40 results about "Recurrence prediction" patented technology

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 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 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.

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.

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 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 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 present application belongs to the technical field of medical data processing, and provides a liver cancer longitudinal recurrence prediction and treatment efficacy evaluation system based on multi-modal fusion, comprising: a data set construction module, which uses multiple patient longitudinal cohort data samples to form a longitudinal cohort data set, and sets a recurrence time label for each longitudinal cohort data sample; a training module, which trains a recurrence network using the longitudinal cohort data set to obtain a recurrence model; a recurrence prediction module, which inputs the patient's to-be-predicted longitudinal cohort data into the recurrence model to obtain prediction results at each treatment time point, the prediction results including the recurrence probability of more than one future time period; and an efficacy evaluation module, which obtains simulated longitudinal cohort data corresponding to different treatment methods at the current recurrence time point of the patient to be evaluated; and inputs the simulated longitudinal cohort data into the recurrence model to obtain prediction results at each treatment time point. The present application improves the accuracy and generalizability of the recurrence model, and accurately and efficiently assists doctors in selecting treatment methods.

The application relates to an ablation evaluation method, an ablation evaluation system and a storage medium. The method comprises the following steps: determining a lesion area from a preoperative image of a target object, and determining an ablation area from a postoperative ablation image of the target object; performing difference analysis according to the lesion area and the ablation area to determine an ablation rate; the ablation rate represents the ablation degree of the lesion; inputting related information of the lesion corresponding to the lesion area and the ablation rate into a preset recurrence prediction model to perform evaluation, and obtaining an ablation evaluation result; the ablation evaluation result comprises recurrence information and / or supplementary ablation reference information determined based on the recurrence information. In one aspect, the ablation effect is evaluated based on the lesion area and the ablation area obtained immediately after the operation, and supplementary ablation is performed in time, so that the ablation effect is improved, and the number of ablation times is reduced. In another aspect, the related information of the lesion and the ablation rate are evaluated and analyzed based on a recurrence prediction model, so that more accurate evaluation results can be obtained, and the accuracy of recurrence prediction is improved.

The application provides a marker combination and application for predicting recurrence of ulcerative colitis. Specifically, the marker is at least one of SLC6A14, MUC-2 and Nancy index. The marker can be used for diagnosis and recurrence prediction of ulcerative colitis, and has strong detection capability for remission and recurrence of patients after clinical drug treatment.

The application discloses a lung adenocarcinoma recurrence prediction method and system based on multi-omics data analysis, relates to the technical field of precision medicine, and comprises the following steps: collecting folate metabolism group data, a transcription group expression spectrum and methylation level data of a target gene promoter region of tumor tissue of a lung adenocarcinoma patient; constructing a metabolism-epigenetic correlation topology graph based on a spatial adjacent relationship, screening a significant causal relationship between methylation variation and metabolism fluctuation through causal analysis, and generating a methylation metabolism significant causal edge set; fusing simulation metabolism channel characteristics and the methylation metabolism significant causal edge set, constructing a lung adenocarcinoma recurrence risk prediction model, triggering a high-risk early warning signal through time sequence analysis of risk factor fluctuation; through the construction of the metabolism-epigenetic correlation topology graph and the screening of the significant causal edge set, the causal correlation modeling of multi-omics data in the spatial dimension is realized, the robustness of the recurrence prediction model is improved, the prediction performance is synergistically enhanced, and the accuracy of lung adenocarcinoma recurrence prediction is significantly improved.

The invention relates to the technical field of medical imaging omics analysis, and discloses an early-stage liver cancer postoperative recurrence prediction model and method based on imaging omics phenotypes, and the method comprises the following steps: data collection and preprocessing, feature screening and standardization, model construction, model verification and risk layering. The data acquisition and preprocessing is to obtain preoperative enhanced CT image data and clinical information of an early-stage hepatocellular carcinoma (HCC) patient meeting the agralan standard, and the clinical information comprises serum alpha fetoprotein (AFP) level and tumor number. According to the image omics phenotype-based early liver cancer postoperative recurrence prediction model and method, pre-operation and post-operation dual models are constructed by integrating pre-operation enhanced CT image omics characteristics (including first-order statistics, texture and wavelet characteristics), clinical parameters (such as serum alpha fetoprotein and tumor number) and post-operation pathological variables (such as microvascular invasion and satellite nodules).

