242 results about "Recurrence risk" patented technology

The invention provides a multi-mode prostate cancer biochemical recurrence risk layering prediction system based on artificial intelligence. Based on an Xgboost framework, a postoperative patient pathological panoramic pathological section scanning image is analyzed through end-to-end, multi-scale, multi-center and large-sample analysis, pathological information is utilized to the maximum extent, meanwhile, the prognosis risk of a patient can be evaluated more comprehensively in combination with clinical indexes such as CAPRA-S scores, and the method has obvious advantages compared with a traditional model. The method aims at better fitting the use scene of a hospital, the risk of prostate cancer recurrence of a patient is more efficiently and accurately predicted by fusing pathological section features and clinical features after a radical operation, and the risk of recurrence of the patient within 3 years and longer time after the radical operation can be accurately predicted. And an interpretable module is further combined to assist a doctor to interpret a result, so that precise layering and personalized follow-up visit of the BCR risk of the prostatic cancer patient are realized, the risk of excessive treatment and missed diagnosis is reduced, and the method has a good application prospect.

The invention discloses a glioma T cell prediction and prognosis evaluation method based on pathological images, particularly relates to the field of patient prognosis health evaluation, and aims to solve the problems that existing pathological evaluation is difficult to combine with tumor structure heterogeneity and immune infiltration distribution, and the future progress risk of a patient cannot be predicted based on follow-up visit pathological data. A spatial heterogeneity map of a tumor core area and an invasion edge is constructed in a digital pathological section, density gradients of T cells in different areas are calculated to generate a distribution heterogeneity coefficient, interaction processing is performed on the two types of characteristics in combination with historical follow-up visit records, and a time sequence neural network constructed based on a gating structure is combined to obtain a high-efficiency characteristic of the tumor. And outputting disease progress probabilities of a plurality of follow-up visit time points in the future to form a prognosis trajectory prediction curve, calculating a quantitative recurrence risk score according to curve slope change and an immune fluctuation mode, and finally generating an individualized management scheme, thereby realizing quantitative prediction evaluation and risk management of the prognosis trend of the glioma patient.

The invention discloses a gastric cancer recurrence risk prediction method based on metabonomics characteristic analysis. The method comprises the following steps: S1, constructing a metabonomics data set; s2, the collected metabonomics data set is preprocessed; s3, extracting metabolite characteristics related to gastric cancer recurrence; s4, based on the extracted metabolite features, using a metabolic pathway integration method to quantify disturbance of each metabolic pathway, and calculating the abnormal degree of metabolite concentration change in the metabolic pathways; s5, constructing a multi-dimensional feature matrix containing a metabonomics data set, metabolic pathway integrals and patient individual information; s6, inputting the multi-dimensional feature matrix into a trained machine learning model, and outputting a gastric cancer recurrence risk score of the patient; and S7, generating a prediction report according to the recurrence risk score. According to the method, by combining metabonomics characteristic analysis, the space-time coupling quantum optimization network and the nonlinear disturbance model, the problems of insufficient real-time performance and insufficient data integration in gastric cancer recurrence risk prediction in the prior art are solved.

The invention relates to the technical field of medical data analysis, in particular to a brain tumor curative effect analysis system which comprises a tumor data sensing layer for collecting multi-department diagnosis and treatment data, tumor image data and patient pathology monitoring data; the tumor feature processing center extracts data features, correlates data and core features before and after treatment through an attention mechanism, and generates a tumor complete-cycle unified feature map; the therapeutic effect dynamic analysis unit evaluates the therapeutic effect in stages, and outputs a therapeutic effect index and a recurrence risk value through a self-supervised model; the dynamic adaptation decision module is used for generating personalized treatment adjustment suggestions based on the dynamic change of the blood brain barrier in combination with the curative effect index, the recurrence risk value and the multi-omics characteristics of the patient; and the AI multi-department consultation unit automatically matches similar cases with field expert suggestions, and formulates a target diagnosis and treatment scheme based on a visual platform and multi-department doctor collaborative consultation in combination with personalized treatment adjustment suggestions. Therefore, the problems of lagging effect evaluation, insufficient diagnosis and treatment suggestions and the like in the prior art are solved.

