711 results about "Tumor patient" patented technology

The invention discloses a blood tumor patient clinical data analysis method and system based on a knowledge graph, and relates to the technical field of medical information processing. The method comprises the following steps: analyzing an unstructured text by utilizing a natural language processing model, and constructing a standardized entity set; aggregating laboratory indexes through a dynamic window, and constructing a time axis data set for marking a treatment stage; a standardized entity is integrated based on a blood tumor ontology model, a dynamic global knowledge graph is constructed in combination with a clinical causal relationship and a time sequence path, and chain reaction early warning is realized; based on the real-time patient sub-atlas, successful cases with the same molecular features are retrieved in the knowledge atlas to generate a candidate treatment scheme set, a graph neural network trained through an optimization algorithm is utilized to predict the complication risk of each scheme, and finally the optimal treatment scheme is evaluated and selected; according to the method, a clinical data barrier is broken through, dynamic reasoning, advanced risk early warning and personalized scheme optimization of the whole treatment cycle are realized, and the diagnosis and treatment accuracy, safety and medical resource utilization efficiency are remarkably improved.

The invention provides an intelligent tumor nursing monitoring system. The intelligent tumor nursing monitoring system comprises a data acquisition module, a data processing module, a dynamic evaluation module, a multi-stage early warning module and a self-adaptive control module. The data acquisition module acquires physiological sign data, tumor local image data and body fluid biochemical data of a patient in real time through a multi-source biosensor. And the data processing module performs space-time alignment, noise suppression and cross-modal feature association on the multi-modal data to generate a comprehensive feature map. The dynamic evaluation module calculates the tumor progress rate and the complication risk coefficient based on the map, and generates a personalized risk prediction curve. And the multi-stage early warning module triggers an early warning signal according to the curve and generates a risk traceability report. And the adaptive control module generates a personalized scheme based on the early warning signal and the traceability report and controls the medical equipment to execute closed-loop feedback adjustment. The nursing quality and the treatment effect of tumor patients can be improved, and the medical risk is reduced.

The invention discloses an intelligent remote follow-up visit method and system for tumor patients, and relates to the technical field of medical intellectualization, and the method comprises the steps: collecting follow-up visit data through a remote medical terminal, carrying out the processing of the follow-up visit data, obtaining a physical sign index sequence and a symptom feature vector, and generating a dynamic follow-up visit evaluation score through a time sequence prediction model; performing semantic analysis on the electronic medical record data of the patient based on the medical knowledge map, extracting tumor stage classification data, chemoradiotherapy measurement data, weight nutrition indexes and laboratory inspection data, determining a basic risk coefficient, a cumulative dose ratio and a body state score, and generating a baseline follow-up assessment score through a prediction model; and according to the dynamic follow-up evaluation score and the baseline follow-up evaluation score, calculating a comprehensive follow-up grade and generating a next follow-up plan. According to the invention, the accuracy of patient state evaluation is improved, accurate configuration of medical resources is realized, and the quality and efficiency of remote follow-up visit are effectively improved.

The invention discloses a tumor patient medication side effect management method and system based on big data analysis, and the method comprises the following steps: collecting and preprocessing patient data and drug data, and extracting patient features and drug features, the patient data comprising genome data provided by a patient; constructing a prediction model which comprises a time sequence analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model and is used for predicting risk scores of different types of side effects of the patient by taking the drug characteristics and the patient characteristics as input of the prediction model; inputting the basic information, the medication information and the genome data of the current patient into the trained prediction model to obtain risk assessment of various side effects of the current patient; according to the risk assessment result, personalized adjustment suggestions suitable for the patient are selected from a preset intervention scheme library and provided for the current patient. The scientificity and accuracy of administration side effect management in the treatment stage can be effectively improved.

The invention provides a tumor patient clinical test matching system and method based on a large language model and an OCR technology, and is applied to the field of medical data processing. The method comprises the following steps: analyzing clinical data and test information, processing an unstructured text, and generating structured clinical feature data through context association analysis; key data is extracted and subjected to double verification correction, and structured data supplementary information is generated; enhancing the structured clinical feature data and supplementary information based on a multi-modal processing assembly line module, extracting an image quantitative index, analyzing an immunohistochemical result, and generating a comprehensive matching score; through a rule engine and semantic similarity calculation, item-by-item comparison of patient features and entry and exhaust conditions is realized, and a preliminary matching result is generated; edge case misjudgment is corrected through context-aware multi-round reasoning, sorting is adjusted in combination with clinical test priority weights, and an optimized clinical test matching list is generated; and generating a clinical test matching report based on the data.

