative colitis disease clearance evaluation molecular marker and application thereof. By detecting the expression level of the HMGCS2 gene in a sample, the ulcerative colitis disease clearance state and disease activity period can be evaluated, and the method is used for curative effect monitoring, recurrence prediction and individualized treatment strategy formulation of ulcerative colitis and has important clinical application value and market prospect.

The invention discloses an ERCP postoperative common bile duct calculus recurrence prediction system based on machine learning, and the system comprises the following steps: a data collection module which is used for forming an original clinical data set; the data preprocessing module is used for constructing a standardized preprocessed clinical data set; the feature construction and hyper-parameter search space definition module is used for defining a hyper-parameter search space of the improved bidirectional gating loop unit network model; the improved bidirectional gating circulation unit network model construction module is used for outputting a recurrence risk prediction value; the grey wolf optimization hyper-parameter tuning module is used for finally generating an optimized improved bidirectional gating circulation unit network model; the model training and prediction module is used for constructing a risk classification rule according to the prediction probability and the postoperative key indexes; and the individualized result output module is used for outputting the prediction result and pushing the prediction result to a doctor end. The method has significant clinical value in early recognition of high-risk individuals in actual deployment.

The application discloses a kind of based on multi-modal medical image's lumbar disc herniation postoperative recurrence prediction system, belong to medical image field.Use lumbar image set and each image corresponding feature true value and each patient corresponding recurrence prediction label to carry out training to based on multi-modal image's lumbar disc herniation postoperative recurrence prediction model, in training process, using random gradient descent algorithm to update model parameter, obtain the trained based on multi-modal image's lumbar disc herniation postoperative recurrence prediction model.Through the application, benefit from the rich image features provided by multi-modal image, the prediction network trained based on multi-modal data shows higher accuracy in lumbar disc herniation postoperative recurrence prediction.

The invention relates to an atrial fibrillation radiofrequency ablation postoperative recurrence prediction method based on multi-modal analysis. Comprising the following steps: dynamically capturing the form and spatial distribution of a low-voltage region by using a three-dimensional electroanatomical map; introducing a regional complexity quantitative index, and analyzing the non-linear degree of the boundary of the low-voltage region; grading the voltage intensity gradient in the low-voltage region, and quantifying the sensitivity to postoperative recurrence; establishing a composite index model of the area ratio of the low-voltage region and the fragmentation potential intensity; based on spatial distribution and voltage features, performing high, medium and low recurrence risk classification on the low-voltage region, and outputting a classification heat map; analyzing the migration path of the fragmentation potential in the three-dimensional space in the operation, and extracting the migration speed, direction and frequency change; discovering a fragmentation potential concentration trend of the high recurrence sensitive area by using a trajectory clustering algorithm; and extracting spectrum characteristics of the fragmentation potential signal based on wavelet transform, and identifying abnormal distribution of the high-frequency fragmentation potential.

The invention relates to a depression disease management system based on targeted metabonomics and a machine learning model, and the system is characterized in that the system comprises an intelligent diagnosis model module, a differential diagnosis model module, a grading diagnosis model module, an accompanying diagnosis model module, a treatment endpoint outcome prediction model module, a recurrence prediction model module, and a comprehensive judgment module. Through the system provided by the invention, the problems in the fields of current clinical depression diagnosis, treatment outcome, relapse and the like can be solved, accurate diagnosis and timely treatment are realized, and the mental health level of the whole people is improved.

This invention relates to an intelligent decision support system for rectal cancer prognosis integrating digital pathological images, belonging to the field of medical image processing technology. The system includes a data acquisition module for collecting multiple sets of sample data; a feature selection module for identifying multiple key medical imaging features of rectal cancer based on the multiple sets of sample data; a model building module for acquiring a pre-trained deep learning model and constructing a rectal cancer prognosis prediction model based on transfer learning, the pre-trained deep learning model, the multiple sets of sample data, and the multiple key medical imaging features of rectal cancer; an image acquisition module for acquiring pre- and post-operative medical images of the rectal cancer patient to be evaluated; and a recurrence prediction module for predicting the recurrence probability of the rectal cancer patient to be evaluated based on the pre- and post-operative medical images of the patient using the rectal cancer prognosis prediction model. This system has the advantage of improving the accuracy of non-invasive prediction of postoperative recurrence of rectal cancer.

