21 results about "Prognostic models" patented technology

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 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.

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.

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 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 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.

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.

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.

The application discloses an interpretable analysis and decision sharing verification method and system for a colorectal cancer prognosis model, and relates to the field of medical artificial intelligence interpretability. The method comprises the following steps: performing gradient weighted class activation mapping analysis on the prognosis model to generate an image heat map; calculating the contribution degree of multi-modal features by using a SHAP interpreter; constructing an integrated visualization interface to present the patient data, model prediction and the above-mentioned explanation results to doctors; the doctors perform independent risk assessment based on the interface information; and finally, the decisions of the doctors and the model are compared to evaluate the auxiliary performance of the model. Through multi-level explanation and innovative doctor-model decision sharing verification mechanism, the transparency and clinical credibility of the complex AI prognosis model are significantly improved, the value of time series data in dynamic risk assessment can be verified, and the clinical landing application of the AI model is effectively promoted.

The invention discloses a sepsis patient prognosis method and system based on a multi-task Transform model, a storage medium and equipment, and belongs to the technical field of intelligent medicines.The method comprises the following steps that S1, multiple time sequence physiological data of ICU patients are extracted from an electronic health record database; s2, preprocessing the time sequence physiological data; s3, a multi-task Transform prognosis model is constructed on the basis of the preprocessed time sequence physiological data, and different backtracking windows are used for generating input sequences with different lengths according to prediction task types; the prediction task type comprises an acute task and a long-term task; s4, carrying out training on the multi-task Transform prognosis model, and carrying out training on the multi-task Transform prognosis model; and S5, predicting the shock and death risks of the current sepsis patient by using the trained multi-task Transform prognosis model. According to the method, the backtracking windows with different lengths are set for the acute task and the long-term task, so that the method better fits the time sequence characteristics of different clinical events, the prediction accuracy is improved, the method is adaptive to different clinical scene requirements, and the interpretability is high.

The invention relates to a prognosis risk assessment method for an LUAD prognosis model constructed based on CNV-driven FRGs, and the method comprises the following steps: obtaining original data of six preselected genes of a target patient, and determining the six preselected genes based on the CNV-driven FRGs; preprocessing the original data to obtain preprocessed data; respectively calculating TPM values of six preselected genes based on the preprocessed data; inputting the TPM values of the six preselected genes into a pre-established LUAD prognosis model, the LUAD prognosis model being a prognosis risk prediction model constructed based on CNV-driven FRGs, and outputting a risk score by the LUAD prognosis model; and dividing the target patients into corresponding risk groups according to the size difference between the risk score and a preset risk threshold value, if the risk score is greater than the preset risk threshold value, dividing the target patients into a high-risk group, otherwise, dividing the target patients into a low-risk group. According to the method, a clear and reliable decision basis can be provided for formulating a clinical treatment scheme, and the prognosis outcome of the LUAD patient can be finally improved.

The invention provides a triple-negative breast cancer pyroptosis related prognosis model and a construction method and application thereof, and the construction method of the model comprises the following steps: (1) obtaining transcriptome data and prognosis data of triple-negative breast cancer patients and normal people from a database, and analyzing to obtain triple-negative breast cancer differential expression genes; and (2) screening from the database to obtain an intersection of the pyroptosis related genes and the differentially expressed genes, based on the genes in the intersection, screening through Lasso Cox regression analysis to obtain genes related to triple negative breast cancer prognosis, and using the genes to construct a risk scoring model. The model can effectively predict the prognosis of the triple negative breast cancer patient through risk scoring.

The present application relates to the technical field of medical data analysis, and more particularly to a model construction method and system for predicting lung adenocarcinoma osimertinib resistance based on machine learning. By integrating lung adenocarcinoma patient clinical data and public database mRNA data, after pretreatment such as Mann-Whitney U test and FPKM standardization, a basic model driven by clinical indicators is constructed using XGBoost, and a prognosis model driven by gene characteristics is constructed through R package Mime, and TYMS and UAP1L1 are selected as key genes. Through residual correction and stacked integration, the model performance is optimized, and finally the AUC is improved to 0.924. Serum RNA detection verification shows that TYMS and UAP1L1 are significantly highly expressed in the drug resistance group. This method replaces the traditional biopsy by non-invasive serum detection, reduces the detection cost, and the model generalization ability is verified by an independent queue, providing an efficient and accurate solution for early warning and personalized treatment of lung adenocarcinoma patients with osimertinib resistance.

The invention discloses a cervical cancer patient prognosis model construction method based on senescence-related genes, which is characterized in that the senescence-related genes are collected from five databases of Aging Altas, Cell Age, GenAge, LongevityMap and SenMayo, clinical data and gene expression data related to cervical cancer are downloaded from a TCGA database, and 4313 prognosis-related genes are calculated by using a Kaplan-Meier method; the method comprises the following steps: carrying out intersection analysis on senescence-related genes and prognosis-related genes, screening out 28 cervical cancer senescence prognosis-related genes, typing cervical cancer patients by using Consensuses ClusterPlus according to an SSGs expression mode, and constructing a prognosis model by using Lasso regression analysis, and has the advantage that the model can provide simple and effective patient prognosis evaluation and treatment scheme selection guidance for clinic.

The application provides a methylation marker site, a brain stem glioma typing and prognosis model, and belongs to the technical field of gene detection, comprising a plurality of methylation marker sites, a methylation model is constructed based on the methylation marker sites, and the brain stem glioma is typed by using ctDNA in cerebrospinal fluid, combining a methylation model, a model composed of a methylation model and a gene mutation. The methylation marker screened by the application has high specificity in different subtypes of BSG. The machine learning model trained by using the methylation marker has very high accuracy and specificity in combination with mutation information. The model constructed based on the above methylation marker also has high accuracy and specificity for cerebrospinal fluid samples, which provides the possibility for subsequent tumor dynamic monitoring. The prediction result of the methylation data has guiding significance for patient prognosis stratification.

The invention relates to the technical field of biomedicine, in particular to a liver cancer molecular subtype typing and prognosis model construction method based on uric acid metabolism related genes and application of the liver cancer molecular subtype typing and prognosis model construction method. The single cell RNA-seq and batch transcriptome data are integrated for the first time, the liver cancer prognosis model based on uric acid metabolism related genes is constructed, the model comprises eight key biomarkers CAD, CCT3, EFNA3, EZH2, G6PD, PSRC1, SLC2A1 and SPP1, high prediction accuracy is achieved, AUC reaches 0.793 in one year, and a new tool is provided for prognosis evaluation of liver cancer.

The invention belongs to the technical field of biological medicine, and particularly relates to a risk assessment and prognosis prediction method and model for lung cancer based on a CTI index and application. The early screening model constructed by the invention and the gene prognosis model form'early screening-prognosis' full chain support, the early screening model can quickly calculate the CTI value by relying on conventional laboratory indexes, and the early screening model can also be conveniently applied to primary medical institutions to assist in early discovery of lung cancer; the gene prognosis model provides prognosis layering basis for diagnosed patients through core gene expression quantity and risk score, and combination of the core gene expression quantity and the risk score can cover key links before, in and after lung cancer diagnosis and treatment, so that the problem of insufficient accuracy of the traditional early screening means is solved, the defect that the existing prognosis evaluation is difficult to individualize and quantify is overcome, and the diagnosis and treatment efficiency is improved. And complete technical support is provided for accurate prevention and treatment of lung cancer.