67 results about "Prognostic models" patented technology

The invention discloses a renal clear cell carcinoma prognosis prediction method based on multi-mode MRI (Magnetic Resonance Imaging) image and digital pathological omics fusion. The method comprises the following steps: S1, collecting a training data set based on an MR image and a pathological image; s2, feature extraction of MR radiomics; s3, deep learning feature extraction of the pathological image; s4, an MR-pathological feature fusion module; and S5, deploying the network. According to the method, depth features with prognosis information are obtained from two scales of pre-treatment images and post-operation pathology, effective features are extracted by adopting image omics and a convolutional neural network mode according to data characteristics of MR images and pathology images, and depth fusion of the two types of features is completed in a hidden space through a multi-task guiding mode, so that the accuracy of the MR image and the pathology image is improved. A precise prognosis model with multi-scale information is provided, and the method has a relatively strong clinical application prospect and is of great significance for realizing precise immunotherapy and improving prognosis of a patient.

The invention discloses an interpretability analysis and decision sharing verification method and system for a rectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: carrying out gradient weighting class activation mapping analysis on a prognosis model to generate an image thermodynamic diagram; calculating the contribution degree of the multi-modal features by using an SHAP interpreter; an integrated visual interface is constructed, and patient data, model prediction and the explanation result are presented to a doctor together; the doctor performs independent risk assessment based on the interface information; finally, decisions of doctors and the model are compared, and model auxiliary efficiency is evaluated. Through a doctor-model decision sharing verification mechanism which is explained and innovated in a multi-level mode, the transparency and clinical credibility of the complex AI prognosis model are remarkably improved, the value of time sequence data in dynamic risk assessment can be verified, and clinical landing application of the AI model is powerfully promoted.

The invention discloses a breast cancer neoadjuvant chemotherapy pyroptosis related prognosis model construction method which comprises the following steps: obtaining an original data set, and preprocessing to obtain an annotated whole-gene expression matrix and a survival clinical information matrix; screening out a gene database having significant differential expression on patient survival; obtaining a pyroptosis related gene database; taking an intersection of the pyroptosis related gene database and the gene database having significant differential expression on the survival of the patient to obtain a list of differential expression genes related to the survival of the pyroptosis; and carrying out disease survival prediction model batch construction on the differential expression gene list, screening out two optimal disease survival prediction models as sub-models, and weighting to obtain a prognosis model. According to the method, the problem that gene pathways of a prognosis model of the breast cancer neoadjuvant chemotherapy patient are deficient is solved, and the prognosis prediction capability of the breast cancer patient receiving the neoadjuvant chemotherapy is expected to be improved.

A method for analyzing brain imaging data from a brain lesion patient and generating prognostic information is disclosed. The method includes obtaining imaging data of the brain of a patient from a scanner and classifying the data by acquisition plane and modality to select a data series of interest. The method further involves processing the data series of interest to generate brain masks and corresponding volumetric data. The brain masks and corresponding volumetric data are then co-registered to a common brain space, and registered volumetric data is generated. Lesion segmentation is performed to generate a lesion mask in the common brain space. The lesion mask data is applied to a trained prognostic model, which comprises a machine learning model configured to output a probability-based prognosis of the patient's brain lesion.

The invention provides a glycolipid metabolism related gene-based endometrial cancer prognosis prediction model construction method, which comprises the following steps of 1, acquiring data containing gene expression and clinical information, and preprocessing the data; 2, differential expression and prognosis gene screening; 3, constructing a prognosis model; 4, analyzing model gene enrichment; 5, evaluating the immunocompetence of the two GLRG related dangerous groups; and step 6, statistical analysis. According to the technical scheme, more accurate and reliable prognosis evaluation is provided for endometrial cancer by comprehensively analyzing multi-dimensional information such as gene expression, immune characteristics, mutation characteristics and drug sensitivity.

