491 results about "Survival rate" patented technology

The invention discloses an AIE type fluorescent probe with tumor targeting as well as preparation and application thereof, and belongs to the technical field of medicines, the probe can accurately target HDAC6 high-expression tumor cells, the specific binding capacity of the probe reduces interference to normal cells, and the detection accuracy is improved. The probe is fused with AIE performance dye molecules and emits strong fluorescence during aggregation, so that the imaging sensitivity and definition are remarkably enhanced. By optimizing the structure and synthesis conditions, the fluorescence intensity and stability of the probe are improved, the sensitivity of early tumor detection is ensured, and the survival rate and life quality of patients are improved. The preparation method is simple, convenient, feasible, low in cost and beneficial to practical application. The probe can be used for preparing tumor detection products, assisting in tumor diagnosis and classification, monitoring treatment changes and evaluating the curative effect. High-precision imaging can be realized in the aspect of tumor imaging, the method can be used for evaluating curative effect and guiding formulation of subsequent treatment schemes, and the application prospect is wide.

The invention discloses an application of a sugar chain marker combination in preparation of a product for diagnosing and predicting pancreatic cancer prognosis. The marker combination comprises the following N-sugar chains: NGA2F, NG1A2F-1, NG1A2F-2, NA2, NA2F, NA2FB, NA3 and NA3Fb. The pancreatic cancer is detected by analyzing the correlation between the change of a sugar chain on seroglycoprotein and diseases and establishing a prediction model of a sugar chain marker combination, so that the pancreatic cancer and non-pancreatic cancer are diagnosed and distinguished in an auxiliary manner, and meanwhile, the prognosis state of pancreatic cancer patient treatment can be evaluated. The method for detecting the pancreatic cancer by adopting the carbohydrate chain marker combination has high sensitivity and high specificity on the pancreatic cancer, can help a pancreatic cancer patient to find and treat the pancreatic cancer patient early, predicts and evaluates the prognosis state of the pancreatic cancer patient, and improves the overall cure rate and survival rate of the patient.

The invention discloses a CNN-Transform method based on multi-scale feature interaction, and mainly relates to the technical field of medical image analysis. Comprising the following steps: S1, acquiring sufficient amount of pancreas and tumor CT data, labeling the data, defining an ROI (Region of Interest), and distributing corresponding labels; s2, dividing the data set obtained in the step S1 into a training set and a test set, and preprocessing the training set and the test set by a data enhancement method including random rotation, scaling and mirroring; and S3, a CNN-Transform pancreas and tumor segmentation model PanUNet based on multi-scale feature interaction is constructed. Visual and accurate image information can be provided for doctors through accurate pancreas and tumor segmentation, personalized treatment schemes can be formulated in time, and the survival rate of patients is increased.

The invention relates to the field of medical information management, in particular to a children malignant tumor treatment process and follow-up visit electronic management system and a children malignant tumor treatment process and follow-up visit electronic management method. The method comprises the steps that the body surface area of a patient is calculated, and the chemotherapy drug dosage in each cycle is calculated based on the body surface area of the patient; performing cycle-by-cycle accumulation on the chemotherapeutic drug dose in each cycle to obtain an actual accumulated dose; calculating and standardizing the specific numerical value of each curative effect index based on the actual accumulated dose, and calculating a comprehensive therapeutic effect score by combining the importance weight of each curative effect index; and calculating the disease-free survival rate based on the actual accumulated dose and the comprehensive treatment effect score. The problems that at present, most medicine dosage calculation methods neglect the special physiological features of children, a treatment monitoring system lacks dynamic adjustment support in the treatment process, a follow-up management system is insufficient in quantitative analysis capacity for long-term prognosis, and personalized long-term health management suggestions cannot be provided for patients are solved.

The invention relates to the technical field of spermidine application, in particular to application of spermidine in preparation of medicine for preventing and treating doxorubicin-induced cardiotoxicity and myocardial aging. Cell proliferation experiments and mouse survival rate experiments show that spermidine can reduce death of myocardial cells and mice caused by adriamycin; a q-PCR experiment shows that spermidine can improve abnormal expression conditions of P53, P21, P16, BAX, TNF-alpha, IFN alpha, IL-10 and IL-18 in myocardial cells caused by adriamycin; a beta-galactosidase staining experiment shows that the spermidine can reduce the condition that the expression of a marker beta-galactosidase for myocardial cell senescence induced by adriamycin is increased; a Western Blot experiment shows that the spermidine can improve the condition that the P53 protein is abnormally increased due to the adriamycin; an Elisa experiment shows that spermidine can improve the condition that myocardial injury markers cTn-I and AST and inflammatory factors IL-1beta are abnormally increased due to adriamycin, and the myocardial injury markers cTn-I and AST and inflammatory factors IL-1beta are abnormally increased due to adriamycin. Therefore, spermidine can improve cardiotoxicity and myocardial aging induced by adriamycin.