The invention belongs to but is not limited to the technical field of medicine, and particularly relates to a method and system for constructing a liver cancer radiofrequency ablation short-term curative effect and postoperative recurrence model, a GE Resolution 256-row energy spectrum CT scanner is used for obtaining specific parameters of a focus area, and the correlation between multi-parameter data and liver cancer radiofrequency ablation postoperative short-term curative effect and postoperative recurrence is analyzed; the method comprises the following steps of: delineating a focus, extracting image feature texture information, analyzing the correlation between the image feature texture information and the liver cancer radiofrequency ablation postoperative recurrence rate, and discussing the application value of energy spectrum CT image radiomics in predicting the liver cancer radiofrequency ablation postoperative recurrence; clinical data including age, gender, laboratory examination, pathological types, size and number of lesions are collected and sorted, correlation analysis is carried out on the clinical data and relapse after radiofrequency ablation, and indexes related to relapse of liver cancer patients are screened; in combination with related clinical data and energy spectrum CT image radiomics quantitative characteristics, an optimal postoperative recurrence prediction model is established through an artificial intelligence method, and clinical verification is carried out.

The application provides a kind of multi-source heterogeneous data fusion's hepatobiliary stone recurrence intelligent prediction method, comprising the following steps: S1: obtaining the CT two-dimensional image and MRI two-dimensional image of the hepatobiliary stone patient's abdomen, respectively using diffusion model to CT two-dimensional image and MRI two-dimensional image image reconstruction, extract image depth features from the reconstructed CT three-dimensional image and MRI three-dimensional image;Obtain the preoperative blood test index of the hepatobiliary stone patient, the data of liver and bile duct stenosis / dilation degree and the time series of liver and bile duct disease, and extract the fusion features of clinical data;S2: after the image depth features and the fusion features of clinical data are fused, input into the pre-trained FastBERT network, to obtain accurate hepatobiliary stone recurrence prediction result.The application provides a kind of multi-source heterogeneous data fusion's hepatobiliary stone recurrence intelligent prediction method, solves the problem that existing hepatobiliary stone recurrence prediction is not accurate enough.

The present application relates to the technical field of medical imageomics, and particularly relates to a pancreatic cancer early recurrence prediction method and system based on an interpretable machine model, comprising the following steps: extracting intratumoral and peritumoral imageomics features in CT images; performing single factor analysis and multivariate logistic regression analysis on body composition parameters and clinical pathological data to obtain clinical features; constructing six groups of classifier models based on the intratumoral and peritumoral imageomics features through six machine learning algorithms, and obtaining an imageomics model according to model performance comparison; constructing a clinical-imageomics combined model by combining the clinical features and the imageomics model, and performing an interpretable SHAP analysis on the clinical-imageomics combined model. The present application combines intratumoral and peritumoral CT imageomics features with body composition parameters, constructs a machine learning model for predicting the early recurrence risk of PDAC after resection, and incorporates the interpretable SHAP analysis to enhance the transparency of the machine learning model decision-making process.

The invention relates to the technical field of medical treatment, and particularly discloses application of NAE1 in diagnosis, treatment and recurrence prediction of lung squamous cell carcinoma. A biomarker is NAE1 protein or a coding gene thereof, when the expression level of the NAE1 is higher than that in normal tissue, existence of the lung squamous cell carcinoma is indicated, and when the expression level of the NAE1 in the normal tissue is lower than that in the normal tissue, existence of the lung squamous cell carcinoma is predicted. The expression level of the NAE1 is determined by detecting the mRNA level, the protein level or the activity level of the NAE1 in a sample; by detecting the NAE1 expression level, the accuracy of predicting the postoperative recurrence risk of the lung squamous cell carcinoma patient is effectively improved, and the diagnosis and treatment strategy of the patient can be remarkably improved. Meanwhile, the treatment mode taking the NAE1 as the target spot can effectively inhibit the growth and proliferation of lung squamous carcinoma cells, reduce the risk of recurrence and metastasis, and significantly improve the treatment effect and the long-term life quality of patients. In addition, the technical scheme provided by the invention has the advantages of higher precision, definite treatment effect, obvious reduction of serious toxic and side effects caused by the traditional treatment method, simplicity and convenience in operation and obvious clinical application prospect.

