121 results about "Patient group" patented technology

The invention relates to the technical field of chronic disease monitoring, and discloses a data abnormal value identification method for an automatic chronic disease monitoring network. The method comprises the following steps: acquiring a physiological index time sequence data set of a target patient at a plurality of monitoring nodes of a chronic disease monitoring network; dividing a time interval according to a data acquisition time sequence and the number of data points to obtain a plurality of sub-time sequence data segments; then associating a first physiological index feature of a first patient node having the same disease feature as the target patient; and finally, outputting abnormal identification data on a preset monitoring interface, wherein the data directly represents the abnormal probability of the physiological indexes of the target patient. According to the method, time sequence data time features and patient group features with the same disease features can be mined, the abnormal value identification effect is optimized, the abnormal probability is visually presented, medical personnel can rapidly master the abnormal conditions of the physiological indexes of the patients, and the method is suitable for automatic monitoring scenes of various chronic diseases.

The invention discloses a brain disease judgment system and method based on fusion of a time sequence affinity graph and a multi-modal network. The method belongs to the technical field of artificial intelligence and medical image crossing. The invention provides a brain disease judgment system based on a time sequence affinity graph fused multi-modal network. The brain disease judgment system comprises a phenotypic feature reconstruction module, a feature extraction module, an affinity graph construction, processing and multi-modal fusion module, a loss construction module and a classification module. The method is used for distinguishing a brain disease patient group from a health control group, and is suitable for a medical image auxiliary diagnosis system, a multi-center brain disease screening platform and an individualized disease risk assessment tool. The core of the method is to solve the problems of insufficient utilization of single-mode information, poor multi-center data robustness and redundant graph structure noise in traditional brain disease classification through time sequence affinity graph construction and multi-mode feature fusion, ROI time sequence data and phenotypic data derived by resting state functional magnetic resonance imaging can be processed, and the accuracy of brain disease classification is improved. The method has application prospects in clinical transformation and multi-center collaborative research.

The invention relates to the technical field of medical information processing, in particular to an acute gastrointestinal hemorrhage secondary sepsis risk prediction system and method, and the method comprises the following steps: retrospectively collecting multi-source clinical data of an acute gastrointestinal hemorrhage patient, and constructing a data set; in the training set, preprocessing the multi-source clinical data and screening key features related to the sepsis risk; based on the key features, adopting a machine learning algorithm or a deep learning algorithm to construct an acute gastrointestinal hemorrhage secondary sepsis risk prediction model; and inputting multi-source clinical data of a patient with acute gastrointestinal hemorrhage to be predicted into the optimized prediction model, and outputting a risk probability value of secondary sepsis of the patient with acute gastrointestinal hemorrhage to be predicted. According to the method, the acute gastrointestinal hemorrhage secondary sepsis risk prediction model based on multi-source clinical data fusion is constructed, so that the sepsis risk of a specific patient group is accurately evaluated.

The invention discloses a concomitant epilepsy depression onset risk prediction system and device, and the method comprises the steps: constructing an array graph model based on multivariable factors, such as marriage conditions, onset age, epileptic seizure frequency, drug treatment types, SEEG epilepsy origin regions, daily life activity ability assessment scale ADL scores, and the like; through the array graph model, the onset risk of concomitant depression of epilepsy patients can be accurately predicted, so that the diagnosis rate of concomitant depression of epilepsy is improved, and a high-risk patient group of depression can be clinically recognized earlier, so that intervention is performed in time, serious consequences caused by depression onset are avoided in time, and the risk of concomitant depression of epilepsy patients is reduced. And a theoretical basis can be provided for clinically and actively intervening the depressive mood of the epilepsy patient.

The invention provides a construction method and system of a heart failure risk prediction model based on medical big data and a machine learning algorithm, and a medium, and the construction method comprises the steps: screening a medical information database, and obtaining a kidney patient group information data set; preprocessing the data set, dividing a training set, a verification set and a plurality of candidate prediction factors related to the heart failure risk, performing importance sorting, and training through a machine learning algorithm according to an importance sorting result and the training set to obtain a plurality of candidate heart failure risk prediction models; and evaluating and comparing the candidate models according to the verification set, and finally obtaining a heart failure risk prediction model. The heart failure risk prediction model specially designed for the chronic kidney patient group is constructed based on the medical big data and the machine learning algorithm, the heart failure risk prediction accuracy for the kidney patient group can be effectively improved, and the method can be widely applied to the technical field of medical information prediction.