The invention relates to the technical field of medical data analysis, in particular to a lung adenocarcinoma osimertinib drug resistance prediction model construction method and system based on machine learning. Clinical data of lung adenocarcinoma patients and mRNA (messenger ribonucleic acid) data of a public database are integrated, after Mann-Wh itney U inspection, FPKM standardization and other preprocessing are carried out, XGBoost is utilized to construct a basic model driven by clinical indexes, a prognosis model driven by gene characteristics is constructed through R packet Mime, and TYMS and UAP1L1 are screened out to serve as key genes. The performance of the model is optimized through residual correction and stacking integration, and finally the AUC is improved to 0.924. Serum RNA (Ribonucleic Acid) detection and verification show that TYMS and UAP1L1 are remarkably and highly expressed in a drug-resistant group. According to the method, traditional biopsy is replaced with noninvasive serum detection, the detection cost is reduced, the model generalization ability is verified through an independent queue, and an efficient and accurate solution is provided for early warning and personalized treatment of osimertinib drug resistance of the lung adenocarcinoma patient.

The invention relates to the technical field of medical treatment, and particularly discloses a compound prognosis model for cancer prognosis evaluation, a prognosis evaluation method and a calculator. The compound prognosis model is constructed based on inflammation burden related prognosis indexes ALNCR and TNM by stage combination; the creation method comprises the following steps: step 1, collecting albumin concentration, lymphocyte count, neutrophil count and C-reactive protein concentration data of a cancer patient; 2, calculating an ALNCR index according to a formula; 3, according to a clinical classic TNM staging system, the patients are divided into a TNM staging stage I, a TNM staging stage II, a TNM staging stage III or a TNM staging stage IV; 4, the ALNCR index obtained in the step 2 and the TNM staging result obtained in the step 3 are associated and integrated, and the individualized prognosis score of the ALNCR.TNM composite prognosis model is obtained through calculation. The method is suitable for clinical rapid application; high-risk patients can be accurately distinguished, a quantitative basis is provided for individualized treatment and follow-up visit strategy formulation, and excessive medical treatment or insufficient treatment is reduced.

A disease prognosis model to may be trained to determine the clinical outcome of a disease based on longitudinal data including a health record for each timepoint in a sequence of timepoints. The disease prognosis model may include a recurrent neural network trained to extract, from each health record, a feature set representative of local dependencies present within the health record. The disease prognosis model may include a spacetime attention trained to determine an importance of each feature in the feature set at each timepoint in the sequence of timepoints. The disease prognosis model may include a feedforward neural network trained to determine, based on the importance of each feature in the feature set at each time point in the sequence of timepoints, the clinical outcome of the disease. The trained disease prognosis model may be applied to determine the clinical outcome of the disease for one or more patients.

The present disclosure provides techniques for the deployment of prognostic models into production framework. A machine learning (ML) model trained to predict condition-based aircraft maintenance is accessed. A set of configuration files for executing the ML model are generated based on one or more predefined templates, where the set of configuration files is structured in a hierarchical form based on one or more execution environments for the ML model. The ML model is deployed based on the set of configuration files. Upon determining that a runtime trigger specified in the set of configuration files is activated, input data from a client system is accessed, one or more predictions are generated by processing the input data using the deployed ML model, one or more alert criteria are determined as satisfied based on the predictions, and one or more alerts are output via one or more notification channels.

The invention discloses an IgA nephropathy prognosis method, system and device based on multi-modal data, and belongs to the technical field of image data processing, the method comprises the following steps: collecting historical data including clinical data and an IgA immunofluorescence map; based on a visual identification method, pathological features are extracted from the IgA immunofluorescence image; screening clinical characteristics; based on a machine learning method, the training set is trained according to pathological features and clinical features, a prognosis model is obtained, and the prognosis model is used for IgA nephropathy prognosis. On the basis of a computer vision method, pathological features are extracted from an IgA immunofluorescence map, and the prognosis of the IgA nephropathy is predicted in combination with clinical features, so that automatic prediction is facilitated, mistakes and omissions caused by artificial naked eye recognition are avoided, stable prediction ability is expressed, and multi-modal data fusion reflects a gain effect on long-term prognosis prediction.

The invention discloses an AI auxiliary radiotherapy dose intelligent optimization device, which comprises a multi-modal image fusion module, a dose distribution prediction module, a dynamic adaptive optimization module and a prognosis model integration module, and is characterized in that the multi-modal image fusion module is used for aligning anatomical features of different images, eliminating respiratory motion artifacts and predicting the dose distribution of the different images; the dose distribution prediction module is used for rapidly predicting radiotherapy dose distribution based on anatomical features and historical data, the dynamic adaptive optimization module is used for monitoring anatomical changes in real time and dynamically adjusting a dose plan, and the prognosis model integration module is used for quantifying correlation between dose distribution and radioactive injury risks. High-precision alignment and respiratory motion artifact elimination of an anatomical structure are achieved through the multi-modal image fusion module, three-dimensional dose calculation is rapidly and accurately conducted through the dose distribution prediction module, organ displacement and deformation are effectively coped with through the dynamic self-adaptive optimization module through a real-time image monitoring and reinforcement learning algorithm, and the accuracy of the three-dimensional dose calculation is improved. And the prognosis model integration module constructs an individualized risk prediction model.