The invention discloses an electric quantity prediction method, device and equipment based on model collaboration and a storage medium thereof, and relates to the technical field of power grid management, and the method comprises the steps: obtaining the electric quantity data of a power grid; based on double-attention modeling and multi-scale fusion of a preset multi-scale deep learning model, extracting a numerical feature vector from the electric quantity data; performing conversion text alignment processing on the numerical feature vector to obtain an aligned semantic feature; and inputting the aligned semantic features into a preset large language model, and predicting the electric quantity of the power grid based on an autoregression generation mode of the large language model. Namely, through multi-scale numerical value feature extraction, cross-modal semantic alignment and autoregression time sequence generation, the understanding capability of the large language model on the numerical value time sequence is enhanced, and meanwhile, the precision and efficiency bottlenecks brought by non-autoregression prediction are overcome, so that the dual requirements of power grid electric quantity prediction on high accuracy and high real-time performance are met.

This application belongs to the field of medical data processing technology and discloses a method for predicting stroke recurrence based on graph neural networks and missing feature prediction. The method first preprocesses collected patient clinical data containing missing values, trains a prediction model using non-missing features to fill in missing feature values, and obtains complete feature data. Then, a feature graph is constructed based on the complete feature data, in which nodes represent features and edges represent correlations between features. Finally, the feature graph is input into a graph neural network model for training, learning high-order interactions between features, outputting stroke recurrence prediction results, and ranking feature importance. By effectively predicting and filling in missing data and using graph neural networks to mine complex topological relationships between features, the present invention improves the accuracy of stroke recurrence prediction and the robustness of the model while maintaining medical interpretability.

The invention discloses a postoperative recurrence prediction method and device, equipment and a storage medium. The method aims at obtaining a first image pyramid of a dyed slide image of a target object, obtaining a plurality of first image blocks through the first image pyramid, and extracting feature vectors of the first image blocks; obtaining a second image pyramid of the immunohistochemical slide image of the target object, obtaining a plurality of second image blocks through the second image pyramid, and extracting feature vectors of the second image blocks; determining a postoperative prediction result and a result confidence coefficient of the first image block based on the feature vector of the first image block, and determining a postoperative prediction result and a result confidence coefficient of the second image block based on the feature vector of the second image block; determining a target feature vector in the feature vectors of the plurality of first image blocks and the feature vectors of the plurality of second image blocks according to the result confidence of the first image blocks and the result confidence of the second image blocks; and determining a postoperative prediction result of the target object according to the feature vector and the target feature vector.

The present disclosure provides an atrial fibrillation recurrence prediction method, system, electronic device and storage medium, the prediction method comprising: training an atrial fibrillation recurrence prediction model based on a plurality of sets of multimodal first sample training data; each set of first sample training data comprises a first number of sample intracavity ultrasound images and at least one other modal data, calculating a first contribution value of each frame of sample intracavity ultrasound image to the training of the atrial fibrillation recurrence prediction model, the matching degree between each frame of sample intracavity ultrasound image and each other modal data, and the cross-modal credibility factor of each other modal data, to determine the target contribution value of each frame of sample intracavity ultrasound image to the training of the atrial fibrillation recurrence prediction model, and obtain different target intracavity ultrasound image types; obtaining a plurality of frames of actual intracavity ultrasound images of target data and inputting them into the atrial fibrillation recurrence prediction model to obtain a target atrial fibrillation recurrence prediction result, so as to ensure the prediction efficiency and accuracy of atrial fibrillation recurrence.