The invention discloses a bladder cancer postoperative recurrence risk prediction system, and relates to the technical field of cancer recurrence risk prediction. The bladder cancer postoperative recurrence risk prediction system comprises a data acquisition module, a data processing module, a feature engineering module, a risk prediction module, a decision support module and a result display module. According to the method, a bladder cancer postoperative recurrence risk prediction model is optimized and improved, that is, an input layer, a full connection layer, a merging layer and a comprehensive processing layer are newly added on a traditional LSTM model, so that the prediction model can fully utilize time sequence data and static feature data; therefore, the influence of time sequence data such as methylation detection and miRNA expression of the patient and static data such as age and gender of the patient on the recurrence risk of the bladder cancer can be fully considered, data can be analyzed from different angles and levels, a more accurate prediction result is provided, the overfitting risk of the prediction model can be reduced, and the prediction accuracy of the bladder cancer is improved. And the generalization ability of the whole prediction model is improved.

The invention discloses a system and a method for predicting postoperative recurrence risk after triple negative breast cancer neoadjuvant therapy based on multi-modal time sequence medical image data, and belongs to the field of medical image analysis. The system comprises a data processing module used for constructing a multi-modal data set; the multi-modal feature extraction and screening module is used for extracting deep learning, radiomics and tumor habitat features from the region and carrying out feature screening; the model training module is used for constructing a time sequence model based on a Transform architecture and carrying out training through a multi-task learning strategy integrated with time consistency constraint and gene association auxiliary loss; and the recurrence risk prediction module is used for loading the trained model and outputting a recurrence probability and a risk level. According to the method, the multi-modal time sequence image and gene information are fused, so that the recurrence risk of the triple negative breast cancer patient is dynamically and accurately quantified, and support is provided for clinical individualized treatment decision.

The invention discloses a cerebral apoplexy prediction method and device based on a cerebrovascular image representation group, a medium and a product, and relates to the field of image processing, and the method comprises the steps: constructing a clinical database; according to the multi-modal image feature group data, the clinical feature data and the clinical cause typing of the patient, constructing a clinical cause typing prediction model; according to TOF-MRA, a cerebrovascular tissue segmentation model and voxel-based morphological analysis are adopted to determine cerebrovascular image features, and positioning diagnosis of a responsible blood vessel area is carried out according to the cerebrovascular image features; according to the cerebrovascular image features in the TOF-MRA, the SWI, the T1WI, the T2-FLAIR and the corresponding clinical feature data, a cerebral apoplexy recurrence risk prediction model is constructed; according to the application, the pathogenesis classification of the acute cerebral apoplexy patient can be quickly and accurately realized in a clinical scene, the responsible blood vessel can be accurately identified, and the recurrence risk prediction can be realized in the early stage of the disease.

The invention relates to the technical field of biomedicine, and discloses a rectal cancer postoperative recurrence risk prediction method based on a prediction model, and the method comprises the steps: obtaining a patient tumor in the in-vivo image data of a rectal cancer patient; extracting imaging features of the tumor of the patient, and identifying recurrence risk features of the rectal cancer patient from the imaging features; analyzing clinical pathological characteristics of the rectal cancer patient according to the preoperative evaluation data and the postoperative pathological report; key recurrence influence factors of the rectal cancer patients are identified, recurrence events and follow-up visit time of the rectal cancer patients are identified, risk scoring is carried out on the rectal cancer patients, and a risk scoring result is obtained; based on the risk scoring result, the recurrence risk stratification of the rectal cancer patient is defined, postoperative recurrence risk analysis is conducted on the rectal cancer patient in combination with the recurrence risk stratification, the recurrence risk characteristics and the risk scoring result, and a risk analysis result is obtained. According to the invention, the comprehensiveness and accuracy of the rectal cancer recurrence risk analysis result can be improved.