The invention discloses a sugar chain marker combination for predicting the recurrence risk of a gastric cancer patient after treatment and application of the sugar chain marker combination. The invention discovers that the levels of six specific N-sugar chains (NGA2F, NG1A2F, NA3, NA3Fb, NA4 and NA4Fb) in blood are obviously related to relapse during definite diagnosis of gastric cancer for the first time. On the basis, a gastric cancer recurrence risk prediction model GC-GPSS is constructed through Cox regression. According to the model, individual risk scores are calculated by using a quantification formula containing a fixed regression coefficient (beta), and patients are divided into a high-risk group and a low-risk group according to a preset threshold. A verification result shows that the model can effectively distinguish patient groups with different recurrence risks. The marker combination, the prediction model and the system provided by the invention provide a brand new tool for realizing earlier and more objective recurrence risk assessment in the early stage of gastric cancer diagnosis and treatment, and have important clinical application value.

The invention relates to the field of data processing, in particular to a drug safety signal mining method and system based on multi-source data. The method comprises the following steps: firstly, constructing a longitudinal event sequence containing medication and diagnosis events for each patient; calculating a time sequence correlation factor of medication and subsequent adverse events, wherein the time sequence correlation factor is in negative correlation with the time interval of the two events and the number of hybrid diagnosis events in the interval; meanwhile, based on the matching degree of the event time interval and the known incubation period probability distribution, obtaining an incubation period rationality weight; combining the drug dosage, the time sequence correlation factor and the reasonable weight of the incubation period, accumulating the related drug event influence before the occurrence of the adverse event, and calculating the accumulated correlation energy of the specific drug and the adverse event; and finally, carrying out non-proportional analysis on the accumulated associated energy of the patient group, realizing drug safety signal mining, and improving the accuracy and reliability of signal identification.

The invention provides an inference thinking chain generation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence, in particular to the technical field of medical artificial intelligence and data generation. According to the specific implementation scheme, the method comprises the steps of obtaining multi-source medical data, and constructing a structured medical knowledge base according to the multi-source medical data; based on the medical knowledge base, generating case data corresponding to different patient groups of diseases; a doctor reasoning process is simulated according to the case data through the large model, a reasoning thinking chain corresponding to the case data is generated and output, and the large model conducts reasoning on the input case data based on the reasoning thinking chain to obtain corresponding case reference information.

The present invention is in the field of ocular disease therapy. In particular, the invention relates to a composition comprising pimecrolimus for use in the topical treatment of moderate to severe blepharitis, in particular in a patient group comprising subjects with a clinical diagnosis of moderate to severe blepharitis, wherein the subjects to be treated have signs and symptoms of moderate to severe blepharitis characterised by swelling of the eyelid margin, ocular debris and ocular discomfort. In particular, the present invention relates to an ophthalmic composition that shows a high patient acceptance and a clinically meaningful improvement in the signs and symptoms of treated patients.

The present invention relates to a novel microorganism and a use thereof for preventing, alleviating, or treating intestinal diseases. The strain according to one embodiment has effects of inhibiting intestinal atrophy and reducing bloody stools, and thus is useful for the prevention, amelioration, or treatment of intestinal diseases. In addition, since the strain is significantly reduced in the patient group, the strain can be used for early diagnosis of intestinal diseases or selection of a risk group.

The present invention relates to a biomarker for predicting immunotherapeutic responsiveness based on spatial transcriptome analysis and uses thereof and, in particular, to: a marker composition for predicting the responsiveness of cancer patients to immunotherapy; a composition for predicting the responsiveness of cancer patients to immunotherapy; a kit for predicting the responsiveness of cancer patients to immunotherapy, comprising the composition; a method for providing information for predicting the responsiveness of cancer patients to immunotherapy; and a method for providing information for predicting the survival prognosis of cancer patients. The biomarker for predicting immunotherapeutic responsiveness, according to the present invention, was discovered by applying spatial transcriptome technology and analyzing cell group-specific gene expression values according to location information of cells in tissue sections, and can more precisely and accurately predict the responsiveness of cancer patients to immunotherapy and the survival prognosis of patients, thus enabling suitable treatments for patient groups, which may result in improved therapeutic effects and a reduction in pain and costs for patients.

The present invention relates to a method for selecting a patient group predicted to respond to immunotherapy cancer treatment. According to the method of the present invention, it is possible to distinguish, in advance, between a patient group that is likely to respond to anti-TIGIT and / or anti-IL-6 immunotherapeutic agents and a patient group that is unlikely to respond to same, enabling effective treatment to be performed, and thus the method is expected to be widely applicable in the medical field.

