136 results about "Recurrence risk" patented technology

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 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 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 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 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 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 provides a mental disorder aided decision-making method and system based on multi-modal data, and belongs to the technical field of disease aided decision-making, the method is applied to a system comprising a data acquisition module, a preliminary screening module and an aided decision-making module, and the method specifically comprises the following steps: preprocessing the multi-modal data of a patient; in combination with the mental disorder risk level of the patient of the preliminary screening model, decision assistance is triggered according to the mental disorder risk level, or corresponding decision suggestions are matched and output; when decision assistance is triggered, the disease classification probability and severity are obtained through a diagnosis model based on a cross-modal attention mechanism, meanwhile, according to a time-dependent risk prediction model, a survival probability curve of a recurrence risk is generated in combination with historical diagnosis data of a patient, and decision assistance suggestions are determined by integrating outputs of the two models. According to the method, on the basis of multi-modal data, multiple types of intelligent models are fused, objective data support is provided in the assessment and intervention process, and the missed diagnosis and misdiagnosis risks of mental disorders are reduced.

The invention discloses a rectal cancer postoperative recurrence risk prediction system and method based on multi-modal time sequence data, and relates to the technical field of medical artificial intelligence. The system comprises a data acquisition and preprocessing module, a feature extraction module and a multi-modal feature fusion and modeling module. The method comprises the following steps: constructing a cross-modal data set containing time sequence clinical data, a time sequence MR image and a biopsy digital pathological image; respectively extracting clinical features, radiomics and deep learning features of the MR image, and nucleus morphology and spatial distribution features of the pathological image; and fusing all the features by using a Transform network, and constructing a prediction model. According to the method, macroscopic images, micropathology and dynamic time sequence information are integrated, tumor heterogeneity is comprehensively quantified, the problem that prediction of a single-mode static model is not accurate is solved, the postoperative recurrence risk of the stage III rectal cancer patient can be evaluated more accurately, and clinical treatment decision making is assisted.

The invention provides a kidney cancer recurrence risk prediction method based on a deep learning model, and relates to the technical field of deep learning, and the method comprises the steps: collecting an image data set for kidney cancer high recurrence risk prediction; carrying out registration on the collected multi-stage enhanced CT image; constructing and training a kidney tumor automatic detection and segmentation model; carrying out ROI positioning cutting and quality control; and constructing a deep learning model for renal cancer recurrence risk prediction based on the multi-modal convolutional neural network, and realizing renal cancer recurrence risk prediction through the constructed prediction network model. According to the method, the multi-phase enhanced CT image of the kidney cancer patient is analyzed through the deep learning model, the tumor postoperative recurrence risk is predicted, an objective basis is provided for a clinician to make an individualized follow-up visit scheme and an auxiliary treatment decision, and excessive treatment of a low-risk patient and insufficient treatment of a high-risk patient are avoided.

The invention discloses a sugar chain marker combination for predicting the recurrence risk of a gastric cancer patient after treatment and application of the sugar chain marker combination. The invention discovers that the levels of six specific N-sugar chains (NGA2F, NG1A2F, NA3, NA3Fb, NA4 and NA4Fb) in blood are obviously related to relapse during definite diagnosis of gastric cancer for the first time. On the basis, a gastric cancer recurrence risk prediction model GC-GPSS is constructed through Cox regression. According to the model, individual risk scores are calculated by using a quantification formula containing a fixed regression coefficient (beta), and patients are divided into a high-risk group and a low-risk group according to a preset threshold. A verification result shows that the model can effectively distinguish patient groups with different recurrence risks. The marker combination, the prediction model and the system provided by the invention provide a brand new tool for realizing earlier and more objective recurrence risk assessment in the early stage of gastric cancer diagnosis and treatment, and have important clinical application value.

The invention discloses a silent pituitary ACTH cytoma identification method. The method comprises the following steps: determining a pituitary neuroendocrine tumor through imaging examination; a serum marker SAA (serum amyloid protein A) index is obtained through blood detection; constructing a silent pituitary ACTH cytoma recognition model by combining serum marker SAA indexes, age, gender, clinical symptoms and various hormone variable characteristics of pituitary; and identifying and judging the silent pituitary ACTH cytoma according to the identification model. The serum SAA is used as an index for identifying the silent pituitary ACTH cytoma, and the serum SAA is used as a mature serum detection index in clinical application, so that a doctor can accurately identify a high-risk subtype of the silent pituitary ACTH cytoma which is a pituitary neuroendocrine tumor, and can adjust an operation strategy according to factors such as invasiveness and recurrence risk of the tumor; therefore, the operation effect is improved, and patient prognosis is improved.

