20 results about "Diseases outcome" patented technology

The invention discloses a disease full-period management system based on a deep neural network, and the system comprises a collection time sequence planning module, a disease biomarker data collection module, and a compliance standard assessment module. The method comprises the following steps: acquiring previous disease diagnosis elements such as specific disease category proportion and attack frequency of each patient in each time window of a historical period from a target hospital patient platform, analyzing a disease period adaptive value according to the previous disease diagnosis elements, and further determining an acquisition time sequence plan when the patient is discharged from a hospital. And the compliance standard evaluation module is used for acquiring a compliance evaluation value of the discharge collection time sequence plan of each patient in the current period, judging whether the compliance reaches the standard or not, sending out an early warning prompt to the substandard patient, and recording the substandard patient as a to-be-analyzed patient. And the disease outcome state analysis module is used for analyzing the disease outcome states of the to-be-analyzed patients at each acquisition time point, carrying out early warning prompt according to the analysis result, and realizing accurate management and monitoring of the whole period of the diseases of the patients through multi-module cooperation.

The invention discloses a disease cause identification method based on causal learning. The method comprises the following steps: preprocessing an observable medical data set; calculating a fitting relationship between each to-be-inspected variable and the data set by using a Bayesian marginal likelihood probability, and separating causal and confounding factors in the disease influence factors; an iterative causal relationship generation algorithm is utilized, the fitting degree of the causal relationship is iteratively judged on the basis of a Bayesian scoring criterion, and causal influence factors of disease outcome are described in a causal graph form into a direct level and an indirect level; for the discovered causal relationship, a conditional expectation expression is modeled based on a back door criterion, a front door criterion and a tool variable criterion, and a causal effect value is estimated based on a linear regression model, so that the influence degree of the cause of the disease on the outcome of the disease is quantified. According to the method, a causal learning method is used for researching high-risk influence factors possibly causing diseases, the causal effect value and the change trend of the direct influence factors and the target result are determined, and the cause of related diseases can be known.

The present invention refers to a diagnostic method for the ex-vivo prognosis, prediction or risk stratification for mortality, disease outcome or ICU admission of a patient who has been diagnosed with community acquired pneumonia (CAP) comprising the steps of i) providing a sample from the patient and ii) analysing the expression level of at least one nucleic acid in the sample, wherein the patient's prognosis, prediction or risk stratification is determined according to the expression levels of the at least one nucleic acid. The at least on nucleic acid is selected from a gene signature comprising biomarkers as identified in the present invention. In further aspects the present invention also refers to the nucleic acids of the gene signature and their use for prognosis and risk stratification. Finally, the present invention also refers to a diagnostic kit for performing the method of the invention

This application discloses the use of a biomarker in the preparation of a product for the treatment and prediction of myasthenia gravis, wherein the biomarker is one or more of OSM, CASP-8, TRAIL, TGF-α, FLT3L, IFN-γ, IL-7, and AXIN1. Through multi-omics combined analysis, this application explores the dynamic changes and molecular mechanisms of immune-inflammatory-related proteins after complement inhibition from the dual dimensions of gene transcription and protein expression, and finds a comprehensive biomarker that can systematically evaluate the dynamics of symptom improvement and the degree of disease stability, with the advantages of accurately guiding treatment adjustments and predicting disease outcomes.

The application discloses a pig disease diagnosis and treatment teaching training method, device and equipment based on AR, which comprises the following steps: loading a three-dimensional sick pig model corresponding to the current teaching scene through an AR device, and presenting the current symptom performance including visual elements and sound at the corresponding part of the three-dimensional sick pig model; receiving the diagnosis and treatment decision input by a user according to the current symptom performance; analyzing the diagnosis and treatment decision based on a preset pharmacology-pathology response model, updating the symptom state of the three-dimensional sick pig model according to the analysis result, and presenting the updated symptom state in real time as corresponding AR visual effects; when the user confirms the completion of the diagnosis and treatment operation based on the updated symptom state, comprehensively scoring the drug selection accuracy, dosage rationality, processing timeliness and diagnosis and treatment process integrity of the user in the diagnosis and treatment process, and generating a teaching evaluation report. The interactive training scheme can simulate a complete diagnosis and treatment process, support autonomous decision-making and dynamically feedback the disease outcome, and improves the teaching interactivity.

Provided are methods and kits for early determination of disease outcome and prognosis of Multiple Sclerosis (MS) patients and for adjustment of suitable treatment.