The invention discloses a method for predicting the survival rate of a patient with heart implantation equipment, which is used for predicting the survival rate of the patient with the heart implantation equipment through a trained heart implantation prognosis model. The cardiac implantation prognosis model construction method comprises the following steps: obtaining clinical data of a patient with cardiac implantation equipment, and preprocessing the clinical equipment; selecting prediction features for predicting the survival rate of the patient with the heart implantation equipment from the existing clinical features; taking the patient data with the prediction features as a training sample set to train a prognosis model based on a multi-layer perceptron model so as to obtain the prognosis model; the prognosis model is optimized by adopting 5-fold cross validation.

The invention provides a biomarker, a model and a system for prognosis evaluation of acute myelogenous leukemia and application of the biomarker, the model and the system. According to the invention, a prognosis model for evaluating the risk of acute myelogenous leukemia is constructed by using a biomarker consisting of GZMB, OPTN, DNM1, MPO and GZMH genes. The method comprises the following steps: extracting marrow fluid nucleated cell RNA of a patient with acute myelogenous leukemia, detecting the relative expression quantity of the five genes in a quantitative detection manner, substituting into a constructed risk prediction model, and performing risk prognosis evaluation according to scores. The method is low in detection cost, low in technical threshold and better in prediction performance, and has independent prognosis evaluation value.

Aspects related to a prognostic compliance model using real-time information taxonomy are provided. A modeling platform may access regulation information associated with an event processing request. The platform may generate a prediction summary using a prognostic model. The prediction summary may include a predicted event processing request and information of one or more potential conflicts. The platform may generate, using the prognostic model and based on the prediction summary, a conflict alert. The conflict alert may correspond to the event processing request or predicted event processing request. The platform may output the conflict alert to a user device. The platform may receive feedback information corresponding to the conflict alert. The platform may update the prognostic model based on the feedback information.

The application provides a model construction and verification method and nomogram for predicting the clinical prognosis of SFTS patients complicated with AP. By collecting the clinical data of SFTS patients, the independent risk factors related to adverse prognosis are screened out by using LASSO regression analysis, a multi-factor Logistic regression model is constructed, and the model is visualized by nomogram for predicting the adverse prognosis of SFTS patients complicated with AP. The model has high discrimination and calibration, and can provide an effective prediction tool for clinicians to optimize clinical decision-making.

The invention discloses application of a lactic acid modified gene ENO1 inhibitor in preparation of a medicine for treating nasopharynx cancer, and relates to the technical field of biological medicines. A head and neck squamous cell carcinoma prognosis model containing seven lactylation related genes is constructed by analyzing a TCGA database, the model can effectively distinguish patient risks, the total lifetime of a high-risk group is remarkably shortened, and a risk score is an independent prognosis factor; the functions of the key gene ENO1 are deeply studied through in-vitro experiments, and experimental results show that silencing of the ENO1 gene in nasopharynx cancer 5-8F cells causes up-regulation of cell pan-lactylation level, up-regulation of MMP2 expression and down-regulation of E-cadherin expression, and migration and invasion ability of tumor cells is significantly enhanced. The prognosis model provided by the invention has an important clinical prediction value, inhibition of ENO1 can promote tumor progression through abnormal lactylation, and a new thought is provided for taking ENO1 as a treatment target.

The invention discloses an intensity modulated radiotherapy plan optimization method based on guidance of an individual prognosis prediction model. The method comprises the following steps: (1) establishing a prognosis model of radiotherapy of a patient; (2) taking the best patient prognosis as optimization main guidance, and combining the prognosis model established in the step (1) and a dose statistical index to construct a target functional expression; and (3) setting parameters for the objective function expression in the step (2) and carrying out numerical optimization solution to obtain an intensity modulated radiation therapy optimization plan. A reverse optimization objective function of radiotherapy plan design is formed by weighting a physical statistical item of dose distribution and an individual prognosis calculation item capable of causing a patient, and radiotherapy plan dose distribution approaching an optimal objective is automatically searched through a corresponding method for solving an optimization problem. And the radiation field intensity distribution of the therapeutic machine corresponding to the dose distribution is calculated, so that the radiotherapy plan design target is sublimated from the optimal physical dose statistical distribution to the optimal final treatment result.