The invention provides a prognosis risk prediction method and device for liver cancer liver transplantation, electronic equipment and a medium, and relates to the technical field of prognosis risk prediction.The method comprises the steps that a liver cancer liver transplantation block set of a target patient is obtained; respectively inputting each liver cancer liver transplantation block in the liver cancer liver transplantation block set into the single-block liver cancer liver transplantation scoring model to obtain a liver cancer liver transplantation score corresponding to each liver cancer liver transplantation block output by the single-block liver cancer liver transplantation scoring model; determining a target liver cancer liver transplantation block according to the liver cancer liver transplantation score corresponding to each liver cancer liver transplantation block and each liver cancer liver transplantation block in the liver cancer liver transplantation block set; and inputting the target liver cancer liver transplantation block into a liver cancer liver transplantation region prognosis analysis model to obtain a liver cancer liver transplantation prognosis recurrence risk prediction result of the target patient output by the liver cancer liver transplantation region prognosis analysis model. The accuracy of risk prediction is improved based on feature structure analysis.

The invention discloses a full-cycle management method for an endometriosis patient, and relates to the technical field of medical informatization decision making, and the method comprises the steps: collecting the preliminary diagnosis disease staging information, lesion feature text and laboratory inspection data of the patient, and generating a case data set through medical term standardized conversion and text structured processing; constructing a recurrence risk prediction model, inputting the case data set into the recurrence risk prediction model, and generating a recurrence risk quantitative score; adjusting a follow-up period based on the recurrence risk quantitative score, setting follow-up time nodes and medical examination items, and combining to generate a follow-up plan; and performing graded reminding based on the follow-up visit plan, and collecting symptom scoring records, medicine use data and life quality evaluation results submitted by the patient to form a follow-up visit data set. The dynamic follow-up visit plan is generated by constructing the recurrence risk prediction model, individualized follow-up visit management is achieved, and the problem that follow-up visit depends on static experience and cannot be matched with a real risk level is solved.

This invention relates to the field of medical technology, specifically to a method for preoperative risk assessment and prediction in liver transplant patients with hepatocellular carcinoma, comprising the following steps: Sample collection: selecting plasma samples and corresponding clinicopathological information from liver transplant recipients of hepatocellular carcinoma, and clarifying the inclusion and exclusion criteria for samples; Plasma cell-free DNA extraction and whole-genome sequencing: extracting and quality-controlling cell-free DNA from the plasma samples collected in step S1, constructing a sequencing library, and performing low-coverage whole-genome sequencing. This invention utilizes plasma-extracted cfDNA for whole-genome sequencing, combined with clinical testing information, to construct a preoperative risk assessment and prediction model for postoperative recurrence in liver transplant recipients of hepatocellular carcinoma based on non-invasive testing. This model can be used to predict the probability of recurrence-free survival before liver transplantation. The model derivation cohort integrates clinical records and circulating tumor DNA data for preoperative recurrence risk prediction.

The invention discloses a dynamic prediction method for a rheumatoid arthritis patient based on multi-modal data fusion and deep learning, and the method is characterized in that a cross-modal attention fusion module is adopted, and the module does not simply splice all features, but learns the mutual dependency relationship between different modal features, and is used for predicting the dynamic prediction of the rheumatoid arthritis patient. Generating an enhanced feature vector capable of representing the synergistic effect; then, the enhanced feature vector and each original modal feature are sent into a gating fusion unit together to adaptively screen and integrate the most critical information for the current prediction task to form a comprehensive'patient current state snapshot '; and finally, inputting a series of state snapshots arranged according to a time sequence into an upper-layer time sequence LSTM model, thereby realizing dynamic prediction of a future disease activity trajectory and a recurrence risk of the patient. According to the method, through a refined fusion strategy and time sequence dynamic deep modeling, the prediction accuracy and interpretability are remarkably improved.

