177 results about "Patient group" patented technology

The invention discloses a gastric cancer treatment effect evaluation method and system based on multi-modal data and a medium, and relates to the technical field of disease treatment effect evaluation, and the method comprises the steps: obtaining multi-modal data of a gastric cancer patient group in a treatment process, dividing the gastric cancer patient group, and obtaining a first evaluation group and a second evaluation group; according to the first evaluation group and the second evaluation group, curative effect influence factors related to gastric cancer treatment are obtained, and the curative effect influence factors comprise a first curative effect influence factor and a second curative effect influence factor; according to the curative effect influence factors, curative effect influence factors representing the treatment effect are obtained, and the curative effect influence factors comprise the first curative effect influence factor and the second curative effect influence factor; dividing a first evaluation group and a second evaluation group according to the curative effect influence factor; obtaining a division result, performing treatment effect analysis according to the division result, and determining a treatment effect evaluation parameter; the gastric cancer treatment effect evaluation tool integrating multi-dimensional data is provided, and the prediction efficiency of the gastric cancer treatment effect is improved.

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 discloses an ablation system and method, the system integrates a handheld ablation unit and a guiding device, the guiding device fixed on the surface of an ablated tissue is used for fixing and guiding the direction of the handheld ablation unit, and the focus of focused ultrasound is located at a target ablation position in combination with an intelligent image guiding and energy self-adaptive regulation and control technology; and accurate positioning of the target ablation position is realized. According to the system, the digital twin navigation technology is utilized, the millimeter-level miniature parallel robot is combined, high-precision automatic measurement and ablation site positioning are achieved, the workload of manual adjustment of doctors is reduced, the treatment process is optimized, the treatment efficiency is greatly improved, and the system is suitable for patient groups in a larger range.

The invention relates to the technical field of diabetic health management, in particular to a diabetic health data management system based on a cloud platform. Obtaining a blood sugar amplification factor according to the blood sugar of the patient; correcting the blood glucose amplification factor according to the metabolic equivalent value and the kinetic energy consumption value of the patient to obtain a blood glucose amplification correction factor; obtaining a blood sugar change characteristic value according to the blood sugar amplification correction factor and the diet sugar content before and after the patient diets; and according to the blood glucose change characteristic values of the patient and other patients in the same kind of patient set, obtaining a blood glucose abnormality amplification index. The method comprises the following steps: acquiring a relative abnormal index according to different characteristics of blood glucose of a patient and other patients in the same kind of patient set and a blood glucose abnormal amplification index of the patient; according to the relative anomaly index of the patient, the health management cloud platform is assisted to make medication and diet suggestions for the patient, so that the evaluation accuracy of the cloud platform on the diabetes symptom of the patient is improved, and the possibility of symptom deterioration is reduced.

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.

Provided herein are pharmaceutical compositions and methods for providing blood plasma concentrations of ensifentrine to specific patient group populations including those with modified renal or hepatic impairment.

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 cardiovascular and cerebrovascular disease classification model construction method, which comprises the following steps: receiving dynamic signal data of a cardiovascular and cerebrovascular disease patient, and constructing a dynamic signal matrix; global pathological features of patients with cardiovascular and cerebrovascular diseases are collected to serve as static feature vectors, and time dimensions of the static feature vectors and the dynamic signal feature matrix are unified to construct a fusion feature matrix; constructing a common disease association network; when patient group grouping is carried out, similarity mapping from individuals to groups is carried out through similarity calculation of features of each time slice and a group feature center to generate patient group features, and the patient group features are aligned with a patient feature center matrix; and constructing a deep classifier for cardiovascular and cerebrovascular disease classification, and finally outputting a classification result by the classifier. The method has significant breakthroughs in the aspects of dynamic feature modeling, disease relevance modeling and personalized adaptation capability, and an efficient and accurate technical means is provided for cardiovascular and cerebrovascular disease classification.

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 relates to a saliva sample extractor for the non-invasive collection of saliva samples, particularly from infants. The saliva sample extractor is designed as a pacifier and comprises a teat (1) formed as a shape-elastic bladder with an inner cavity (7) for saliva collection. The wall of the teat (1) has at least one wall section made of a fluid-selectively permeable membrane (6), through which saliva fluid (8) can pass into the inner cavity (7). The teat (1) also has a vent opening closed by a one-way valve (5). Saliva collection occurs passively through natural sucking and chewing movements. The saliva sample extractor ensures hygienic, stress-free, and efficient saliva collection, which is particularly suitable for infants and uncooperative patient groups.