The invention belongs to the field of biomedicine, and particularly relates to application of CRYL1 protein in preparation of a product for evaluation, diagnosis or auxiliary diagnosis of postoperative recurrence risk of calcium oxalate kidney stone combined with renal nipple calcium spot, and the sequence of the CRYL1 protein is shown as SEQ ID NO.1. The invention discovers that the expression of the CRYL1 protein in renal tubules and collecting tube cells of renal papillary calcium spot tissues is reduced for the first time, and tests prove that the urine CRYL1 protein is a predictive factor and a predictive marker for postoperative recurrence of a patient with CaOx kidney stone combined with renal papillary calcium spot. The CRYL1 protein detection kit is further researched and developed to be used for detecting or evaluating the postoperative recurrence risk of patients suffering from CaOx kidney stone combined with renal nipple calcium spots, personalized follow-up visit and prevention schemes of the patients are promoted, and the CRYL1 protein detection kit has huge clinical application prospects.

The invention discloses a depression recurrence risk intervention method, device and equipment and a medium, and belongs to the technical field of medical treatment. The method comprises the following steps: integrating a gene risk score, neuroimaging brain region characteristics and clinical medical history data through a multi-modal fusion neural network, and generating an individual baseline risk score; the baseline score is dynamically corrected based on the self-assessment data, and the real-time performance of risk assessment is enhanced; analyzing time sequence characteristics of the physiological and behavior data by using an LSTM time sequence model, and outputting a short-term recurrence early warning label; and in combination with emotion knowledge graph analysis and dynamic risk grading of real-time voice / text data, a hierarchical intervention strategy is triggered. Through multi-dimensional data fusion and a dynamic calibration mechanism, the problems that a traditional method is single in evaluation dimension and lags in response are solved, closed-loop management from risk early warning to accurate intervention is achieved on the premise that direct clinical diagnosis is avoided, and comprehensiveness and timeliness of prevention and control of depression recurrence are improved.

The invention provides a dynamic adjustment method based on a depression mind-correcting treatment scheme, and relates to the technical field of medical care information, and the method comprises the steps: collecting multi-dimensional information of a patient, and collecting life behavior risk data, psychological scale data and cognitive function evaluation data; comprehensively evaluating the depression level of the patient; determining the priority of a treatment therapy module according to the multi-dimensional information of the patient; generating an initial treatment scheme; calculating a daily task comprehensive weight value, and dynamically adjusting the training content of the next day; and evaluating the state of the patient in stages, recalculating the priority of each therapy module, and optimizing the therapeutic scheme. Through multi-dimensional data fusion evaluation and intelligent algorithm dynamic adjustment, a complete closed-loop feedback optimization system is constructed, precision and individuation of a depression treatment scheme are achieved, the treatment effect and patient compliance are effectively improved, the recurrence risk is reduced, and a more scientific and effective treatment scheme is provided for depression patients.

The invention relates to the technical field of artificial intelligence precision medical treatment, in particular to a multi-mode hepatocellular carcinoma recurrence risk prediction method and system and application, and the prediction method comprises the steps of multi-mode data acquisition, data standardization preprocessing, model training and recurrence risk prediction. The method comprises the following steps: firstly, collecting multi-modal data of a patient, wherein the multi-modal data comprises preoperative data, postoperative data and a recurrence risk label; secondly, carrying out standardized preprocessing on the collected multi-modal data; and updating parameters of the pre-operation model and the post-operation model by using an adaptive moment estimation algorithm to obtain a trained multi-modal hepatocellular carcinoma recurrence risk prediction model which comprises the pre-operation model and the post-operation model and can be used for performing recurrence risk prediction before and after an operation respectively. According to the method, the deep learning technology is used, the multi-modal data before and after the operation can be respectively utilized, the recurrence risk of the HCC patient can be accurately predicted before and after the operation, and a targeted treatment scheme is formulated for the HCC patient.

The invention discloses a tumor recurrence risk prediction method and system based on electronic medical record data, and relates to the field of electronic medical record data processing and analys.The historical electronic medical record data are processed in a structured mode, a recurrence risk mapping model is established, individualized recurrence risk assessment can be achieved based on multi-dimensional features, and the tumor recurrence risk prediction accuracy is improved. The subjective judgment error is obviously reduced; a risk level layering mechanism can automatically distinguish patients needing emergency intervention and conventional monitoring, excessive medical treatment or delayed treatment is avoided, and the method is particularly suitable for chronic diseases such as tumors needing long-term management; through periodic marker measurement and risk level feedback, closed-loop management of evaluation-intervention-re-evaluation is formed, and the requirement for continuous optimization of clinical diagnosis and treatment is met; in addition, through preprocessing, the preprocessed multi-dimensional features can be directly called subsequently, and repeated data cleaning work is avoided.