The application discloses a tumor recurrence risk prediction method and system based on electronic medical record data, relates to the field of electronic medical record data processing and analysis, and can realize individualized recurrence risk assessment based on multidimensional characteristics, significantly reduces subjective judgment errors, and establishes a recurrence risk mapping model through structured processing of historical electronic medical record data; a risk grade stratification mechanism can automatically distinguish patients who need emergency intervention from patients who need routine monitoring, avoids excessive medical treatment or delayed treatment, and is especially suitable for chronic diseases such as tumors that need long-term management; through periodic marker measurement and risk grade feedback, a closed-loop management of 'assessment-intervention-reassessment' is formed, meeting the needs of continuous optimization of clinical diagnosis and treatment; in addition, through preprocessing, multidimensional characteristics after preprocessing can be directly called in the subsequent process, and repeated data cleaning work is avoided.

The invention discloses a dynamic diet risk assessment method and query system for pancreatitis, and the method comprises the steps: carrying out the multi-source heterogeneous collaborative analysis of feedback data collected by a mobile terminal and hospital discharge guide data through employing a distributed database cluster architecture, and constructing a dynamic risk prediction model based on a Spark Streaming real-time calculation engine and an LSTM neural network. And generating taboo period suggestions and risk compensation coefficients by quantitatively processing time sequence interaction association characteristics among the discharge days, the eating response types and the individual health parameters, and designing a closed-loop feedback mechanism to drive dynamic iterative optimization of the model. According to the system, automatic synchronization of user feedback data and a database cluster is achieved through a Kafka message queue, cooperative processing of MongoDB document rules and Redis real-time sorting is achieved depending on a differential storage engine, and personalized diet risk quantitative evaluation for dynamic changes of pancreatitis patients in the rehabilitation stage is achieved. The taboo period prediction accuracy is improved, the recurrence risk is reduced, and the generalization ability of the system is continuously optimized through a sliding window triggering mechanism.

The application discloses a lumbar disc herniation postoperative recurrence risk prediction system based on domain offset correction, and relates to the technical field of medical data processing and clinical disease risk prediction. The system obtains the multi-modal clinical and imaging feature vectors and follow-up outcome data of a training queue and an independent verification queue, trains a central classifier to quantize and correct the multi-center data distribution difference, calculates and trims the importance weight of each training case, trains a survival analysis base learner based on the processed weight and performs stacked integration, determines a final deployment model in combination with an independent verification case queue, calls the final deployment model to perform reasoning on a target patient after lumbar disc herniation surgery, and outputs an individualized recurrence risk score, risk stratification and recurrence-free survival curve, thereby improving the robustness and reliability of the risk prediction system in a cross-center scenario, and being suitable for postoperative follow-up management of lumbar disc herniation and auxiliary clinical decision-making.

The invention relates to the technical field of biological medicine, in particular to application of S100A9 protein to preparation of a product for predicting the recurrence risk of AML. The product is used for detecting a marker CD15, a marker CD33, a marker CD14, a marker CD15, a marker CD33, a marker CD64, a marker CD5, a marker CD14, a marker CD10, a marker CD19, a marker CD33, a marker CD34, a marker CD64, a marker CD117, a marker CD13 or a marker CD45. Therefore, the accuracy of predicting the AML recurrence risk is improved.

The invention relates to application of a reagent for evaluating the recurrence risk of epithelial ovarian cancer, and belongs to the technical field of molecular biology. The application specifically refers to application of a reagent for specifically detecting the expression level of SNORD18C and / or SNORD84 in an epithelial ovarian cancer operation tissue sample in preparation of a kit for evaluating the recurrence risk of an epithelial ovarian cancer patient subjected to resection. The change of the expression quantity of SNORD18C and SNORD84 in an epithelial ovarian cancer operation tissue sample is found, and the change of the expression quantity is closely related to the recurrence risk of an epithelial ovarian cancer patient after the epithelial ovarian cancer patient is subjected to resection. Therefore, by detecting the expression of SNORD18C and / or SNORD84 in an epithelial ovarian cancer tissue sample and combining the binomial logistic regression model, the recurrence risk of an epithelial ovarian cancer patient after the resection is assessed, and high sensitivity and specificity are achieved.