The present invention discloses a machine learning-based system for predicting the recurrence of common bile duct stones after ERCP. The system comprises the following steps: a data acquisition module for forming an original clinical data set; a data preprocessing module for constructing a standardized preprocessed clinical data set; a feature construction and hyperparameter search space definition module for defining the hyperparameter search space of an improved bidirectional gated recurrent unit network model; an improved bidirectional gated recurrent unit network model construction module for outputting a recurrence risk prediction value; a Gray Wolf optimization hyperparameter tuning module for ultimately generating an optimized improved bidirectional gated recurrent unit network model; a model training and prediction module for constructing risk classification rules based on predicted probabilities and postoperative key indicators; and an individualized result output module for outputting and delivering the predicted results to the physician. The present invention has significant clinical value in the early identification of high-risk individuals in actual deployment.

The invention provides a DNA quantitative detection method based on a real methylation level and application of the DNA quantitative detection method in preparation of a bladder cancer detection kit. The method comprises the following steps: (1) DNA purification: purifying a DNA sample by adopting a sodium acetate method; (2) DNA oxidation: oxidizing the DNA purified in the step (1) by using a potassium perruthenate solution and a neutralizing oxidant; (3) DNA transformation: transforming the DNA oxidized in the step (2) by using a methylation detection sample pretreatment kit; and (4) qRT-PCR detection of the DNA: detecting the DNA converted in the step (3) by using a DNA methylation qRT-PCR mixed system to complete quantitative detection of the DNA. The method provided by the invention is simple and convenient to operate and high in specificity, can effectively remove interference of hydroxymethylation and truly reflect DNA methylation filling, and is suitable for early diagnosis, recurrence prediction and the like of bladder cancer.

The invention belongs to the technical field of medical data processing, and provides a multimodal fusion-based liver cancer longitudinal relapse prediction and treatment effect evaluation system, which comprises a data set construction module, which is used for forming a longitudinal queue data set by using longitudinal queue data samples of a plurality of patients, and setting a relapse time label for each longitudinal queue data sample; the training module is used for training a recurrence network by utilizing the longitudinal queue data set to obtain a recurrence model; the recurrence prediction module inputs the to-be-predicted longitudinal queue data of the patient into the recurrence model to obtain a prediction result of each treatment time point, and the prediction result comprises the recurrence probability of more than one future time period; the curative effect evaluation module is used for acquiring simulation longitudinal queue data corresponding to different treatment modes selected by the to-be-evaluated patient at the current relapse time point; and inputting the simulated longitudinal queue data into the recurrence model to obtain a prediction result of each treatment time point. According to the method, the accuracy and generalization of the recurrence model are improved, and doctors are accurately and efficiently assisted in selecting treatment modes.

The invention discloses an atrial fibrillation recurrence prediction method based on artificial intelligence, and belongs to the technical field of medical information, and the method specifically comprises the steps: inputting a discrete clinical event record after an ablation operation of a target patient, and carrying out the inversion of a continuous internal state evolution path from the discrete clinical event record through an event-driven hidden state deduction model. The path is divided into a plurality of recovery stages and a feature vector is generated. And taking the last stage as a query object, retrieving similar historical stages in the pre-constructed group recovery process graph, and extracting a complete stage chain of the similar historical stages until a clear outcome. And mapping back to a physiological state space, and forming a plurality of candidate future evolution chains starting from the current state of the patient through coordinate translation. And finally, calculating the likelihood score of each candidate chain in combination with the historical event mode of the patient, and outputting a personalized recurrence risk prediction path set after sorting and screening. The invention provides a new approach for dynamically predicting the recurrence risk of the atrial fibrillation with both individual adaptability and time sequence interpretation.