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 provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention discloses a breast cancer recurrence risk prediction method and system based on multi-modal data missing interpolation and gene interpretability enhancement. The method comprises the following steps: firstly, dynamically generating and complementing features of a missing mode by matching a generative adversarial network with a mode missing mask matrix; then, a feature screening mechanism driven by gene information is introduced, through a multi-task learning network, image feature extraction is supervised by using a gene expression tag in a model training process, and image features highly associated with recurrence-related genes are screened out; and finally, fusing the complemented multi-modal time sequence characteristics by adopting Transform, and outputting a recurrence risk probability. According to the method, the robust prediction performance can be realized under the condition of data missing, and meanwhile, image interpretation with a molecular biology basis is provided for the feature screening process of the model, so that the reliability and clinical acceptability of the whole system are enhanced.

The invention relates to the technical field of bioinformatics analysis, in particular to an enhanced CT image-based hepatocellular carcinoma postoperative early recurrence multi-modal prediction model and a construction method thereof, and the construction method comprises the following steps: S1, data acquisition: collecting clinical data and liver enhanced CT images of a hepatocellular carcinoma surgical patient, and evaluating the recurrence condition of the HCC patient; s2, image preprocessing: sketching a region of interest in the image, and extracting deep learning features; s3, establishing a model: establishing a plurality of image omics models, constructing a plurality of deep learning models, and constructing a multi-modal model; s4, evaluating and verifying the model: evaluating the prediction accuracy of the multi-modal model by adopting an ROC curve and an AUC value, and evaluating the prognosis prediction capability of the patient by using a Kaplan-Meier curve; according to the method, the characteristic expression ability and the recurrence risk discrimination performance are improved, a scientific basis can be provided for follow-up visit and treatment of the hepatocellular carcinoma postoperative patient, and the lifetime of the patient is prolonged.

The invention relates to the technical field of medical care, in particular to a tumor prognosis prediction method based on tumor database text recognition, which comprises the following steps: S1, data collection and preprocessing: collecting medical data related to tumors from a plurality of tumor databases; s2, text information extraction: performing information extraction on clinical text data; s3, predicting a tumor metastasis path: predicting the metastasis path by utilizing the graph neural network model, predicting a potential metastasis path of the tumor, and analyzing the influence of different metastasis paths on the prognosis of the patient; s4, prognosis evaluation and risk prediction: predicting the tumor prognosis risk in combination with the medical data of the patient, and comprehensively evaluating the five-year survival rate and the recurrence risk of the patient. According to the method, the medical institution can be assisted in optimizing resource allocation, a scientific personalized diagnosis and treatment scheme is formulated, and the life quality and the treatment effect of the patient are remarkably improved.

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 discloses a construction method of a risk prediction model for early diagnosis of liver cancer. The construction method of the risk prediction model comprises the following steps: 1, preparing clinical data; 2, processing, analyzing and learning clinical data, and constructing a project database of a hepatocellular carcinoma clinical diagnosis path based on big data; 3, determining main diagnosis key points in the disease timing sequence diagnosis scheme and related indexes influencing the operation, and formulating a diagnosis and treatment path extraction rule; 4, constructing a quality-efficiency evaluation index system for the diagnosis and treatment path set, and calculating the early diagnosis probability and postoperative recurrence probability of the liver cancer; and 5, establishing a Markov model for early screening and diagnosis of liver cancer and risk prediction of postoperative recurrence. According to the method, the ANN principle is utilized, an HCC early diagnosis risk prediction model and an HCC clinical diagnosis and treatment path evaluation system are established, HCC high-risk groups are subjected to early recognition, and the HCC early diagnosis rate is increased; meanwhile, an optimal clinical diagnosis and treatment decision is provided for HCC treatment.