The invention discloses a cerebral apoplexy prognosis early warning system based on CTP image omics and machine learning, and the system comprises a data collection module which is used for obtaining and standardizing the CTP image data and clinical data of a patient; the radiomics feature extraction module is used for extracting a high-dimensional feature set containing morphological features, textural features and hemodynamic functional features; the machine learning prognosis model module is used for fusing the high-dimensional feature set and the clinical data into a training feature matrix; and a prognosis early warning output module. According to the method, a multi-dimensional and high-dimensional feature set including form, texture and functional parameters is automatically extracted from a CTP image through a radiomics technology, focus microcosmic heterogeneity and hemodynamic information which cannot be recognized by human eyes are deeply excavated, deep features and clinical data are fused, learning is performed by using a machine learning model, and the accuracy and accuracy of the fusion of the deep features and the clinical data are improved. Therefore, the dependence of doctors on personal experience is effectively reduced, and the prognosis early warning result is more objective.

The invention discloses a kidney cancer survival prognosis model with cell death-immune characteristics constructed based on machine learning combination and a small molecule drug screening method, the method adopts 101 machine learning algorithm combinations to analyze gene expression data of KIRC patients, screens out genes closely related to two PCD modes, namely Anikis and LDCD, and finally identifies two key genes, namely HMOX1 and PIK3CG. Finally, it is found that SB505124, Carnosol and PROTAC can be used as an immunotherapeutic agent of KIRC.

The invention discloses an infant AML prognosis model constructed by integrating transcriptomics and machine learning and a construction method of the infant AML prognosis model. The method comprises the following steps: collecting clinical data and whole genome transcriptome data of an infant AML patient; identifying difference up-regulation expression genes of infant AML relative to healthy control and old children AML in the discovery set, and screening intersection genes of the genes and an external verification set; carrying out model construction on the obtained gene by taking the progression-free lifetime of the patient as an outcome, generating a plurality of algorithm combinations based on a machine learning algorithm, and calculating a C-index index of each combination; determining a model with the highest C-index mean value in the internal verification set and the external verification set as an optimal model, calculating a risk score IPScore of each patient by using the model, and performing evaluation in the verification set; and dividing the patients into a low-risk group and a high-risk group according to IPScore by utilizing the optimal cutoff value 0.42, namely an IPGroup model. The model can accurately and effectively predict the prognosis of the infant AML patient, and has good clinical practicability.

The present invention relates to a method based on miRNA biomarkers and clinical data for risk stratification of non-metastatic breast cancer of any molecular subtype according to the risk of developing remote metastasis, preferably in a fresh frozen sample or formalin-immobilized paraffin embedded (FFPE) sample, or in a liquid biopsy sample (serum or plasma sample). More particularly, the present invention relates to the use of 2-miRNA profiles in a prognostic model comprising two miRNAs and biomarkers that have been applied to clinical management of breast cancer patients.

The invention discloses a pAML SCT risk stratification prognosis model and a construction method thereof, and belongs to the field of children acute myelogenous leukemia stem cell transplantation risk prognosis research. Clinical data of a pAML patient are collected and analyzed by adopting a statistical method, a risk stratification model is established according to prognosis factors including combination of diagnosis age, KMT2A-r state and MRD1 state, and through verification, the pAML SCT risk can be effectively predicted by adopting the total lifetime prognosis model and the event-free lifetime prognosis model; the risk group of the patient can be determined according to the diagnosis age, the KMT2A-r state and the MRD1 state, so that a clinician is assisted in judging clinical development and disease outcome of the patient, precise layered treatment is realized, and the doctor can guide and / or identify candidates suitable for stem cell transplantation and formulate a treatment scheme.