The invention relates to the field of neurodevelopmental disorders, and discloses an electro-oculogram signal analysis and diagnosis system based on neurodevelopmental disorders, which extracts signal features from pre-processed electro-oculogram signals, and analyzes inter-group differences between signal features of a child patient group and signal features of a healthy child group. The method comprises the following steps: preliminarily screening out signal features with significant statistical difference between two groups, then carrying out severity correlation analysis on the screened signal features, screening and forming a feature list highly related to ADHD severity, and then comparing eye movement features of a child patient group under natural behaviors with eye movement features of a healthy child group, so as to obtain a feature list related to ADHD severity. The method comprises the following steps of: identifying and forming an abnormal signal feature list, fusing the feature list highly related to the ADHD severity and the abnormal signal feature list, calculating a comprehensive abnormal score, drawing an ROC curve according to the calculated comprehensive abnormal score, and carrying out grading evaluation on the ADHD severity of a child patient, so that accurate diagnosis and severity judgment on the ADHD disease are realized.

To enable a patient himself / herself to collect medical information which is related to his / her own symptoms and worries, is made strict and has high reliability without anxiety.SOLUTION: A medical information providing device 30 is mutually connected to a plurality of patient terminals 10 related to a specific disease and a manager terminal 20 operated by a manager of a patient group, and includes a reception part 311 for receiving information related to a request to provide medical information from the patient terminals, and a transmission part 120 for transmitting a request to determine whether the medical information requested to be provided is specific medical information related to the specific disease to the manager terminal. The medical information providing system includes a determination part 312 for acquiring information indicating a determination result from an administrator terminal, a request part 313 for transmitting information related to a medical information provision request to a related external organization 41 related to medical information selected from a plurality of external organizations 40 when the medical information requested to be provided is specific medical information, an acquisition part 314 for acquiring the medical information from the related external organization, and a sharing part 315 for permitting a plurality of patient terminals to access the medical information.SELECTED DRAWING: Figure 1

This invention provides a method, apparatus, device, and readable storage medium for constructing a drug recommendation model, relating to the field of drug recommendation algorithm technology. The method includes acquiring diagnostic and surgical records to extract patient representations; calculating drug allocation weights based on the patient representations; constructing an initial model, training the initial model using the drug allocation weights, and updating the patient representations; recalculating the drug allocation weights based on the updated patient representations, and repeatedly training the initial model using the updated drug allocation weights to obtain a drug recommendation model. This invention addresses the technical problems of existing algorithms that cannot assign different weights based on the importance of neighboring nodes to the current node and ignore patient group characteristics with high representation similarity.

The invention relates to the technical field of data analysis, in particular to a patient medical information intelligent analysis method based on a big data artificial intelligence model, which comprises the following steps: processing large-scale and high-dimensional data according to clinical symptoms and disease degrees in multiple sources such as electronic health records, genome data and the like, extracting key features by utilizing a dimension reduction technology, and analyzing the key features; and information loss is reduced. The non-linear relation in the data can be identified and modeled, the complex mode is disclosed, and the medical data information of the patient can be analyzed more deeply. The refined analysis helps doctors to identify specific requirements of different patient groups, the effectiveness of clinical decisions is improved, and personalized medical treatment and resource optimization are realized.

In the present invention, the accuracy of prediction of the therapeutic effect of the immune checkpoint inhibitor is sufficiently high.　Test data and training data for model training are prepared using the effect of administering an immune checkpoint inhibitor to a patient group and the results of RNA sequence analysis pertaining to the patient group (S11). A combination of a plurality of genes including the NLRC5 gene is selected (S12), and a model is trained using training data relating to the combination of genes (S13). The trained model is tested using the test data (S14). After training all combinations of genes (S15, Yes), a test result is output (S16). A biomarker comprising a combination of genes having high accuracy of prediction of therapeutic effect is determined (S17).

Conventional hospital management support systems are limited to a relative evaluation of patient attributes and medical departments, and do not accurately grasp the profitability and costs of each disease or treatment group. This makes it difficult to perform specific data analysis that directly leads to optimal allocation of medical resources, improved treatment efficiency, and management improvements. [Solution] This invention provides a "patient grouping management module" that groups patients based on specific treatments or diseases, and calculates and visualizes the profitability, costs, and profit margins of each group. Furthermore, by calculating management indicators for each patient group based on medical fees, drug price margins, and material margins, and visualizing and analyzing the results in real time, it builds a management support system that allows doctors and managers to specifically plan resource allocation and treatment strategies.