The invention belongs to the field of antibody preparation and protein detection, and particularly relates to a monoclonal antibody pair for detecting IGFBP2 protein or IGFBP2 polypeptide and application of the monoclonal antibody pair. The monoclonal antibody pair provided by the invention comprises a first antibody and a second antibody, the first antibody comprises a 14F8 antibody or a 8F1 antibody; the second antibody comprises a 6F2 antibody or a 9G1 antibody. According to the present invention, the amino acid sequences of the complementary determining regions CDR1, CDR2 and CDR3 of the light chain and heavy chain variable regions of the monoclonal antibody are determined, the IGFBP2 protein or IGFBP2 polypeptide can be specifically detected, the specificity is strong, the sensitivity is high, the IGFBP2 protein in human serum can be detected, and the basis is provided for the diagnosis and treatment of clinical tumor patients.

The invention discloses an intelligent nutrition and metabolism management system for tumor patients, and particularly relates to the technical field of intelligent management. Comprising a patient treatment duration division module, a patient treatment nutrition and metabolism information acquisition module, a patient treatment nutrition intelligent management module, a patient metabolism risk intelligent evaluation module, an auxiliary decision data analysis module, a patient treatment effect intelligent early warning module and a patient treatment nutrition and metabolism intelligent optimization module. According to the invention, the nutrition and metabolism monitoring data of each sub-period of the disease treatment duration of the target patient is collected, the nutrition state evaluation index and the metabolism function risk management index are calculated, and the patient treatment effect early warning index is obtained through analysis, so that the nutrition and metabolism conditions of the patient are intervened, the treatment effect evaluation accuracy is improved, and the patient treatment efficiency is improved. Waste of medical resources is reduced, and intelligent development of treatment effect decision making is promoted.

The invention discloses a tumor risk layering method and system based on multi-modal imaging omics, and is applied to the technical field of medical image processing, and the method specifically comprises the steps: delineating a region of interest of a tumor according to the clinical medical information of a patient; extracting radiomics features in the region of interest; screening the image omics characteristics to obtain updated image omics characteristics; inputting the updated radiomics features into a risk hierarchical prediction model for training to obtain a tumor risk prediction model; the non-invasive risk stratification prediction of tumor patients can be realized, good sensitivity and specificity can be achieved, and the high false negative rate existing in the current clinical technology is reduced.

The invention relates to the technical field of tumor patient management, in particular to a tumor patient hospitalization full-period management system, which acquires and manages personal information, physiological data and treatment data of tumor patients through a data management module and ensures the real-time performance and integrity of the data. The data processing module improves the accuracy of the data by supplementing missing values and screening out abnormal values, optimizes the calculation efficiency through dimension reduction processing, retains main information features, and provides high-quality data input for intelligent prediction. The intelligent prediction module is based on a neural network algorithm, the future physiological parameter change trend of the patient is accurately predicted, the physiological anomaly occurrence probability is calculated, the prediction accuracy and timeliness are remarkably improved, and medical staff are helped to recognize potential risks in advance and provide personalized treatment. And the full-cycle visualization module displays patient data through a timeline and generates a risk object attention set, so that key monitoring of high-risk patients is realized, and the safety management level during hospitalization is enhanced.

The invention discloses a PROTACs compound for targeted degradation of PRMT5 and application of the PROTACs compound. The PROTACs compound comprises a compound as shown in the following formula, and related analogues or pharmaceutically acceptable salts, metabolites or prodrugs of the compound: in the # imgabs0 # formula, X represents a ligand bound to PRMT5 protein, Z represents a ligand of E3 ubiquitin ligase, and Y represents a linking group for linking X and Z; according to the invention, CRBN is used as E3 ligase, and a series of PRMT5-targeted PROTAC degradation compounds are designed and synthesized. The compound can effectively induce PRMT5 degradation, significantly inhibit proliferation, growth, migration, infiltration, clone formation and metastasis of tumor cells, promote apoptosis of cancer cells, promote autophagy of the tumor cells, prevent or overcome drug resistance of BTK inhibitors and / or prolong the lifetime of tumor patients, and has wide application prospects.