The invention discloses a construction method of an ALS prognosis model based on chest CT skeletal muscle quantitative parameters. The method comprises the following steps: firstly, performing regular follow-up visit on an ALS receptor which is clearly diagnosed; imageJ software is used for quantifying skeletal muscle area / index / attenuation values (SMD / SMD / SMI) and paravertebral muscle area / attenuation values (PMA / PMD) of the first lumbar vertebra (L1) level of chest CT of all receptors and performing inter-group difference analysis, and it is found that the consistency of L1SMA / SMD / SMI / PMA / PMD of an ALS receptor group is reduced along with staging progress. According to the method, an ALS death risk prediction model is constructed through a single-factor and multi-factor COX proportional risk model, and a column diagram is drawn, so that L1SMA (HR is equal to 0.96, 95% CI is equal to 0.94-0.98, p is equal to 0.001), L1SMD (HR is equal to 0.92, 95% CI is equal to 0.88-0.96, plt; 0.001) and L1PMA (HR = 1.06, 95% CI = 1.01-1.11, plt; 0.022) significantly influences the survival rate of ALS, and areas under curves (AUC) of one-year survival prediction and three-year survival prediction are 0.687 and 0.851 respectively.

The invention relates to the technical field of medicine, discloses a systematic calculation method combining network pharmacology and pan cancer analysis, and relates to data extraction of TCMSP and Swiss Target Prediction databases, Cytoscape network construction and core gene screening, GO / KEGG enrichment analysis, TCGA data analysis, Cox regression prognosis model construction and XGBoost indication prediction model development. According to the method, screening and mechanism exploration of potential cancer indications are achieved through multi-dimensional data analysis and a machine learning algorithm, and the method has remarkable clinical application value and scientific research guiding significance.

Fault diagnosis and prognosis (FDP) is critical for ensuring system reliability and reducing operation and maintenance (O&M) costs. Lebesgue sampling based FDP (LS-FDP) is an event-based approach with the advantages of cost-efficiency, uncertainty management, and less computation. In previous works, LS-FDP approaches are mainly model-based. However, fault dynamic modeling is difficult and time consuming for some complex systems and this severely hinders the applications of LS-FDP. To address this problem, this present disclosure presents a data-driven based LS-FDP framework in which deep belief networks (DBN) and particle filter (PF) are integrated to achieve fault state estimation and remaining useful life (RUL) prediction. In the proposed approach, DBN learns the state evolution model and the Lebesgue time transition model, which are used as diagnostic and prognostic models in PF for FDP. The proposed approach has higher efficiency in terms of computation and better performance in terms of FDP accuracy and precision.

The invention discloses a construction method of a circulating microorganism abundance prognostic scoring model. The construction method comprises the following steps: carrying out univariate COX regression analysis; carrying out LASSO COX regression analysis and cross validation; carrying out multivariable COX regression analysis; an MAPS model is constructed; determining an optimal cutoff value; evaluating the performance; and clinical correlation analysis. The method has the beneficial effects that microorganism characteristics and clinical factors are comprehensively considered, and more comprehensive information is provided for the prognosis model; a multi-level screening process is adopted, so that the risk of overfitting is effectively reduced; dynamic evaluation and personalized grouping are realized; the robustness of the model is ensured by applying an efficient evaluation and verification method; the clinical application value of the model is improved; the method has good adaptability and expansibility; the method is deeper in the aspect of microbial feature mining; personalized evaluation better fits the reality of the patient; the improvement of reasonable distribution of medical resources is facilitated; and multidisciplinary cross fusion is promoted.

A method performed by a driver intention prediction device (10) for predicting a driver's intention, the method comprising: receiving (S600) driving environment information including lane information and driving trajectory information, receiving (S600) driving information, outputting (S610) a lane feature vector by inputting the lane information to a first encoder, outputting (S610) a trajectory feature vector by inputting the driving trajectory information to a second encoder, and predicting (S640) the driver's intention by inputting a plurality of feature vectors to a driver intention prediction model.

Cumulus cell (CC) gene expression is explored as an additional method of morphological scoring to select an embryo with the highest chance of pregnancy. The present invention relates to a novel method for identifying biomarker genes for assessing the ability of mammalian oocytes to produce viable pregnancy after fertilization based on the use of live birth and embryonic development as endpoint criteria for the oocytes for exon level analysis of potential biomarker genes. The invention further provides biomarker genes of CC expression thus identified, as well as prognostic models based on biomarker genes identified using the methods of the invention.