The invention provides a gout diagnosis and recurrence risk prediction method and system based on Raman spectrum and multi-modal machine learning, and the method comprises the steps: generating a Raman spectrum sample data set, and carrying out the preprocessing; performing data dimension reduction and feature extraction on the preprocessed Raman spectrum sample data set to obtain key feature vectors; performing feature screening and enhancement on the key feature vectors before modeling; modeling the data before and after dimension reduction by using a machine learning algorithm to obtain a plurality of machine learning models; and training the multiple machine learning models by using a training set, verifying the models by using a ten-fold cross validation method, selecting the machine learning model with the best discrimination effect as a prediction model, and performing risk prediction of medical history development and gout recurrence. The Raman spectrum and the advanced artificial intelligence technology are integrated, a new prospect is provided for identifying unique metabolic characteristics related to the diseases, the clinical management level of gout is improved, and intervention is earlier and more accurate.

The invention discloses a thyroid tumor classification and recurrence risk prediction method based on deep learning, and belongs to the field of medical image processing, and the method comprises the following steps: collecting and preprocessing multi-modal data related to thyroid follicular carcinoma FTC or follicular adenoma FTA; performing feature extraction on the preprocessed multi-modal data to obtain image features and pathological features; respectively adopting different fusion and optimization methods for the combination of the image features and the pathological features and the image features to obtain a first optimized fusion feature and a second optimized fusion feature; and inputting the first optimized fusion feature into a preoperative classification model to obtain a thyroid tumor type classification probability, and inputting the second optimized fusion feature and the pathological feature into a postoperative recurrence risk prediction model to obtain a recurrence risk score. By means of the multi-mode image classification model and the recurrence risk prediction model which are specially constructed, the sensitivity and specificity of preoperative classification are improved, and accurate individualized management is provided for postoperative patients.

The present invention relates to methods for predicting the recurrence risk of hepatocellular carcinoma (HCC). Specifically, it proposes a deep learning model capable of integrating information from different phases of CT images and clinical data to predict the risk of HCC recurrence within 1 to 5 years after treatment. Experimental results demonstrate that these models outperform traditional prediction methods based on histological microvascular invasion (MVI) in predicting HCC recurrence risk.

The invention provides a coronary heart disease recurrence risk assessment method and device, equipment and a storage medium, and relates to the technical field of medical data processing. The method comprises the following steps: acquiring dynamic behavior data, physiological data and static risk indexes of a target patient; calculating a treatment compliance index and a rehabilitation health index of the target patient based on the dynamic behavior data and the physiological data of the target patient; and inputting the treatment compliance index, the rehabilitation health index and the static risk index into a trained coronary heart disease recurrence risk scoring model to obtain a coronary heart disease recurrence risk score of the target patient. According to the method, objective and quantitative recurrence risk scores can be obtained, more accurate decision support is provided for clinicians, and early warning and personalized intervention can be realized, so that the prognosis of patients is improved, and the medical cost is reduced.

The invention discloses a cerebral apoplexy recurrence risk monitoring method and device and a medium, and relates to the technical field of medical health monitoring, the cerebral apoplexy recurrence risk monitoring method comprises the following steps: according to a preparation result, collecting electroencephalogram, oxyhemoglobin saturation, electrocardio, pulse waves and acceleration signals, synchronously recording timestamps, and generating multi-modal physiological data; performing de-noising processing and feature extraction on the multi-modal physiological data to generate de-noised feature data; performing multi-modal feature fusion on the de-noised feature data by adopting a convolutional neural network to generate a multi-modal feature vector, identifying feature signal modes of epilepsy, brain structures and brain diseases according to the multi-modal feature vector, calculating a cerebral apoplexy recurrence risk score, and generating a risk score result and an anomaly identification report; and carrying out risk grade division on the risk scoring result and the abnormity identification report according to a recurrence risk threshold value and a personalized judgment rule, and generating risk early warning information and personalized intervention suggestions. According to the invention, real-time and explainable risk early warning information is provided for clinicians and patients.