The invention relates to the technical field of tumble prediction, in particular to a tumor patient tumble prediction system based on machine learning, which comprises a stride frequency recognition module, a physiological fluctuation monitoring module, a gait coupling judgment module, a posture analysis module and a risk output regulation and control module. According to the method, by accurately positioning the wave crests and the wave troughs in the acceleration signal period and analyzing the frequency change direction of the adjacent sections, accurate marking of the mutation sections is achieved, the sensitive capture capacity of behavior state changes is effectively improved, and the synchronous extraction mode of the mutation sections and the physiological signals is improved. Through consistency comparison of attitude angle change and gravity center track direction, accuracy of motion structure abnormity and imbalance state identification is enhanced, pressure distribution and spine compression direction are extracted, corresponding pairing between structure pressure and physiological load is realized, optimization of time sequence integrity is realized, and accuracy of motion structure abnormity and imbalance state identification is improved. And multi-dimensional data fusion analysis has high pertinence and stability.

The invention discloses a tumor evolution trajectory prediction method and system based on image feature learning, and the method comprises the steps: obtaining the whole-process pathological section image data of a target type tumor patient, carrying out the analysis and screening of the image quality, constructing a pathological section screening strategy, and extracting a standard image; extracting tumor cell characteristics based on the standard image, performing grouping analysis on the cell characteristics of different time periods through a clustering algorithm, and determining tumor cell development characteristics; further constructing the cell development characteristics of multiple patients into a heterogeneity propagation network, simulating the tumor evolution process by using a random walk algorithm, and identifying the multi-branch evolution trajectory of the tumor; and finally, establishing a tumor evolution trajectory prediction model to predict the tumor development trend of the current patient. According to the method, the accuracy and interpretability of tumor evolution trajectory modeling can be improved, and reliable support is provided for clinical individualized diagnosis and treatment.

The invention provides a tumor treatment effect prediction method based on multi-modal data, and relates to the field of medical artificial intelligence. The method comprises the following steps: collecting magnetic resonance image data, histopathological section images and clinical baseline data of a tumor patient and a treatment response evaluation record after neoadjuvant chemotherapy; the collected image information is preprocessed; feature extraction is carried out based on the preprocessed image information; screening a feature subset with the highest prediction value from the extracted features; constructing a radiopathomics fusion model by using the feature subset with the highest prediction value; evaluating the radiopathomics fusion model; and predicting the tumor treatment effect by using the evaluated radiopathomics fusion model. The problems that an existing tumor neoadjuvant chemotherapy (NACT) curative effect prediction method is low in accuracy, poor in generalization ability, lack of multi-modal data fusion and insufficient in model interpretability are solved.

The invention relates to the technical field of pathological section image recognition, in particular to a system for achieving precise prognosis prediction of a gastric cancer patient based on deep learning, and aims at achieving precise prognosis prediction of the gastric cancer cell tumor patient on the pathological section level. The implementation package of the system comprises an information acquisition module, an image processing module, a wavelet parallel Mama model module, a result prediction module and an interpretability module, so that the efficiency of image feature extraction and the accuracy of model prediction are greatly improved. The core target of the invention is to solve the problems of insufficient accuracy, poorer result stability, limited model generalization ability, lack of interpretability and the like in the existing gastric cancer prognosis prediction method. Through strict verification and testing, the wavelet parallel Mama model of the research shows excellent performance in the aspect of predicting the total lifetime of gastric cancer patients in multiple centers, provides reliable support for clinical decision-making, and manifests the huge potential of the wavelet parallel Mama model in application in the field of precision medical treatment.