The invention relates to the technical field of remote monitoring, in particular to a critically ill patient remote monitoring system and method, and the system comprises a life signal collection module, a physiological state analysis module, a remote monitoring response module and a remote intervention operation module. According to the method, the data analysis and real-time feedback process is optimized, the efficiency and accuracy of medical response are improved, the key physiological indexes deviating from the normal range can be quickly and accurately recognized and analyzed by comprehensively analyzing the instant vital sign data and the historical medical records of the patient, the prediction accuracy is improved, and the prediction efficiency is improved. Medical personnel are allowed to receive clear health risk warnings at critical moments, direct communication with a medical network is enhanced through a rapid grading and alarm system, a faster medical intervention response is achieved, meanwhile, a treatment plan can be remotely and dynamically adjusted according to the current physiological state of a patient, the treatment process is more accurate, and the treatment efficiency is improved. And the survival rate and the treatment quality of patients are greatly improved.

The invention relates to a methylation biomarker for detecting colorectal cancer or colorectal adenoma. The methylation biomarker comprises any one gene of SDC2, COL4A2 and C9H9orf50 or any combination of the SDC2, the COL4A2 and the C9H9orf50. Wherein a methylation target detection region of the SDC2 gene is a full-length region of chr8: 96494012-96494130 or a partial region of the full-length region of chr8: 96494012-96494130; a methylation target detection region of the COL4A2 gene is a full-length region of chr13: 110306814-110306983 or a part of the full-length region of the chr13: 110306814-110306983; and the methylation target detection region of the C9H9orf50 gene is the full-length region of chr9: 129620291-129620374 or a part of the full-length region of chr9: 129620291-129620374. According to the kit, methylation signals related to intestinal cancer and adenoma in excrement samples can be more accurately detected through detection in different combination modes of the three markers, the early effective diagnosis rate of colorectal cancer patients can be increased, and therefore the survival rate of the patients is increased, and prognosis is improved.

The invention provides a gastric cancer intestinal microbial marker, application thereof and a method for constructing a gastric cancer detection model, and belongs to the technical field of biomedicine. The method comprises the following steps: extracting biomarkers related to gastric cancer pathogenesis and progress from metagenome data, constructing a random forest model by using a random forest algorithm, and training and testing the constructed random forest model by using a training set and a test set to obtain a gastric cancer detection model. According to the gastric cancer detection model constructed based on the gastric cancer intestinal microbial marker, the specificity is 97.2964%, the sensitivity is 94%, the accuracy is 93.40649%, the model efficiency and conformity are high, and the condition of gastric cancer can be accurately predicted and evaluated. Based on the gastric cancer intestinal microbiological marker and the gastric cancer detection model, the disease condition of a gastric cancer patient can be accurately predicted and evaluated, a doctor can make a more effective treatment scheme, the survival rate and life quality of the patient are improved, and the treatment effect is improved.

The invention provides a pot seedling transplanting culture survival rate prediction method, and relates to the field of machine learning. The pot seedling transplanting culture survival rate prediction process comprises the steps of constructing a pot seedling transplanting culture data set, and decomposing a time sequence into trend, seasonal and residual components through STL sequence decomposition so as to respectively extract long-term trend, periodic fluctuation and random disturbance characteristics; sine and cosine functions are utilized to generate position codes, relative sequence information is added into components, then multiple interactive learning modules are constructed, unique features of each component are deeply extracted, common features of the three components are fused through an information sharing module, feature interaction is enhanced, meanwhile, residual connection is introduced to relieve the gradient dissipation problem, and finally, the position codes and the unique features of the components are obtained. The comprehensive features of the three components are input into the full connection layer, a prediction result of the pot seedling transplanting and culturing survival rate is generated, and the method can effectively improve the prediction accuracy of the pot seedling transplanting and culturing survival rate.