The invention discloses a stomach cancer postoperative recurrence prediction method and device based on cross attention, and the method comprises the steps: carrying out the pyramid-shaped compression and segmentation of a stomach pathology full-slice image, generating a plurality of patch maps, extracting the patch maps through an encoder, and obtaining image features, which comprise a plurality of patch map features; performing feature extraction and normalization on the clinical data to obtain clinical features; inputting the image features and the clinical features into a stomach cancer postoperative recurrence prediction model to obtain a stomach cancer postoperative recurrence prediction result; the processing process of the stomach cancer postoperative recurrence prediction model comprises the following steps: based on a weak supervision multi-instance learning framework, clustering constraints are introduced into an attention mechanism to aggregate a plurality of patch map features of image features to obtain aggregated patch-level features; performing weighted fusion on the aggregated patch-level features and the clinical features by using a cross attention mechanism to generate fusion features; and inputting the fusion features into a classifier to obtain a gastric cancer postoperative recurrence prediction result.

The present invention provides a prostate cancer biochemical recurrence prediction system based on pathological sections and a construction method. It is based on high-definition panoramic pathological section scan images of pathological sections after radical resection, and adopts a multi-instance algorithm based on a cyclic cross-attention module and a pseudo-bag strategy. It first extracts deep features of tumor images of different objective lens magnifications, and then generates feature representations at the level of panoramic pathological section scan images in a weakly supervised network to obtain prediction results of postoperative biochemical recurrence risk at different magnifications; then integrates model results at different scales, and through multi-center verification, predicts whether the patient will eventually have a biochemical recurrence. The system of the present invention innovatively uses multi-center, multi-slice, and multi-scale panoramic pathological section scan images for training and verification, and realizes the prediction of the risk of prostate cancer recurrence in patients through pathological sections after radical resection, which helps to accurately predict the risk of recurrence of patients within 3 years after surgery and longer, and achieve more personalized treatment.

The invention relates to the technical field of data processing, and discloses a kidney disease rehabilitation patient data processing method and system, and the method comprises the steps: collecting multi-dimensional structured data, carrying out the data cleaning and standardization preprocessing of the multi-dimensional structured data, and obtaining the preprocessed data; a storage architecture combining a Hadoop distributed file system and an Apache Cassandra database is adopted, the preprocessed data are stored in a partitioned mode according to a time sequence, and index association between the unique identification of the patient and a multi-dimensional index is established; on the basis of the processed data, dimension reduction is carried out on high-dimension scale data, a complication topological relation is mined, time sequence indexes are dynamically weighted, and a multi-dimension comprehensive feature set is formed; inputting the multi-dimensional comprehensive feature set into an isolated forest model, predicting the disease recurrence probability of the patient, outputting a disease recurrence prediction result, and generating a personalized rehabilitation scheme; the method breaks through the limitation of traditional single index evaluation, and improves the comprehensiveness of recurrence prediction.

The application discloses a device for predicting the recurrence / metastasis risk of a non-small cell lung cancer patient after operation and application thereof, adopts multiple machine learning algorithms to combine the clinical characteristics, genomic sequencing data and immune microenvironment infiltration characteristics of real world non-small cell lung cancer patients with disease-free survival, and constructs a postoperative recurrence prediction model. Through a Lasso coefficient path diagram, it is confirmed that five characteristics including tumor size, tumor interstitium CD8+ T cell positive rate, tumor parenchyma M2 type tumor-related macrophage positive rate, TP53 gene mutation state and postoperative ctDNA-MRD state are included in model construction, and a MIMIR prediction model is constructed through a random survival forest. The model can help clinicians to accurately evaluate the postoperative disease-free survival of non-small cell lung cancer patients, guide the development of individualized treatment and follow-up schemes, and bring better survival benefits to patients.