The invention relates to the technical field of intelligent follow-up visit, in particular to an intelligent follow-up visit system and method for daytime tumor patients, a medium and equipment, and the system comprises a physiological monitoring module, a drug interaction analysis module, a side effect mapping module, a health risk early warning module and an intelligent inquiry optimization module. According to the method, through continuous monitoring and real-time comparison of the physiological data of the patient and effective archiving of data fluctuation in a key period, instant and accurate reference is provided for clinical decision making, deep drug interaction analysis enables drug adjustment to be more targeted, unsuitable drug combinations are reduced, and therefore potential health risks of the patient are relieved; through side effect mapping, the system can predict and prevent potential side effects, the treatment safety is guaranteed, the risk early warning module enables the system to identify major health risks in the first time, the response speed of emergency medical events is improved, and the system can be used for improving the treatment efficiency by analyzing the interaction history of patients and optimizing the inquiry process. And the medical experience of the patient is more targeted and practical.

The invention relates to the technical field of tumor prognosis prediction, in particular to a chest tumor prognosis prediction method and system based on data analysis, and the method comprises the following steps: S1, data collection: collecting multi-modal data of a chest tumor patient; s2, data preprocessing: preprocessing the collected multi-modal data; s3, prediction of tumor immune cell infiltration: predicting the infiltration degree of tumor immune cells, and evaluating the influence of the infiltration degree on patient prognosis; and S4, prognosis analysis and prediction: performing comprehensive analysis through a multi-layer perceptron algorithm to generate prognosis prediction results of the tumor patient, including tumor metastasis risk, recurrence probability and immunotherapy response. According to the invention, the complexity of the tumor immune microenvironment can be better captured, so that more accurate prognosis evaluation is provided, and clinical doctors are helped to understand the potential influence of immune response on tumor metastasis, recurrence and immunotherapy response.

The invention discloses a tumor standardized whole-course management system based on digitization, and relates to the technical field of medical information. Comprising a personnel management module, a system management module, a diagnosis and treatment specification module, a tumor specification diagnosis and treatment training and examination module, a TNM staging module, an anti-tumor drug prescription right oriented application module, an anti-tumor drug data real-time monitoring module, a tumor treatment medical record supervision module and a tumor medical conjoined module. According to the method, the working efficiency of a team is improved by managing the individual information and role authority of medical personnel, the treatment scheme is optimized by analyzing the TNM stage data, the information transparency is improved by submitting the prescription right application, and the anti-tumor drug use data of the tumor patient is monitored in real time, so that the anti-tumor effect of the patient is improved. The medical staff can know the use condition of the medicine, the medical records of the tumor patients are randomly selected for spot check, the medical record recording and processing quality is continuously improved, a medical conjoined platform interface is constructed, and the operation efficiency of the whole medical conjoined is improved.

The invention discloses a method for constructing a tumor organoid by using stromal stem cells, which improves the survival rate and success rate of the tumor organoid by improving the introduction of the stromal stem cells in the construction of the human tumor organoid, especially for tumor samples which fail to be cultured by the currently disclosed method. The organoid constructed by the invention can be used for analyzing biological characteristics of human tumor organoid; and a clinical tumor drug sensitivity prediction platform is established by taking a tumor organ as a model tool, so that the effectiveness of a treatment scheme is evaluated more systematically and comprehensively, and support is provided for screening an optimal individualized precise treatment scheme for a tumor patient. In addition, on the platform, screening, verification and mining can be carried out on tumor batch chemotherapy drugs or other potential anti-cancer small molecule drugs in clinical application.

The invention discloses an intelligent monitoring and management method and system for postoperative care of tumor patients, and relates to the field of data analysis, and the main scheme is as follows: calculating a postoperative care state value # imgabs5 # according to a stability index # imgabs0 #, an equilibrium index # imgabs1 #, a wound inflammation immune response index # imgabs2 #, a pain tolerance and psychological toughness association index # imgabs3 # and a body temperature and immune response index # imgabs4 #; the problem that evaluation indexes are not comprehensive enough, are limited to basic vital sign data and meanwhile ignore relevance among different types of data, so that evaluation accuracy is reduced is solved, the body state of the user is judged according to the nursing state value # imgabs6 # and the nursing state threshold set, the body state of the patient can be accurately judged, and the evaluation accuracy is improved. And the efficiency and the pertinence of nursing work can be improved.