A method is provided for predicting a disease outcome using a machine learning model that generates training data for training the machine learning model useful for implementing a cellular disease model.SOLUTION: A method for predicting a disease outcome using a machine learning model includes implementing an ML-enabled cellular disease model to validate an intervention, identifying a patient population likely to be a responder to the intervention, and developing a therapeutic structure activity relationship screen. To generate a cellular disease model, data from human genetic cohorts, the literature, and generic cellular or tissue-level genomic data are combined to elucidate a set of factors (e.g., genetic, environmental, cellular factors) that cause a particular disease. A series of factors are used to manipulate invitro cells to generate training data for training a machine learning model useful for implementing a cellular disease model.SELECTED DRAWING: FIG. 1B

The invention discloses a health status assessment method and device, electronic equipment and a storage medium, and through the method and the device, traditional physiological index data and neurological function defect symptom data of a user can be collected at the same time by means of mobile terminal equipment, so that the blank of neglecting of digital collection of early symptoms of neurological function defect in an existing method is filled; then the two types of data are analyzed through a multi-dimensional health data fusion analysis model, internal association between the data and cerebrovascular disease outcome and relapse is fully mined, and the problems that only traditional physiological index monitoring is relied on, neglect is given to digital acquisition of early symptoms of neurological impairment, multi-modal data fusion analysis is lacked, and the accuracy of data analysis is poor are solved. The technical effects that early warning signals of the cerebrovascular diseases are accurately captured, the accuracy rate of health state evaluation and early warning is improved, a health management scheme meeting individual requirements is generated, the out-of-hospital rehabilitation monitoring and intervention effect is enhanced, and long-term scientific management of the cerebrovascular diseases is assisted are achieved.

The present invention relates to a method for diagnosing, predicting disease development, disease progression and / or disease outcome, predicting sensitivity to treatment and / or classifying in the context of female genital tract diseases or conditions, in particular endometriosis, in which biomarkers in Table 1, e.g., CCL5 and / or NEAT1, are determined. The invention further relates to a pharmaceutical product for a patient stratified according to the method of the invention and to a composition comprising an agent for detecting a biomarker in Table 1 for diagnosing a female genital tract disease or condition.

The invention discloses an inflammatory bowel disease accurate diet intervention scheme generation method and system based on multi-modal data integration, and relates to the technical field of diet and health management. The method comprises the following steps: acquiring crowd health data from a plurality of data sets; analyzing and marking the acquired crowd health data; constructing an identification rule and consistency calibration model of the macroscopic phenotype combination features; constructing a biomics alignment data structure reflecting diet and lifestyle-clinical medication-disease outcome association; performing reliability weighting on the macroscopic phenotypic information of the individuals, and endowing each layer of data of different data sets with corresponding weights; constructing a multi-dimensional comprehensive data matrix; establishing a precise diet model; and generating an individualized diet intervention scheme, continuously collecting follow-up visit data, feeding back the follow-up visit data to the model, and dynamically updating model parameters. According to the invention, an accurate diet scheme with high individual suitability can be generated, the dynamic optimization capability is realized, and the diet intervention accuracy and long-term management effect of the patient with the inflammatory bowel disease are improved.

The present invention refers to a diagnostic method for the ex-vivo prognosis, prediction or risk stratification for mortality, disease outcome or ICU admission of a patient who has been diagnosed with community acquired pneumonia (CAP) comprising the steps of i) providing a sample that has been obtained from the patient and ii) analysing the expression level of at least one nucleic acid in the sample, wherein the patient's prognosis, prediction or risk stratification is determined according to the expression levels of the at least one nucleic acid. The at least on nucleic acid is selected from a gene signature comprising biomarkers as identified in the present invention. In further aspects the present invention also refers to the nucleic acids of the gene signature and their use for prognosis and risk stratification. Finally, the present invention also refers to a diagnostic kit for performing the method of the invention.

This invention discloses a kit, its application, and its method for evaluating the prognostic outcome of liver failure. The invention analyzes the correlation between high-throughput transcriptome sequencing and disease outcome (death, survival), and verifies the results using qPCR. The results show that VSIG4 is specifically highly expressed in patients with poor liver failure outcomes (death) and has a strong, stable, and reliable correlation with the outcome (death, survival). The kit's detection process is simple, reproducible, and can be performed by qualified technicians, making it highly feasible. It provides accurate early warning and prediction results for liver failure outcomes, promptly reflecting the disease status of liver failure patients. This allows for early warning and prediction of liver failure outcomes, guiding physicians in developing targeted treatment plans, enabling early treatment, and reducing short-term mortality.