The invention discloses an aortic dissection assessment management method based on multi-source information coupling and related equipment. The method comprises the steps that face image information and electrocardio information of a target patient are obtained, wherein the electrocardio information comprises information collected by an ECG recorder; extracting evaluation association features in the face image information and the electrocardio information of the target patient so as to carry out embedding fusion; and performing aortic dissection risk assessment on the target patient based on the fusion features after embedding fusion. The problems that early evaluation and screening of the aortic dissection are difficult, invasive detection is needed, an existing evaluation source data set lacks diversity and is mainly manifested in the fact that covered patient groups are limited, most data sources are single center data, and complex pathological features of the aortic dissection cannot be comprehensively reflected can be solved.

The invention discloses a method and a system for identifying muscular weakness of a critical patient, and relates to the field of muscular weakness identification. Subjective clinical muscular strength grading of the patient and instrument detection data are integrated, a muscular weakness grade mapping model of multiple parts is established, and the diagnosis accuracy is greatly improved; through a host / guest part mapping model, namely in combination with subjective feeling and objective detection data of a patient, the medullary muscular weakness symptom prone to missed diagnosis in traditional examination can be identified; on the basis of a muscle weakness index data prediction mechanism, a muscle weakness crisis risk can be warned in advance; through a treatment-evaluation-adjustment closed-loop system, dynamic optimization of a medicine scheme is realized, the crisis occurrence rate can be greatly reduced, the dosage of an immunosuppressive agent is reduced, meanwhile, the treatment effect is maintained, and the system is suitable for patient groups managed for a long time; by establishing a mapping system of a part type set and an index grade set, quantitative unified evaluation of different types such as eye muscle type / whole body type and the like is realized, and subjective judgment errors are reduced.

The invention belongs to the technical field of medical treatment, and discloses an auxiliary decision-making method for accelerating rehabilitation surgery clinical path multi-decision multi-objective optimization. The method comprises the following steps: constructing a data set of preoperative states and medical decisions of a patient; unsupervised clustering is adopted, the clustering method is beneficial to dividing the patients into different phenotypes and risk levels according to unique features of the patients, and the obtained clusters provide valuable insights for heterogeneity of patient groups. And then, according to the decision data of each clustering phenotype, constructing a Bayesian network model, and adding some regulations in the construction process to trim the network, so that the constructed Bayesian network model better accords with clinic. And finally, inferring the state posterior probability of each node by using the Bayesian network, and inferring a decision scheme of each risk group by using the support degree so as to accelerate the postoperative rehabilitation process of the patient, reduce the hospitalization time of the patient and reduce the hospitalization cost.

An Interferon Gamma Inducible Protein 16 (IFI16) mutant gene and its use as a marker for predicting, diagnosing, or prognosticating risk or severity of chronic liver disease is described. As a result of performing genomic analysis on NAFLD and NASH patient groups, it was confirmed that the frequency of IFI16 single-nucleotide variants (SNVs) including rs2276404, rs73021847, rs7532207, and rs6940 was increased, and the expression of the IFI16 mutant gene was increased depending on the disease stage of liver disease. The IFI16 SNV was highly expressed in infiltrating macrophages, playing a role in macrophage-induced inflammatory processes, and the IFI16 variant bound more strongly to dsDNA than wild-type IFI16, exacerbating the impaired mitochondrial DNA-sensing response signaling of the IFI16-PYCARD-CASP1 pathway. Thus, the IFI16 mutant gene may be used for predicting, diagnosing, or prognosticating risk or severity of chronic liver disease.