The invention discloses a co-culture method of gastric cancer organoid and gastric cancer-related myofibroblast and application of the co-culture method, gastric cancer organoid single-cell suspension and gastric cancer-related myofibroblast single-cell suspension are mixed according to the number ratio of 1: 1, then matrigel is added, after uniform mixing, the mixture is put into a CO2 cell incubator to solidify the matrigel, and the gastric cancer-related myofibroblast co-culture medium is obtained. The co-cultured gastric cancer organoid is faster in growth speed and stronger in resistance to chemotherapeutic drugs, the drug resistance of tumor cells in the body of a gastric cancer patient to the chemotherapeutic drugs is better simulated, and the co-cultured gastric cancer organoid can become a screening platform of individualized therapeutic drugs for tumor patients.

The invention provides application of TRMT61A and its inhibitor tetramethylthiuram disulfide in tumor immunotherapy, and relates to the technical field of biological medicine, clinical research proves that RNA m1A methyltransferase TRMT61A is highly expressed in tumor tissues and is related to poor prognosis of tumor patients, and can be used as one of tumor therapy targets. Meanwhile, high expression of the TRMT61A is related to poor prognosis of a tumor patient receiving anti-PD-1 immunotherapy, and after anti-PD-1 antibody immunotherapy is carried out on the patient with low TRMT61A expression level in the tumor of the patient, pathological complete relief is easier. Tetramethylthiuram disulfide inhibits TRMT61A, inhibits m1A modification regulation of TRMT61A on PD-L1 mRNA, increases PD-L1 protein expression, and is combined with a PD-1 antibody drug to significantly enhance the immunotherapy effect of the anti-PD-1 immune checkpoint drug in treating malignant tumors.

The invention belongs to the technical field of biomedicine, and particularly relates to tumor neoantigen polypeptide based on EGFR gene mutation and application of the tumor neoantigen polypeptide. According to the effective method for predicting and screening the tumor neoantigen by using the tumor neoantigen prediction platform, the specific mutation of tumor tissues is obtained, and the neoantigen polypeptide with high immunogenicity, high affinity and high activation effect is more accurately predicted; the screened new antigen polypeptide makes up the blank of individualized antigen peptide in treatment of lung cancer NSCLC patients with EGFR-L858R somatic mutation, can be synthesized in a large scale, is used for subsequent standardized and individualized immunotherapy of EGFR-L858R mutation tumor patients, and has a good clinical application prospect.

The invention provides a lung adenocarcinoma prognostic marker and a model, constructs a lung adenocarcinoma prognostic model based on mRNA and lncRNA related to programmed cell death (PCD), and synthesizes bioinformatics analysis and experiments to explore the biological effect of the lncRNA model in lung adenocarcinoma. The prognosis model covers all PCD pathways and various molecules, has stable prognosis prediction accuracy, and can provide a potential molecular target for personalized treatment. According to the method disclosed by the invention, the PCD-related mRNA and lncRNA are integrated to construct a lung adenocarcinoma diagnosis model suitable for Chinese population, so that the identification capability of tumor patient classification is enhanced from multiple aspects, the accuracy and reliability of prognosis prediction are improved, powerful support can be provided for promoting personalized treatment strategies of lung adenocarcinoma patients, and the method is suitable for popularization and application.

A method of using a DNA alkylating agent prodrug compound-containing single drug or using the single drug and other drugs in combination for treatment of p53 gene mutation or defect negative cancer and tumor patients, and a pharmaceutical use. In particular, the DNA alkylating agent prodrug compound is selected from a hypoxia-activated DNA alkylating agent prodrug compound, an AKR1C3-activated DNA alkylating agent prodrug compound, and a β-D-Glucosidase- or β-galactosidase-activated DNA alkylating agent prodrug compound.

The invention discloses an application of the number of neutrophils in predicting the curative effect of oncolytic viruses in treating tumors. In the application, the neutrophile granulocyte number level in the blood of a tumor patient is closely associated with the tumor treatment effect, and the applicable tumor syndromes are common, including melanoma, colorectal cancer, esophageal cancer, head and neck tumors, gastric cancer, biliary tract system tumors, sarcoma and other tumors. In the application, the critical value of the number of neutrophils for predicting the tumor treatment effect is 2 * 10 < 9 >-5.5 * 10 < 9 > / L, and the most preferable critical value is 4.0 * 10 < 9 > / L; the curative effect of OH2 single-drug treatment on tumor patients with the neutrophil number level below a critical value is better than that of tumor patients with the neutrophil number level above the critical value.