The application discloses an ablation system and method, which integrates a handheld ablation unit and a guide device. The handheld ablation unit is fixed and directionally guided by the guide device fixed on the surface of the tissue to be ablated. In combination with intelligent image guidance and energy self-adaptive regulation technology, the focus of focused ultrasound is located at the target ablation position, and accurate positioning of the target ablation position is realized. The system utilizes digital twin navigation technology and combines with a millimeter-level micro parallel robot to realize high-precision automatic measurement and ablation site positioning, reduce the workload of manual adjustment of doctors, optimize the treatment process, greatly improve the treatment efficiency, and is suitable for a larger range of patient groups.

The invention relates to the field of clinical molecular typing, and discloses a novel PCOS subphenotype precise diagnosis and treatment platform, which comprises a case sample inclusion and queue construction module; a data acquisition and processing module; the key feature screening module is used for acquiring 12-dimensional feature data of the patient for sub-phenotype clustering; the unsupervised learning module is used for carrying out unsupervised clustering on the 12-dimensional feature data of the cases by adopting a K-Means clustering algorithm and dividing a PCOS patient group into five new subtypes; the XGBoost model construction module is used for constructing an XGBoost model by taking the five new subtypes as classification tags; and the typing prediction module is used for predicting the PCOS subphenotype by adopting the constructed XGBoost model. The invention provides a new set of fine-grained typing and prediction model and a subtype-based precise treatment scheme, and is suitable for subtype distinguishing and treatment guidance of PCOS patients.

The application relates to the technical field of medical image processing, and particularly discloses a lung ventilation function evaluation system based on CT images, which adopts deep learning-based image processing technology to analyze lung CT images at the end of inspiration and expiration of a patient, so as to extract semantic feature representations of the lung CT images, and then, through fine-grained semantic interaction and semantic compensation of the lung CT image features at the end of inspiration and expiration, accurate characterization of a lung deformation field is obtained, and the lung capacity of the patient is intelligently estimated. In this way, non-invasive and accurate evaluation of lung function can be realized, and the method is particularly suitable for a patient group with poor physical conditions or unable to complete standard breathing tests as required.

A computer system is coupled for access to a first database that stores clinical data including, as an item, information acquired on a clinical site and relating to a patient provided with a therapy and a second database that stores non-clinical data including, as an item, information acquired for an investigation purpose and relating to a patient group provided with a therapy. The computer system selects a predetermined number of pieces of the clinical data from the first database, and aggregate the selected predetermined number of pieces of the clinical data, to thereby generate comparative data; and executes, through use of the non-clinical data and the comparative data which are the same in therapy content, statistical analysis for analyzing a difference in the item.

This disclosure relates to the field of medical big data technology, specifically to a method for establishing a prediction model of endpoint events in HCM patients based on LGE radiomics features and clinical predictive features, a method for predicting SCD risk based on the HCM patient endpoint event prediction model, and an apparatus. The method for establishing the model innovatively combines LGE radiomics with clinical features and employs multiple machine learning methods to establish multiple radiomics-clinical feature joint prediction models corresponding to each patient group in the cardiovascular death patient group, heart failure hospitalized patient group, and secondary endpoint event patient group. Then, the optimal radiomics-clinical feature joint prediction model for each patient group is selected as the HCM patient endpoint event prediction model for that patient group. This disclosure achieves the prediction of multiple cardiovascular endpoint events in HCM patients, not limited to sudden cardiac death, and demonstrates good predictive efficacy in various predictions.

The invention relates to a D62A site modified anti-IFN-gamma autoantibody escape mutant and application thereof, and relates to the technical field of biological medicine and immunotherapy. The D62A site modified anti-IFN-gamma autoantibody escape mutant is an IFN-gamma mutant, and compared with a wild type IFN-gamma, the D62A site modified anti-IFN-gamma autoantibody escape mutant not only can escape combination of an anti-IFN-gamma autoantibody, but also retains the activity of combination with an IFN-gamma receptor 1 (IFNGR1), so that the mutant can effectively fill a part of treatment blind areas of a patient group insensitive to other mutation sites, and the treatment effect of the patient group is improved. And the method can also be used for identifying patients sensitive to the mutant in an AIGA patient group, so that a foundation is laid for guiding clinical precise medication.

