111 results about "Disease prognosis" patented technology

The invention provides a method and a system for predicting cardiovascular and cerebrovascular disease risks. The method comprises the following steps: step 1, defining problems of cardiovascular and cerebrovascular disease prognosis risk prediction, step 2, collecting health medical data of cardiovascular and cerebrovascular disease patients; step 3, preprocessing data, including data integration, data cleaning, and processing missing data; step 4, constructing features and selecting features, identifying potential risk factors; step 5, the identified risk factors and rehabilitation outcomes forming an input-output sample set, inputting the input-output sample set to a random forest algorithm for model training, and evaluating prediction performance of a model. The method and the system can obtain health medical data of cardiovascular and cerebrovascular disease patients, the data being required by a clinician input model, and a predicted rehabilitation outcome of a patient in a certain time period in future is obtained through the model. The method and the system can preferably predict prognosis risks, so as to realize personalized accurate rehabilitation treatment.

A method of obtaining and processing patient data and patient treatment data to provide a prognosis parameter related to a patient's disease state is provided. The method identifies and calculates coefficients related to appropriate predictor variables which are then used by the prediction model to calculate the prognosis parameter. The prediction model may be a logistic regression model. The method may also be used to assess the level of care being provided to patients, as well as providing a way of assessing the outcome of the patient's condition as a function of treatment. A method of calculating a harm index reflective of the risk of treatment is also provided.

A method of acquiring an image biomarker suitable for prognosis of a blood-related disease, such as acute myeloid leukemia, includes the steps of: (a) acquiring physical parameter sets, each including at least two physical parameters, respectively from time-signal intensity curves, the time-signal intensity curves being respectively obtained from magnetic resonance image sets of different subjects that are diagnosed as having the blood-related disease, each of the image sets being acquired through MRI scanning using one of first and second configuration parameter sets; (b) analyzing the physical parameter sets thus acquired with reference to prognoses of the different subjects so as to obtain weight values corresponding to the physical parameters; and (c) establishing a risk score function that is a sum of products of each of the physical parameters and the corresponding weight value, wherein a risk score obtained using the risk score function serves as the image biomarker suitable for prognosis of the blood-related disease.

The present disclosure provides genetically engineered cell lines comprising chromosomally integrated synthetic sequences having predetermined epigenetic modifications, wherein a predetermined epigenetic modification is correlated with a known diagnosis, prognosis or level of sensitivity to a disease treatment. Also provided are kits comprising said epigenetically modified synthetic nucleic acids or cells comprising said epigenetically modified synthetic nucleic acids that can be used as reference standards for predicting responsiveness to therapeutic treatments, diagnosing diseases, or predicting disease prognosis.

A medical imaging system that processes input data (imaging and/or non imaging) having high dimensionality and few samples to learn from, by using multiple ranks of machine learning modules each dealing with a separate portion of the clinical data. The outputs of the individual machine learning modules are the combined to provide a result reflective of the complete image data set.

The invention discloses a medical image processing method based on dictionary studying upsampling, and belongs to the technical field of image processing. The medical image processing method is realized by the following steps of: inputting an original medical image; cutting and reinforcing the original medical image; extracting the characteristics of the cut and reinforced medical image to obtain a training sample set and a testing sample set according the extracted characteristics; finding out boundary points of weakness samples from the training sample set to obtain the number of new samples to be generated according to the situation of the neighborhoods of the boundary points; generating required new samples by utilizing a sparse ligature and point getting method; adding the new samples into the training sample set to form a new training sample set; carrying out classifying diagnosis to the new training sample set to get a classifier; and diagnosing the testing sample set by adopting the classifier to get a final diagnosis result. The medical image processing method has the advantages of high recognition rate and strong generalization capability on medical image diagnosis, and can be used for medical workers to evaluate disease prognosis and therapeutic effect.

A method for in vitro diagnosis of atherosclerosis, comprising: (a) obtaining a sample from a subject; (b) determining expression levels of one or more microRNAs (miRNAs) as atherosclerotic biomarkers and an internal control RNA; (c) computing the relative expression levels of the one or more miRNAs as atherosclerotic biomarkers; (d) computing a prediction model with one or more variables, wherein the variable includes one or more relative expression levels of the one or more miRNAs as atherosclerotic biomarkers and one or more risk factors of atherosclerosis; and (e) computing a prediction probability by the prediction model, wherein the subject is diagnosed with atherosclerosis if the probability is more than 0.5 is presented. A kit for in vitro diagnosis of atherosclerosis or prognosis of atherosclerosis-inducing diseases is also presented.

The (Streptococcus suis Serotype 2 (SS2) virulence gene PCR amplification kit includes a SS2DNA contrast template, a PCR amplification detecting reagent, an agarose gel electrophoresis reagent and a DNA gradient standard. The PCR amplification detecting process includes the measurement of 7 important SS2 genes, diagnosing Streptococcus infection and SS2 infection based on whether to amplify specific Streptococcus gene, specific SS2 gene and 5 relevant important SS2 virulence genes, and predicting the virulence, invasiveness trend and disease prognosis fast and early. The present invention is favorable to the clinical diagnosis of borderline case and early warning, and is hopeful to be applied in emergency detection of human SS2 infection, etc.