The invention discloses a multi-modal-based prediction device for the recurrence risk of a patient with cavity and facial breast cancer, the device comprises one or more pre-trained multi-modal models, and the device is suitable for: in response to the received multi-modal information of a patient to be predicted, determining a target multi-modal model to be used from the one or more multi-modal models; obtaining a prediction feature set from the multi-modal information according to a training feature set adopted by the target multi-modal model during training; and inputting the prediction feature set into the target multi-modal model, and determining a recurrence risk score of the to-be-predicted patient.

The invention discloses a model uncertainty-driven man-machine collaborative risk grading interpretation method, system and device and a computer readable storage medium. According to the method, under federated learning deployment, calibration confidence, bucket-level calibration deviation and multi-model inconsistency are simultaneously calculated for a single sample, and a comprehensive uncertainty score is formed to perform risk grading: when the comprehensive uncertainty score exceeds a threshold value or the calibration confidence is insufficient, manual re-checking is automatically triggered; otherwise, directly outputting the AI result. Artificially confirmed samples enter a feedback sample library for subsequent federation retraining, temperature parameters and barrel counting are periodically updated, and a continuous learning closed loop is constructed. According to the scheme, in medical scenes such as lung CT nodule detection and lung cancer I-stage recurrence risk prediction, the diagnosis efficiency and clinical safety are effectively considered, and the long-term stability and credibility of the model are improved.

The invention discloses a depression recurrence risk intelligent early warning method, system and device based on multi-modal data and artificial intelligence, and relates to the field of depression classification early warning, and the method comprises the steps: carrying out the individualized deviation calculation based on the baseline features and multi-modal brain image features of a target patient, obtaining an individual multi-modal brain image deviation feature vector; dimension reduction processing is carried out on the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features, the individualized multi-mode brain image deviation feature vector, the genetic features and the environment and clinical features are input into a pre-trained layered integrated classification model and a pre-trained layered integrated risk early warning model, and depression subtype classification tags and risk probabilities are obtained; in the pre-training process of the hierarchical integration classification model and the hierarchical integration risk early warning model, multi-modal feature fusion and hierarchical integration learning strategies are adopted, and samples from a plurality of data centers are used for model training and verification. According to the method, the accuracy and individualization degree of classification and early warning are improved, and the generalization ability and robustness of the model are also improved.

The invention relates to the technical field of health risk prediction, in particular to a metabonomics-radiomics prediction method for chronic subdural hematoma recurrence risk, which comprises the following steps: acquiring a CT image and extracting edge gray fluctuation, constructing fluctuation parameters in combination with metabolome data, screening coordination characteristics to generate a risk combination, and predicting a chronic subdural hematoma recurrence risk. Feature pairs consistent in trend are extracted to form a collaborative channel, and a feature matrix is constructed to generate an input vector set; according to the method, disturbance features are extracted through a CT image edge gray level path, a cross-modal fluctuation trend comparison mechanism is established in combination with patient brain metabolism indexes, biological consistency between the features is enhanced, feature combinations with uncoordinated changes are eliminated, feature pairs with collaborative structure and function trends are screened, and a linkage path is constructed. The evolution relation from structural disturbance to metabolic response is reflected, channel data sorting and recombination improve the difference of input characteristics, the stability and accuracy of recurrence discrimination are enhanced, and the systematicness and interpretability of risk assessment are improved.

The invention discloses an application of a training immune agonist in preparation of a drug for sensitizing radiotherapy treatment of tumors, and researches show that the training immune agonist significantly activates immune response of a body by promoting expression of inflammatory cytokines, enhancing phagocytic ability of macrophages, recruiting immune cells and amplifying an immune activation effect, so that the immune response of the body is enhanced. The sensitivity of tumor cells to radiotherapy is improved, and a dual mechanism of'radiotherapy sensitization-immune synergy 'is formed, so that the bottleneck of the traditional radiotherapy curative effect is broken through, the local control rate of radiotherapy is improved, and the recurrence risk is reduced. Experiments prove that the training immune agonist combined with radiotherapy has a remarkable treatment effect on various highly invasive tumors (such as brain glioma and lung cancer brain metastasis) and common malignant tumors (such as colon cancer and lung cancer), and shows wide applicability. The invention provides a new combined therapy direction for tumor treatment, is expected to improve the prognosis of patients, prolong the lifetime and improve the life quality, and has important clinical transformation value.