The application belongs to the field of biomedicine, and particularly relates to application of CRYL1 protein in preparation of products for evaluating, diagnosing or assisting in diagnosing postoperative recurrence risk of calcium oxalate kidney stone combined with renal papillary calcium plaque, and a sequence of the CRYL1 protein is shown as SEQ ID NO. 1. The application firstly finds that expression of CRYL1 protein in renal tubular and collecting duct cells of renal papillary calcium plaque tissue is reduced, and it is proved through experiments that urine CRYL1 protein is a postoperative recurrence predictor and prediction marker of patients with CaOx kidney stone combined with renal papillary calcium plaque, and a CRYL1 protein detection kit is further developed for detecting or evaluating postoperative recurrence risk of patients with CaOx kidney stone combined with renal papillary calcium plaque, which promotes personalized follow-up and prevention scheme of the patients, and has great application prospect in the clinic.

The invention belongs to the field of myocardial infarction diagnosis and research, and particularly relates to a serum lipid biomarker for predicting the recurrence risk of myocardial infarction and application of the serum lipid biomarker. The invention discloses a serum lipid biomarker for predicting the recurrence risk of myocardial infarction. The serum lipid biomarker for predicting the recurrence risk of myocardial infarction is PI (18: 2), PI (16: 0), TG (20: 2), PC (22: 4) and PI (22: 6). According to the method, through lipid deconstruction analysis with higher resolution, lipid molecules of a specific chain structure closely related to the PRMI risk can be identified, and more accurate prediction of the recurrence risk is realized. The deconstructed lipid molecular marker provided by the invention has higher specificity and reliability in clinical application, not only can make up for the defects of the traditional lipid marker, but also provides a brand new technical means and application prospect for early recognition and individualized intervention of AMI postoperative recurrence risk.

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 relates to the technical field of medical information, in particular to a tumor postoperative follow-up visit method and device, electronic equipment and a storage medium, and the method comprises the steps that firstly, an illness state file is acquired, and the illness state file comprises multiple pieces of illness state data representing preoperative states and multiple pieces of illness state data representing postoperative states; then performing redundancy item reduction on the illness state file by using a redundancy reduction matrix, and constructing the reduced illness state file into a first feature vector; the first feature vector is input into a risk assessment model, a risk assessment comparison table is obtained, and the risk assessment comparison table comprises a plurality of data pairs representing the corresponding relation between follow-up visit opportunities and recurrence risk assessment values; and finally, according to the risk assessment comparison table, determining a follow-up opportunity. The risk estimation value is given based on the illness state file, the follow-up time is selected according to the estimation value, the risk of the patient is controllable, and medical resources are saved to the maximum extent.

The invention belongs to the field of biological medicines, and particularly relates to application of a compound to preparation of a medicine for treating papilloma of the upper respiratory tract. The invention provides an application of a compound in preparation of a medicine for treating and / or preventing papilloma of the upper respiratory tract, and is characterized in that the compound comprises one or more of asperisib, crizotinib, lapatinib, osimertinib and bortezomib. Experimental results show that the asperisib, crizotinib, lapatinib, osimertinib and bortezomib can realize long-term control on the papilloma of the upper respiratory tract and reduce the recurrence risk of the papilloma of the upper respiratory tract to a certain extent, so that the life quality of patients with the papilloma of the upper respiratory tract can be improved; the invention has important scientific value and clinical application prospect in the field of upper respiratory papilloma treatment.

A lymphoma recurrence risk prediction method, device and equipment based on MRD are disclosed, comprising: acquiring clinical feature information related to diffuse large B-cell lymphoma; in response to the clinical feature information, calculating a plurality of baseline clinical features through a structured constraint target Bayesian network model to obtain an initial recurrence risk probability of diffuse large B-cell lymphoma of a target object; the structure constraint is used to prohibit direct connection between each baseline clinical feature; according to the initial recurrence risk probability, the state of minimal residual disease related to diffuse large B-cell lymphoma and high-risk pathological factors, the multidimensional calculation of recurrence risk is carried out through the target random forest model to obtain the final recurrence risk probability of the target object; according to the final recurrence risk probability, the recurrence risk information of diffuse large B-cell lymphoma of the target object is generated. To improve the interpretability and accuracy of lymphoma recurrence risk prediction.