The invention discloses a rectal cancer prognosis intelligent analysis method based on two-stage 3D feature extraction. The method comprises the steps of obtaining a prognostic MRI image of a target patient, constructing a 3D convolutional neural network based on a 3D-Unet architecture, performing staged training on the 3D convolutional neural network, and generating a rectal cancer tumor extraction model; taking the preprocessed MRI 3D image as a model input, segmenting a rectal cancer tumor image, and outputting a 3D tumor image and a risk score in stages; and performing prognosis evaluation by combining the risk score with clinical data of the target patient to obtain the survival rate of the target patient. According to the invention, the 3D tumor information in the MRI image can be extracted, the accuracy of prognosis analysis is further enhanced, a more accurate and more reliable prognosis result is provided, and the clinical decision 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 a cardiovascular disease intelligent diagnosis and treatment auxiliary system which comprises a hospital server terminal, a cardiovascular disease intelligent diagnosis and treatment cloud platform, a doctor mobile terminal, a medical care mobile terminal and a patient mobile terminal. The hospital server terminal is used for compiling a clinical patient medical record created and uploaded by a doctor and data uploaded by medical care for clinical examination of a patient, and creating a medical record model of the patient, and the hospital server terminal uploads the created medical record model to the cardiovascular disease intelligent diagnosis and treatment cloud platform for cardiovascular case big data collection; according to the intelligent diagnosis and treatment auxiliary system for the cardiovascular disease, in the intelligent diagnosis and treatment process, a medical record model of a patient can be analyzed, healing duration data of cardiovascular diseases of the same type in a patient healing and disease death record storage unit are retrieved and called, and the healing duration of the patient is calculated and predicted through big data; and the disease death duration data of the same type of cardiovascular diseases in the patient healing disease death record storage unit are retrieved and called, and the survival rate of the patient is calculated and predicted through big data.

The invention relates to application of 4-GBA in preparation of a medicine for improving infection endurance capacity of a sepsis patient, 4-GBA is used for treating mice infected with sepsis, compared with control mice, the survival rate of the mice treated by the 4-GBA is far larger than that of the control mice, and experiments prove that the 4-GBA has a protection effect on sepsis infection. The mechanism of 4-GBA for improving infection endurance capacity of sepsis patients is researched, and after 4-GBA treatment, the content of liver and kidney functional markers in peripheral blood serum of mice infected with sepsis is remarkably reduced, the content of DAMPs (injury related molecular patterns) related molecules in peripheral blood serum of mice infected with sepsis is remarkably reduced, and the application of 4-GBA in the treatment of the mice infected with sepsis in the treatment of the infection endurance capacity of the patients infected with sepsis in the treatment of the infection endurance capacity of the patients infected with sepsis in the treatment of the patients infected with sepsis in the treatment of the patients infected with sepsis. And the product does not have in-vivo and in-vitro sterilization functions and body immune regulation functions. The result shows that 4-GBA can improve the infection endurance capacity of the sepsis patient, and provides a new direction and a new drug for treatment of the sepsis patient.

The invention relates to the technical field of cancer treatment, and provides application of a lactyltransferase inhibitor in pancreatic cancer and immunotherapy thereof. According to the present invention, the histone lactosyltransferase inhibitor and the anti-programmed death receptor-1 antibody are combined to form the composition for pancreatic cancer and immunotherapy thereof, such that the recognition and the removal of the immune system on the cancer cells can be enhanced, the growth of the pancreatic cancer tumor can be effectively inhibited, and the survival time of the pancreatic cancer patient can be prolonged. The histone lactosyltransferase inhibitor reduces the infiltration level of tumor-associated neutrophil, increases the infiltration level of CD8 positive T cells, reverses the composition of a pancreatic cancer inhibitory immune microenvironment, and generates a synergistic anti-tumor effect with an anti-programmed death receptor-1 antibody. The traditional Chinese medicine composition is suitable for early-stage, middle-stage and late-stage treatment of pancreatic cancer, improves the survival rate and the treatment effect of pancreatic cancer patients, remarkably reduces the side effects of treatment and improves the living quality of the pancreatic cancer patients.