The invention provides a multisystem disease analysis method based on gastrin plasma proteomics, which comprises the following steps of: A, analyzing a large number of adult individuals subjected to baseline evaluation by using queue research data of a biological sample library, and measuring a plurality of plasma proteins; b, analyzing a standardized protein expression (NPX) value of gastrin (GAST) by adopting a multivariable Cox proportional risk model, and correcting by using Bonferroni so as to reduce a false positive result; and C, exploring the correlation between the GAST-PRS and the disease phenotype by adopting full phenotype correlation analysis (PheWAS) based on polygene risk score (PRS), and further analyzing the genetic correlation between the GAST and the disease by using linkage imbalance score regression (LDSC). According to the method, the GAST can be redefined as a cross-system risk regulation factor, and the method is of great significance to layering of patients with shared pathway imbalance. The GAST has multi-system correlation with morbidity and mortality in the outcome of 168 diseases, and challenges the classical normal form of the gastrointestinal specific function of the GAST. These findings establish the location of GAST as a multi-system risk indicator.

Disclosed are systems and methods for predicting patient response to a treatment option. In one embodiment, the image slides from patient tissue samples are divided into patches and morphological patterns correlated with a disease outcome are labeled and given a patch-level score, based on whether the morphological patterns occur only in patients with good outcomes or patients with poor outcomes. A patient-level score can be generated based, at least partly, on the patch-level scores. Patch-level scores can identify regions of interest for targeted biomarker identification.

The invention relates to the field of animal disease models, and discloses an improved progressive immune checkpoint inhibitor related myocarditis mouse model as well as a construction method and application thereof. The construction method of the model comprises the steps that 0.1 mL of a Freund's complete adjuvant mixed solution is subcutaneously injected into a mouse on the 0th day and the 7th day, and the solution contains 0.25 mg of a mouse cardiac troponin I immunogenic peptide fragment; the method is characterized in that a PD-1 inhibitor and a CTLA-4 inhibitor are jointly injected into a mouse in a caudal vein mode at the frequency of one time every two days from the eighth day, the injection dosage of each drug is 5 mg / kg per time, and the injection process is carried out for 5 times in total. According to the application, cardiac inflammation caused by ICIs drugs is induced through combined use of various reagents, and a disease outcome stage of a patient in the real world is perfectly simulated through dose control and injection method improvement, so that a bedding effect is achieved for clinical research and drug test.

The invention discloses an application of a biomarker in preparation of a product for treating and predicting myasthenia gravis. The biomarker is one or more of OSM, CASP-8, TRAIL, TGF-alpha, FLT3L, IFN-gamma, IL-7 and AXIN1. According to the application, through multi-omics conjoint analysis, the dynamic change and molecular mechanism of immune inflammation related protein after complement inhibition are explored from double dimensions of gene transcription and protein expression, and a comprehensive biomarker capable of systematically evaluating symptom improvement dynamics and disease stability degree is found; the method has the advantages of accurately guiding treatment adjustment and predicting disease outcome.

The present invention relates to a colorectal cancer subtype identifier based on a percentage splice value (PSI) of a variable splice event. The invention also relates to a method for predicting the outcome of a subject suffering from colorectal cancer by using the PSI value. The invention also relates to a kit for predicting the outcome of a subject suffering from colorectal cancer by means of the PSI value. The invention also relates to the use of PSI values as outcome markers for subjects suffering from colorectal cancer. The invention also relates to a method of treating a subject suffering from colorectal cancer by obtaining a CMS likelihood related to the outcome of disease using PSI values.

The invention relates to methods for diagnosing, predicting disease development, disease progression and / or disease outcome, predicting susceptibility to treatment, and / or classification in the context of diseases or disorders of the female reproductive tract, in particular endometriosis, wherein biomarkers of Table 1 are determined, e.g. CCL5 and / or NEAT1. The invention further relates to pharmaceutical products for use in patients stratified according to the methods of the invention and to compositions comprising reagents for the detection of the biomarkers of Table 1 for the diagnosis of diseases or disorders of the female reproductive tract.

Provided herein are methods, compounds, and compositions for safely reducing thrombopoietin in a cell or an individual. Such methods, compounds, and compositions maintain platelet count within a safe hemostatic range. Such methods, compounds, and compositions are useful to safely treat, prevent, or ameliorate a disease that can benefit from platelet count reduction in an individual. Such methods, compounds, and compositions are useful for treating or preventing diseases in which thrombopoietin contributes to the presence or activation of platelets contributes to and promotes disease initiation or progression, and / or adversely affects disease outcome.

Provided herein are methods, compounds, and compositions for safely reducing thrombopoietin in a cell or an individual. Such methods, compounds, and compositions maintain platelet count within a safe hemostatic range. Such methods, compounds, and compositions are useful to safely treat, prevent, or ameliorate a disease that can benefit from platelet count reduction in an individual. Such methods, compounds, and compositions are useful for treating or preventing diseases in which thrombopoietin contributes to the presence or activation of platelets contributes to and promotes disease initiation or progression, and / or adversely affects disease outcome.