The invention discloses a disease prognosis prediction system based on deep semi-supervised multi-task learning survival analysis. The disease prognosis prediction system comprises a data acquisitionmodule, a data preprocessing module, a prediction model construction module and the like. On the basis of a deep neural network model, a survival analysis problem is converted into a multi-task learning model composed of a semi-supervised learning problem of multi-time-sequence-point survival probability prediction; the model directly models the survival probability, does not depend on proportional risk hypothesis, can fit a time-dependent effect, and has better interpretability; a semi-supervised loss function and a sorting loss function are utilized to fit the data, complete data and deleteddata are fully utilized, and a traditional survival analysis problem and a survival analysis problem considering competition risks can be solved; according to the model, through multi-task learning of multiple time sequence points, data sharing among multiple prediction tasks is achieved, mutual constraint among the multiple prediction tasks is achieved, and the generalization ability of the model is improved.

The invention relates to a system for detecting multiple quantitative and virulent genes of H.pylori as well as kits and applications of the system. The system for detecting multiple quantitative and virulent genes of H.pylori comprises multiple pairs of primers, each for strain identification genes (16S rRNA), quantitative analysis genes ureCand Beta-globin, as well as virulent genes (cagA, vacA-s1, vacA-s2, vacA-m1, vacA-m2, iceA1, iceA2, dupA, oipA and luxS). The system for detecting multiple quantitative and virulent genes of H.pylori and the kits of the system can directly perform synchronous detection and analysis on the strain identification, quantification and virulence of a tissue sample in a same reaction system without adopting a conventional culture step or other steps, remedy defects that a conventional detection method is low in throughput, time-consuming and low in detection rate, obviously improve the accuracy of detection results, immediately provide a comprehensive, accurate and low-cost etiological diagnosis for clinic, and provide an important reference for the accurate diagnosis, differential diagnosis and disease prognosis of H.pylori infection.

The present invention relates to a disease prognosis prediction modeling method for preparing a model for predicting the prognosis of a specified disease from clinical laboratory test values for the disease by means of a computer, the method comprising the steps of: inputting a plurality of actually measured clinical laboratory test values for the disease and actual measured values of the prognoses into the computer; processing these values by a data mining method to determine one or a plurality of clinical laboratory test items which have an influence on the prognosis of the disease; determining a priority of the items with respect to the prognosis in a case where there are a plurality of the items; and establishing a judgment routine in which correlation of the plurality of clinical laboratory test items and the clinical laboratory test value ranges of the test items with the predicted value of the prognosis is stipulated on the basis of the priority, wherein the judgment routine is used as the model.

The invention relates to a detection kit for detecting an MDS/MPN (myelodysplastic syndrome/myeloproliferative neoplasms) related gene group. According to the technical scheme, firstly, a gene group used for screening MDS/MPN related gene mutation is determined, and 17 genes with high directivity are screened out. On the basis, a high-throughput sequencing technology is combined, auxiliary diagnosis of the MDS/MPN beyond the morphology and the flow cytometry can be realized, and particularly, the detection kit has special advantages in evaluation of disease prognosis and anticipation of actioneffects on related target medicines. The provided detection kit of the gene group for used for screening MDS/MPN related gene mutation has the advantages of high detection efficiency, high detectionrate and the like.

The invention discloses a disease gene animal model of primary open angle glaucoma and a construction method thereof. The method comprises the following steps: (1) cloning human an OPTN (optineurin, E50K) mutant gene to an expression vector with a retinal specific promoter c-kit to construct a recombinant mammal expression vector; and (2) introducing the recombinant mammal expression vector into the bodies of mice and screening and identifying to obtain transgenic mice. In consideration of the difference between the mouse OPTN (E50K) and the human gene, the human OPTN (E50K) is introduced into the retinal specific promoter so that the human OPTN (E50K) gene is specifically expressed in the retina of the mouse. The model of primary open angle glaucoma due to the mutation of OPTN (E50K) is constructed, and the laboratory animals are provided for the systemic researches on pathogenic mechanism, development trend and disease prognosis and treatment of primary open angle glaucoma (POAG) due to the mutation of OPTN (E50K) in human beings.

The application relates to methods for compositions for identifying IncRNA loci associated with target genotypes or phenotypes, including desirable plant genotypes or phenotype. The application also relates to regulatory regions and genes associated with drug resistance, such as resistance to BRAF-inhibitors. Such regulatory regions and genes form the basis for methods for identifying resistance to BRAF-inhibitors, which is useful for improving disease prognosis, treatment, and likely outcomes. The regulatory regions and genes are also suitable targets for therapy in melanoma that is resistant to BRAF-inhibitors.

The present invention relates to methods for diagnostics, detection or research analysis of cancer. In particular, the present invention is in the field of analysis of the levels of gene expression in normal or noncancerous cells because of their prosximity to cancer cells. The present invention further provides for analysis of the altered gene expression levels in normal or non-cancerous cancerous cells as an indicator of disease prognosis, staging and grading. The current invention is a means to increase the sensitivity of needle core biopsies to detect the presence of cancer.