The invention relates to the technical field of precision medical treatment, in particular to an interpretation system and method for tumor precision medication and a storage medium, and the system comprises a data collection module which is used for collecting gene data, clinical data and drug response data of tumor patients from a plurality of data sources; the data standardization processing module is used for carrying out formatting and standardization processing on the data, so that the data formats are consistent, and effective processing and analysis can be carried out in subsequent modules; the gene data analysis module is used for performing variation analysis according to the gene data of the patient, identifying a gene mutation site and generating a drug sensitivity analysis result; and the drug screening module is used for screening drugs matched with the gene variation sites of the patient from a drug database according to the gene data analysis result, and providing drug dosage recommendation. By adopting the technical scheme of combining a quantum optimization technology and gene data analysis, the technical effect of remarkably improving the drug screening and dose recommendation precision is achieved.

The invention discloses a Nomogram clinical prediction method for postoperative skin flap vascular crisis of an oral and maxillofacial tumor patient, and the method comprises the following steps: analyzing an influence factor Meta, and forming a data collection table; matching logistic regression analysis, and adjusting a data collection table: collecting clinical data, and collecting related information of a patient; processing the collected information, and screening variables by adopting single factor analysis; a logistic regression algorithm is adopted to analyze the multi-factor field; constructing a risk prediction model, and performing internal and external verification; carrying out analysis modeling on the training group, and carrying out visual presentation through a Nomogram graph; a Bootstrp 1000-time re-sampling method is adopted to carry out replacement extraction on the model data, and internal verification is carried out on the development model; and drawing a clinical decision curve. The objective of the invention is to explore risk factors of vascular crisis after free tissue valve repair operation of oral and maxillofacial tumor patients from finer and simplified clinical variables and establish an efficient and rapid model to predict the risk of vascular crisis after operation.

The invention discloses a female tumor comprehensive management system and method based on a female tumor large model, and relates to the technical field of female tumor management, and the system comprises a model training module and a personalized management suggestion generation module. The model training module is used for training a model with a double-engine architecture based on multiple groups of multi-modal data associated with the female tumor to obtain a large female tumor model; and the personalized management suggestion generation module is used for generating personalized management suggestions of the target user by using the female tumor large model when the female tumor related data of the target user is received. According to the female tumor large model, individual differences can be fully considered, so that the generated personalized management suggestions are more targeted. Moreover, the whole system architecture has good expandability and self-learning ability, can continuously optimize the female tumor large model along with continuous input of new data and new knowledge, and provides more accurate personalized management suggestions for female tumor patients.

The present invention discloses a method and system for managing the side effects of medication for tumor patients based on big data analysis, the method comprising the following steps: collecting and preprocessing patient data and drug data, extracting patient characteristics and drug characteristics, wherein the patient data includes genomic data provided by the patient; constructing a prediction model, including a time series analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model, for using drug characteristics and patient characteristics as inputs of the prediction model to predict the risk score of different types of side effects in the patient; inputting the basic information, medication information and genomic data of the current patient into the trained prediction model to obtain a risk assessment of various side effects in the current patient; according to the risk assessment results, selecting a personalized adjustment suggestion suitable for the patient from a preset intervention program library and providing it to the current patient. The present invention can effectively improve the scientificity and accuracy of the management of medication side effects during the treatment stage.

The invention discloses a tumor radiotherapy prognosis prediction simulation method and system based on big data and a medium, and relates to the technical field of tumor radiotherapy prognosis, and the method comprises the following steps: collecting basic physiological information of a tumor patient, and collecting a color image and a temperature image of skin of a radiotherapy part of the tumor patient; skin color feature extraction and skin temperature feature extraction are carried out to obtain skin color feature data and skin temperature feature data; a radiodermatitis prediction model is constructed; performing prediction simulation on the skin of the radiotherapy part of the tumor patient based on the radiodermatitis prediction model, and performing prediction simulation correction to obtain a radiodermatitis prediction result of the tumor patient; the method is used for solving the problem that when an existing tumor radiotherapy prognosis technology is used for predicting and simulating disease development, the risk and time of radiodermatitis of a tumor patient cannot be objectively and accurately predicted and simulated according to physiological characteristics of skin of the tumor patient after radiotherapy.