The invention belongs to the field of pancreatic duct adenocarcinoma prediction, and provides a pancreatic duct adenocarcinoma prediction method and system based on oral flora. The pancreatic ductal adenocarcinoma prediction method based on oral flora comprises the following steps: acquiring relative abundance information of each biomarker in a potential biomarker set for diagnosing pancreatic ductal adenocarcinoma of a to-be-detected person; using the relative abundance information of each biomarker in the biomarker set and a pre-trained multivariate statistical model to obtain a probability value of suffering from pancreatic ductal adenocarcinoma; the method comprises the following steps: performing microbiome difference analysis on saliva, duodenal juice and pancreatic tissue samples of a pancreatic ductal adenocarcinoma patient group and a pancreatic benign disease patient group, and taking an intersection of differential bacteria in the samples to obtain a potential biomarker set for diagnosing the pancreatic ductal adenocarcinoma.

The present invention relates to a novel microRNA for in vitro early diagnosis of mild cognitive impairment and Alzheimer's dementia, and a convergence diagnostic cartridge and diagnostic device using same. The novel miRNA of SEQ ID NO: 1 obtained by molecular analysis exhibits differences in expression levels in the plasma of a normal group, a mild cognitive impairment patient group, and an Alzheimer's dementia patient group, and has the effect of reducing the expression levels of mild cognitive impairment- and Alzheimer's dementia-related proteins. Therefore, by using a novel miRNA present in plasma as a molecular diagnostic index in a non-invasive manner, the present invention enables not only early diagnosis of Alzheimer's dementia but also screening for mild cognitive impairment and Alzheimer's dementia.

The invention relates to the technical field of diabetic complication prediction, and discloses a modeling method of a diabetic complication prediction model. The method comprises the following steps: collecting clinical index data of a patient, including blood glucose level, blood pressure value, blood fat content and renal function parameters, and performing standardization processing to generate a standard data set; time sequence features in the data set are extracted, the short-term fluctuation trend and the long-term change rule of each index are calculated through a sliding window algorithm, and a time sequence feature vector is generated; and inputting the vector into a deep feature extraction network formed by multilayer convolution and long and short term memory units, and extracting high-dimensional time sequence feature representation. Based on the representation, a hierarchical clustering algorithm is used for grouping patient groups, and personalized complication risk prediction sub-models are constructed for different groups. And integrating the outputs of all the personalized sub-models, and generating a final diabetic complication prediction result through a weighted voting mechanism.

The present invention relates to a composition for diagnosing Parkinson's disease, comprising a preparation for detecting a protein comprising GLUT3 in combination with one or more substances selected from the group consisting of USP14, alpha-synuclein, and AIMP2, or a gene encoding the protein. When using the composition for diagnosing Parkinson's disease and the diagnostic kit comprising the composition according to the present invention, it is possible to detect or diagnose Parkinson's disease by distinguishing Parkinson's disease patient groups by a simple blood test. Further, the present invention can be used as a pre-diagnosis for determining whether or not neurological examinations or brain image examinations are to be performed in a large hospital by preferentially applying to patients who exhibit premonition symptoms of Parkinson's disease such as anomalies, sleep disorders, and constipation. In addition, the compound can be used as a means for quantifying the treatment effect of Parkinson's disease.

The invention discloses a health economics evaluation method based on multi-source medical data configuration, and the method comprises the steps: obtaining multi-dimensional medical data through real-time data interaction with a multi-source heterogeneous data terminal, and carrying out the preprocessing of the multi-dimensional medical data; fusing the processed multi-dimensional medical data by using the constructed dynamic health economics evaluation model and generating an evaluation result; based on the evaluation result, personalized treatment schemes are generated for different patient groups, and a multi-objective optimization strategy is generated for the treatment cost, the medical service quality and the hospital operation cost of each patient; and periodically evaluating the cost saving rate, the income growth rate, the patient satisfaction change and the treatment effect index after the implementation of the optimization strategy, and carrying out iterative optimization on the dynamic health economics evaluation model according to an evaluation result. According to the embodiment of the invention, the accuracy of the evaluation result can be improved.

The application relates to the technical field of data processing, and discloses a multi-modal hemodynamic analysis method and device applied to sepsis, which comprises the following steps: monitoring multi-modal data of a plurality of sepsis patients; constructing an adjacency matrix of each view according to sepsis data in each view contained in the multi-modal data of each sepsis patient; performing embedding processing on the adjacency matrices of all views based on a dynamic fusion algorithm of a preset multi-view spectral clustering model to obtain a multi-view embedding matrix; and performing clustering analysis on all sepsis patients based on a preset clustering algorithm according to all multi-view spectral embedding matrices to obtain a clustering result, wherein all sepsis patients in each sepsis patient group contained in the clustering result correspond to a hemodynamic characteristic. It can be seen that the application can improve the classification accuracy of sepsis patients based on diversified monitoring data, and is beneficial to improving the analysis accuracy of the hemodynamic characteristics of each type of patient.