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

The invention discloses an intensive care unit patient early warning method and system based on clinical data, and belongs to the technical field of medical monitoring, and the method comprises the steps: medical staff carries out the setting of a two-dimensional grading system for an intensive care unit patient, and collects the input information data and monitoring data of the patient; constructing a risk factor joint model to evaluate the multi-dimensional potential crisis of the patient in the intensive care unit, giving out early warning according to the evaluation result of the risk factor joint model, and adjusting the severity level and the monitoring level of the patient; key physiological parameters of the patient are calculated in real time, the threshold value is corrected, the corrected threshold value serves as a new threshold value to be written into a database of the risk joint model, and the new threshold value is synchronized to a monitoring terminal through a timestamp. Potential medical risks are effectively reduced, the survival rate of the patient is improved, early warning is given out in time, the monitoring level is optimized, implementation of targeted medical services is ensured, the situation changes of the patient are rapidly recognized, the safety and efficiency of critical care are improved, and powerful technical support is provided for the patient.

The invention specifically discloses an intelligent triage classification AR glasses system based on multi-modal data, and relates to the technical field of medical risk management. The system comprises an AR glasses hardware terminal module, a multi-modal data acquisition module, an intelligent analysis processing module, an AR man-machine interaction module, a data storage and transmission module and a peripheral linkage module. Wherein the AR glasses hardware terminal is in lightweight design, the multi-modal data acquisition module acquires and optimizes video, voice, physiology and other data of the wounded, the intelligent analysis processing module outputs red, yellow, green and black triage results through space-time alignment, feature fusion and AI analysis, the AR man-machine interaction module is used for optimizing interaction, and the data storage and transmission module guarantees data safety. And the peripheral linkage module realizes label printing and NFC writing. The system is short in single-time triage time, triage accuracy and environmental adaptability can be improved, information tracing is achieved, two hands of medical care are liberated, and rescue efficiency and the survival rate of wounded personnel are effectively improved.

A method for providing a treatment recommendation to a physician for treating a patient is disclosed. The method comprises determining, from a processor in communication with a patient data repository, a first treatment recommendation based on a combination of selected patient demographics from the patient data repository applicable to the patient, and operational parameters of a plurality of ventricular assist devices (VADs) suitable for treating the patient, the first treatment recommendation having a first survival rate and comprising the use of a first VAD. The method then obtains a first signal from using the first VAD on the patient. The method then determines a second treatment recommendation based on the first signal and the first treatment recommendation, the second treatment recommendation having a second survival rate. The method then provides the second treatment recommendation to the physician if the second survival rate is higher than the first survival rate.

The invention discloses a shield tunnel detection method based on natural ray muons, and relates to the technical field of shield tunnel detection, and the method comprises the steps: construction early warning, which is used for early warning stratum density abnormity; the accurate scanning is used for delineating an abnormal area; the construction quality control is used for ensuring proper measures; the construction early warning step specifically comprises the steps of obtaining ground contour information and muon detector position information, and performing forward modeling to obtain a theoretical muon survival rate; calculating an actual muon survival rate; comparing the theoretical muon survival rate with the actual muon survival rate, and calculating the difference significance of the two; if the saliency is smaller than the target threshold value, the shield tunneling machine continues to conduct tunneling forwards till the project is completed; and if the saliency is greater than the target threshold value, indicating that the stratum in the region has density abnormity, and giving out early warning by the system. The method does not use an artificial ray source, has the characteristics of non-invasion and capability of penetrating through a large-scale structure, and is very suitable for detection of an over-excavation area of a tunnel scene.

The invention relates to the technical field of biology, in particular to a rice salt stress regulation gene OsMTA1 and application thereof.The rice salt stress regulation gene OsMTA1 is obtained by conducting CRISPR gene editing on the OsMTA1 gene, and compared with receptor rice Zhonghua 11 (ZH11), after two independent strains with mutants knocked out of the transgenic rice OsMTA1 gene are subjected to 180mM NaCl simulated salt stress treatment, the rice salt stress regulation gene OsMTA1 is obtained. After recovery treatment, phenotype observation and survival rate statistics are carried out, it is confirmed that the osmta1 mutant has a salt-sensitive phenotype compared with wild type ZH11, and it is indicated that the OsMTA1 gene plays an important role in the process of responding to rice salt stress.