The invention relates to the technical field of medical data mining, in particular to an atrial fibrillation recurrence prediction method and system based on multi-modal data. The method comprises the steps of dividing grade time periods based on historical symptom grade duration conditions; determining an atrial fibrillation danger index in combination with the similar situation of symptom level change maintenance and keyword addition in the level time period; determining a normal time period and screening out a problem time period according to the periodic fluctuation condition of the dynamic electrocardiogram; and analyzing the problem performance degree according to the difference between the problem time period and the normal time period, the problem distribution of the level time period and the danger index, determining the prediction attention degree by referring to the frequent confusion condition of the preorder problem distribution, and adjusting the prediction weight based on the prediction attention degree to carry out prediction and early warning. According to the method, through integration of multi-source data and dynamic risk quantification, the prediction weight of a high-risk time period is adjusted, the individuation and accuracy of atrial fibrillation recurrence prediction are effectively improved, and more reliable support is provided for recurrence early warning.

The application provides a laryngeal squamous cell carcinoma prognosis gene methylation marker and application thereof, and relates to the technical field of clinical medicine. The methylation marker is CORO1C and MAPK11, and a recurrence risk prediction model is constructed based on the two sites. The prediction model is independent of factors such as stage, age and differentiation of patients, proving the independence and universality of the prediction model. Through the RRBS technology, the methylation changes of the genes can be comprehensively analyzed at high resolution, and the prognosis value of the markers in laryngeal squamous cell carcinoma is verified. Compared with traditional single clinical pathological indicators, the methylation marker based on the molecular level provided by the application has high accuracy and sensitivity, and can provide more accurate basis for the recurrence prediction of laryngeal squamous cell carcinoma.

The present application relates to the technical field of medical data mining, in particular to a method and system for predicting recurrence of atrial fibrillation based on multi-modal data. The method divides the level period based on the historical symptom level duration; determines the atrial fibrillation risk index by combining the symptom level change and the keyword addition similarity in the level period; determines the normal period and filters out the problem period by the periodic fluctuation of the dynamic electrocardiogram; analyzes the problem performance degree according to the difference between the problem period and the normal period, the problem distribution of the level period and the risk index, and determines the prediction attention degree by referring to the frequent confusion of the previous problem distribution, and adjusts the prediction weight based on the prediction attention degree to make prediction and warning. The present application integrates multi-source data and dynamically quantifies the risk, adjusts the prediction weight of the high-risk period, effectively improves the individualization and accuracy of the prediction of recurrence of atrial fibrillation, and provides more reliable support for recurrence warning.

The application relates to the establishment of an early gastric cancer recurrence prediction model. By using two batches of gene chip transcriptome data GSE130823 and GSE55696, 25 potential genes related to early gastric cancer recurrence are screened out, and an early gastric cancer recurrence prediction model based on eight genes AREG, LOC100507520, MMD, CH3L1, FOS, CCL20, CXCR2 and BATF3 is established. The model has excellent sensitivity, that is, all the patients predicted to not relapse do not relapse, and the frequency of reexamination and follow-up of the patients can be adjusted according to the model.

The invention relates to the technical field of prediction models, and particularly discloses a pain and rheumatism syndrome biomarker, a diagnosis / prediction model and a construction method thereof. The pain and rheumatism syndrome biomarker comprises at least one of acetyl vanillin, cyclic adenosine monophosphate, methyl vanillate and uridine succinic acid; the pain and rheumatism syndrome biomarker comprises a pain and rheumatism syndrome diagnosis marker and a pain and rheumatism syndrome recurrence prediction marker; the pain and rheumatism syndrome diagnosis marker comprises at least one of acetyl vanillin, cyclic adenosine monophosphate and methyl vanillate; the pain and rheumatism syndrome recurrence prediction marker comprises cyclic adenosine monophosphate and uridine succinic acid. According to the application, a diagnosis model of the pain and rheumatism syndrome can be constructed on the basis of the expression levels of the three plasma metabolites, namely the acetylvanillin, the cyclic adenosine monophosphate and the methyl vanillate; based on the plasma expression level of cyclic adenosine monophosphate and uridine succinic acid, a gout recurrence prediction model can be developed, and high prediction accuracy is achieved.