The application provides a non-muscular invasive bladder cancer postoperative recurrence prediction method and system, the method comprising: obtaining preoperative enhanced CT images of a target object, urine SIM2 gene methylation detection results and clinical pathological characteristics; pre-processing the preoperative enhanced CT images and delineating a tumor region of interest, and extracting imageomics features and deep learning features to construct a preliminary multi-modal feature set; performing feature screening and dimension reduction processing on the imageomics features, and fusing the processed imageomics features with the preliminary multi-modal feature set to obtain a multi-modal feature set; inputting the multi-modal feature set into a pre-constructed prediction model to output a non-muscular invasive bladder cancer postoperative recurrence risk probability and risk level of the target object, wherein a first layer of the prediction model is constructed by using a plurality of heterogeneous base learners, and a second layer is constructed by using a meta learner. The application can realize fine stratified management of non-muscular invasive bladder cancer patients.

This invention provides a method and system for predicting the recurrence risk of nasopharyngeal carcinoma (NPC) using multimodal data, belonging to the field of medical data processing technology. The method includes acquiring NPC data of different modalities, including whole-section pathological images, magnetic resonance imaging (MRI) images, and clinical test data; extracting features from the whole-section pathological images, MRI images, and clinical test data respectively; cascading the extracted features according to channels to achieve feature fusion; mapping the fused feature vectors to obtain NPC recurrence prediction probability data for different timeframes. This forms a unified decision-making path, providing data support for NPC recurrence prediction and facilitating the planning of subsequent treatment strategies.

The invention provides Salivary Biomarker Panel for Therapeutic Monitoring and Relapse Prediction in Head and Neck Cancers, comprising synergistic combination of miR-1307-5p, CD44v6, KRT4, and PD-L1 quantified from salivary extracellular vesicles. Biomarker expression values are processed in which fold-change thresholds are computed, individual ChemoScore, RelapseScore, and ImmunoScore are derived, and patients are stratified into therapeutic response, relapse risk, immunotherapy suitability, and prognostic categories. Individual scores are further integrated into Overall Risk Score to classify patients into low, moderate, high, or very high risk. Limitations of tissue biopsies and delayed imaging-based assessments are overcome, enabling repeatable, real-time molecular monitoring to support personalized treatment strategies. The panel embodies into diagnostic kit comprising reagents, primers, reference controls, integrated scoring system, facilitating standardized detection and interpretation for dynamic clinical decision-making in head and neck oncology.

The application provides a multi-omics joint detection system for prostate cancer recurrence risk assessment, the application synchronously acquires genomic, transcriptomic, epiproteomic and metabolomic data of different regions of a tumor through spatially resolved in situ capture technology, and integrates multi-dimensional information such as circulating tumor DNA epigenetic memory, urological microbiome-host interaction, single-cell clone evolution and tumor microenvironment three-dimensional topology. The system uses a dynamic Bayesian fusion engine to perform probabilistic risk calculation, combines digital twin technology to simulate treatment response, and realizes model adaptive updating through longitudinal follow-up data. The output result has high interpretability and can directly show key driving factors and their clinical interventional properties. The system breaks through the limitations of traditional static and single-omics models, significantly improves the prediction accuracy of recurrence, especially in low-risk populations, and provides intelligent support for individualized auxiliary treatment decisions.

The invention relates to the field of intelligent medical treatment, in particular to a recurrence prediction method, device and program product after cancer ablation based on multi-time sequence images. The method includes: acquiring an image of a cancer patient before tumor ablation; transmitting the tumor pre-ablation image to a multi-task classification prediction model to obtain a recurrence or non-recurrence classification result; the multi-task classification prediction model training process comprises the following steps: acquiring a multi-time sequence image set of a cancer patient; dividing the multi-time sequence image set based on an ablation time node and a latest time node to obtain a pre-ablation image set and a latest image set respectively; and the multi-time-sequence image set is transmitted to the neural network for feature extraction to obtain a first feature, a second feature and a third feature, the first feature, the second feature and the third feature are transmitted to the classification task module for training to obtain a multi-task classification prediction model, and the application has good clinical value.