The invention discloses a liver cancer post-transplantation muscle loss prediction method and device, and belongs to the technical field of biomedicine, and the method comprises the steps: constructing an unbalanced random forest model comprising a plurality of decision trees, independently predicting through the plurality of decision trees to obtain a plurality of high-risk or low-risk dichotomy prediction results, taking the proportion of the decision tree with the prediction result being high risk in all decision trees as the prediction probability of muscle loss after liver cancer liver transplantation finally output by the model; performing risk classification according to the prediction probability, and judging the postoperative muscle loss level of the patient as high risk, medium risk or low risk; constructing a decision tree model to layer prognosis of patients, and dividing the patients into a high-risk group, a medium-risk group and a low-risk group; and carrying out survival rate prediction in combination with liver transplantation standards and prognosis layering results of patients. According to the method, the muscle loss level after the liver transplantation can be accurately predicted, and a clinician can make a personalized treatment scheme and perform scientific management after the transplantation.

The invention discloses a prawn culture-oriented ecological environment condition dynamic evaluation system, and relates to the technical field of culture, prawn metabolism states under different salinity are tested through a test module, the influence of salinity on prawn metabolism can be analyzed, an appropriate salinity range is preliminarily screened out by constructing an influence coefficient Yxxs, and the salinity of prawns can be evaluated according to the influence coefficient Yxxs. According to the method, the defects of traditional empirical judgment are overcome, salinity optimization is more scientific and digitized, and the survival rate and the culture efficiency of the prawns are improved. The survival rate Chz is obtained through secondary screening of the survival analysis module and in combination with survival conditions lasting for the same duration under different salinity, and the salinity in the culture pond is further optimized according to the value, so that the influence of salinity change on prawns can be continuously monitored, the culture environment can be dynamically adjusted, and the survival rate of the prawns is improved. It is ensured that prawns grow under the optimal survival condition, and therefore the death risk caused by environment changes is reduced.

The invention belongs to the technical field of biomedicine, and particularly relates to application of ceramide as a marker in prediction of hepatocellular carcinoma microvascular invasion risk. The invention finds that the preoperative plasma ceramide CER (d18: 1 / 16: 0) level can be used for predicting the MVI of HBV-HCC patients and evaluating the postoperative prognosis of the patients for the first time. The invention provides a preoperative non-invasive detection method based on plasma ceramide CER (d18: 1 / 16: 0), and trauma of traditional histopathology detection is avoided. According to the method, the occurrence risk of the MVI can be effectively reduced by simply detecting a blood sample of a patient. According to the method, the MVI risk of a patient is evaluated through a multi-factor prediction model, and a basis is provided for personalized treatment. The method is not only suitable for preoperative MVI risk assessment, but also can assist in making postoperative follow-up visit and treatment plans, and helps to reduce the postoperative recurrence rate and improve the survival rate of patients.

The invention belongs to the field of tumor medicine and the technical field of molecular biology and gene detection, and relates to application of rs17771861 in preparation of a nasopharyngeal carcinoma prognosis prediction marker, and the sequence of the SNP site rs17771861 is as shown in SEQ ID NO.1. The invention also relates to application of the SNP site rs17771861 in preparation of a nasopharyngeal carcinoma prognosis prediction marker. The invention provides an SNP (Single Nucleotide Polymorphism) marker rs17771861 related to nasopharyngeal carcinoma prognosis, application of the SNP marker rs17771861 and a detection kit capable of being used for nasopharyngeal carcinoma prognosis prediction, and the SNP marker rs17771861 is used for assisting in guiding individualized treatment and improving prognosis of tumor patients. After the kit disclosed by the invention is applied to clinical detection, prognosis of a patient can be evaluated before treatment, and a more positive and effective treatment scheme is formulated for the patient with poor prognosis, so that individualized treatment of the patient is realized, and the survival rate is increased. In addition, the kit only needs to detect peripheral blood, and has the characteristics of convenience in sampling, simplicity in operation, high timeliness and the like.

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 glycometabolism and lactic acid metabolism related genes and application thereof. According to the invention, the typing and prognosis model of liver cancer subtypes is constructed for the first time on the basis of a coordinated regulation network of a glycometabolism gene and a lactic acid metabolism gene, the constructed liver cancer subtype subtypes comprise Cluster1 and Cluster2, and the total survival rates of different subtypes have significant difference; the clinical characteristics T.stage and TNM.stage of different subtypes are obviously different from each other; 12 kinds of immune cells have significant differences among different subtypes; iPS immune scores of different subtypes are significantly different; the TIDE scores of different subtypes have significant score differences; the genes with the highest mutation frequency in different subtypes comprise TP53, CTNNB1 and TTN. CLM scores of the prognosis model constructed on the basis of the collaborative regulation network of the glycometabolism genes and the lactic acid metabolism genes have no significant difference in different patient states; the 17 immune cells have significant differences between high and low risk groups. And a new way is provided for individualized treatment strategy formulation and survival outcome improvement of HCC.