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 provides a mental disease classification method and system based on an individual difference structure covariant network and machine learning, and belongs to the field of mental disease classification. The problem of classification performance bottleneck caused by heterogeneity of mental diseases in the prior art is solved. The method comprises the following steps: acquiring a structural magnetic resonance T1 weighted image, and preprocessing the image; performing brain region segmentation on the pre-processed T1 image based on an AAL brain map, and extracting the gray matter volume of each brain region; constructing an IDSCN network by calculating the Pearson's correlation coefficient of the brain grey matter volume of the paired brain regions; calculating the area under a node topological attribute curve of the IDSCN network; screening node attribute indexes with statistical differences between the patient group and the healthy control group through double-sample t test; and taking the screened node attribute indexes as feature vectors, and inputting the feature vectors into a support vector machine classification model for disease classification. The method is mainly used in the medical image processing field.

The invention discloses an internal medicine emergency patient medical assistant system and method based on artificial intelligence, and relates to the technical field of medical treatment.The method comprises the steps that multiple sensor sets are arranged, and infusion physiological state data are collected in real time; carrying out data preprocessing and standardized normalization processing through an AI preprocessing strategy to form a standard infusion physiological state data set; inputting the standard infusion physiological state data set into the infusion time prediction model in the constructed infusion time prediction model, realizing high-precision prediction of the infusion completion time prediction value Tinf of the patient in combination with nonlinear attenuation modeling, and dividing the internal medicine emergency patients who are about to complete infusion through a preliminary comparison evaluation mechanism so as to obtain an infusion completion time prediction value Tinf; therefore, a patient group about to complete infusion is recognized in advance, accurate early warning and pre-planning of a liquid changing task are achieved, and the occurrence rate of adverse events such as infusion empty passing, blood return and vein blockage is remarkably reduced.

The present invention relates to the field of traditional Chinese medicine technology, and in particular to a Chinese medicine composition, granule preparation and preparation method thereof for treating carotid atherosclerosis combined with hyperlipidemia. The Chinese medicine composition for treating carotid atherosclerosis combined with hyperlipidemia is mainly prepared from the following components in parts by weight: 8-12 parts of Chuanxiong, 8-12 parts of red peony root, 3-7 parts of coptis root, 8-12 parts of red yeast rice and 3-7 parts of roasted liquorice. The Chinese medicine composition provided by the present invention can have good therapeutic effects on the patient group with carotid atherosclerosis combined with hyperlipidemia, which is a common and frequently occurring disease in the field of cardiovascular disease prevention and treatment. In addition, the present invention prepares the Chinese medicine composition into a granule preparation in a certain manner, ensuring the molding rate, solubility rate, bulk density, angle of repose and moisture absorption rate of the preparation, thereby ensuring the efficacy of the preparation, extending the storage time of the product, taking into account the patient's acceptance, and easily realizing large-scale production.

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 embodiment of the invention provides a patient data real-time quality control method and device, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: acquiring patient data, wherein the patient data comprises patient information and test result data of a patient on a target test item; inputting the patient information into a target regression model corresponding to the target inspection item to obtain prediction result data; determining a residual sequence according to the test result data, the prediction result data and a historical residual result; a quality control range is determined by smoothing the residual error sequence; and if the inspection result data exceed the quality control range, triggering an alarm indication. Therefore, by introducing the target regression model, the complex relationship between the patient information of the patient and the inspection result data can be automatically learned, multiple quality control schemes do not need to be formulated for different patient groups, the quality control process is remarkably simplified, and the complexity of quality control steps is reduced.

A method of operating an apparatus operated by at least one processor, the method including constructing a medical data library that generates a table for each data item by applying disease-specific fabric conditions including data items extractable from a clinical data warehouse and medical conditions including disease-specific patient groups for disease-specific research; obtaining a database generation request for research on a specific disease; determining specific data items related to research on the specific disease from the medical data library; applying the fabric conditions for the specific disease to generate a table for each of the specific data items using the limited patient group data for the specific disease; and generating a database for research on the specific disease using the tables of the specific data items.

The present invention belongs to the field of information processing of medical or health data and images, and particularly relates to a research method and application of the rich club of the brain functional network for juvenile myoclonic epilepsy. By calculating the Pearson correlation coefficient, the brain functional networks of two groups of subjects are constructed, and the rich connections, feeder connections and local connections of the brain networks of the JME patient group and the normal control group are calculated. The differences in the rich-club organizational structures of the brain networks of the two groups of subjects are analyzed to facilitate the discovery of abnormal rich club tissues and further understand the pathophysiological mechanism of JME.

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