The invention discloses a drug screening method based on endotoxin attack, and relates to the technical field of drug screening, in the process of drug screening by using an experimental animal, firstly, an experimental animal model with diseases is established, a certain dose of endotoxin is given to the experimental animal for a period of time after the model is successfully built, and then the experimental animal is subjected to drug screening. And establishing evaluation indexes by utilizing a mathematical formula, and comprehensively evaluating the effectiveness and the safety of the medicine. The evaluation indexes comprise an endotoxin attack survival rate, a survival disease control rate and a survival disease control index. The method can be applied to screening of drugs for treating different diseases, and a more scientific and reliable screening method is provided for research of preclinical drugs or treatment methods.

The invention discloses a pSS-ILD lung-free transplantation survival prediction model and application, and relates to the technical field of pSS-ILD lung-free transplantation survival prediction. A training queue and an external verification queue are established, and pSS-ILD is evaluated through multidisciplinary discussion; the patients are divided into an F-pSS-ILD group and an NF-pSS-ILD group according to an HRCT mode, and the degree of imaging features, especially fibrosis scores, is quantified; analyzing and evaluating the relationship between clinical features and pSS-ILD prognosis by adopting single-factor Cox regression, and formulating a column diagram prediction model for predicting lung-free transplantation survival of pSS-ILD patients in 1, 3 and 5 years; the test of the column diagram prediction model adopts a subject working characteristic ROC curve, an area under the ROC curve and a consistency index to evaluate the prediction accuracy of the model. According to the method, a risk prediction model based on the four factors of age, oxygenation index, CA125 and fibrosis score is constructed, the model is a visual Nomogram visual model for evaluating the lung-free transplantation survival rate of 1, 3 and 5 years, a visual column graph model is provided, and a simple, visual and practical prediction tool is provided for clinicians.

The invention discloses a lung cancer patient dynamic survival prediction model based on multi-dimensional clinical data, and relates to the technical field of cancer survival prediction, and the key point of the technical scheme is that the dynamic survival prediction model comprises a core model module and a data support module, the core model module comprises a graph convolutional neural network model, a long-short-term memory network model and a generative large language model, and the data support module comprises three data processing systems, namely a time-dependent variable, a time independent variable and a data balancing strategy. According to the dynamic survival prediction model based on combination of traditional Chinese medicine and western medicine, abundant multivariate data in EHR can be fully utilized, individualized and precise diagnosis and treatment of the patient can be realized, artificial intelligence is assisted, and dynamic prediction of the survival rate of the lung cancer patient in one year, two years and three years can be effectively realized.

The invention relates to the technical field of biological and medical diagnosis, in particular to a biomarker for predicting whether a patient suffering from pyotoxic myocardial injury has a short-term death risk or not and application of the biomarker, and the biomarker is the ratio of erythrocyte distribution width to albumin. According to the method, retrospective queue research design is adopted, two data sets, namely MIMIC-IV and eICU-CRD, are used, suppurative myocardial injury patients meeting conditions are included, covariants such as baseline features, laboratory inspection results and complication information of the patients are collected, the patients are divided into two groups according to RAR medians, the relation between RAR and short-term death of the patients is analyzed by adopting multiple statistical methods, and the accuracy of the short-term death of the patients is improved. A risk prediction model of short-term death of the patient suffering from the pyotoxic myocardial injury is constructed, so that a clinician can identify the patient suffering from the pyotoxic myocardial injury with high death risk in an early stage, a personalized treatment strategy can be formulated more accurately, the survival rate of the patient suffering from the pyotoxic myocardial injury is increased, and the method has a wide application prospect.

A machine learning system makes medical predictions based, at least in part, on pathomic features indicating collagen fiber organization extracted from pathology images that include lesions. The pathomic features are extracted from pathology images. The pathology images may be routine clinical images gathered in the course of diagnosis and treatment, such as hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of tissue. The medical predictions may concern, e.g., the diagnosis, prognosis, genotype, or phenotype of a lesion, and may include disease-free survival (DFS) and overall survival (OS) of patients known or suspected of having malignant lesions. Methods for training a machine model to make, and for making, such predictions are also disclosed.