221 results about "Patient group" patented technology

The invention relates to the technical field of medical data analysis, in particular to a lung cancer dynamic evaluation method and system based on immune monitoring, and the method comprises the following steps: collecting immune index data obtained in the immune monitoring process of a patient, analyzing the mean value change between adjacent segments of an immune index data time sequence, marking an abnormal fluctuation region, and calculating the immune index data; and generating abnormal fluctuation section data. According to the method, the immune monitoring data of the patient are divided into the time sequences, the abnormal fluctuation sections and the abnormal change points are accurately extracted through the mean value change analysis and sliding window method, the remarkable fluctuation of the immune system can be captured more rapidly and accurately, and therefore the timeliness of dynamic monitoring is improved. And further in combination with statistical feature analysis and similarity matrix calculation, differentiated grouping of immune indexes among patients is refined, and personalized evaluation for patient groups with different immune states is facilitated.

The invention relates to the technical field of anesthesia effect evaluation, in particular to an anesthesia effect evaluation system based on big data, which comprises a physiological data processing module, a patient grouping and analysis module, an anesthesia risk evaluation module and an anesthesia adjustment support module. And collecting physiological indexes and types and doses of anesthetic drugs. According to the method, the real-time processing capacity of physiological data is enhanced by integrating historical health records and operation information, accurate evaluation of the instant effect of anesthetic is provided by deeply analyzing time sequence changes of heart rate and blood pressure, the response of different patient groups to the anesthetic can be recognized through grouping analysis, and the accuracy of the anesthetic is improved. Therefore, a targeted anesthesia scheme is realized, the anesthetic injection time and dosage are analyzed by using the LSTM network, the dynamic prediction of drug response is optimized, a doctor is supported to adjust the anesthetic dosage according to real-time data, the precision and safety of anesthesia management are greatly improved, and the anesthesia side effect risk is reduced.

The invention discloses a myocardial infarction patient risk assessment model construction method and system, and particularly relates to the field of cardiovascular disease prediction, and the method comprises the following steps: I, collecting modal data of a hospital information system, an image filing system and wearable equipment, and integrating the modal data; according to the method, the accuracy of myocardial infarction risk prediction can be optimized, risk assessment not only depends on statistical characteristics, but also fuses pathophysiological mechanisms, the clinical interpretability of prediction is improved, the limitation that a traditional data-driven method is only based on correlation modeling is avoided, the optimal treatment scheme can be dynamically adjusted, data-driven personalized medical treatment is achieved, and the risk assessment efficiency is improved. The scientificity of intervention decision making is improved; the method can quickly adapt to new patient data, improve the individualized prediction ability, improve the data utilization rate, enhance the generalization ability of the model for different patient groups, avoid training the whole model from zero, greatly reduce the consumption of computing resources, make up for the problem of insufficient data of a single patient, and improve the prediction ability of rare cases.

The invention discloses an acute kidney injury risk early warning system, and relates to the technical field of clinical decision support systems. In order to solve the problems that in the prior art, due to the fact that a large amount of data preprocessing and model training need long-time data processing and model operation, the early warning opportunity is not accurate, the real-time performance is low, personalized early warning prompting cannot be carried out for patient groups, and early warning fatigue and early warning desensitization are likely to be caused. Through data preprocessing, model construction and risk analysis, real-time monitoring and accurate prediction of the AKI risk of a patient are realized, the early recognition rate is improved, the early warning decision unit generates specific early warning information according to risk scores and kidney function change rules and provides targeted treatment suggestions, and meanwhile, through an early warning threshold adjustment and approach matching module, the early warning decision unit can perform early warning on the AKI risk of the patient. The early warning strategy is optimized, the response speed of medical staff and the compliance of patients are improved, and the user interaction unit continuously optimizes the early warning system through a feedback mechanism.

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 method for predicting the social distraction risk of a colorectal cancer chemotherapy patient. The method comprises the following steps: firstly, acquiring an initial sample data set of colorectal cancer chemotherapy patients, carrying out potential profile analysis and subject working characteristic curve analysis on the patients, dividing a colorectal cancer chemotherapy patient group into a group without social distraction and a group with social distraction, and defining a social distraction threshold according to group division; on the basis, performing variable screening on an initial influence factor in the initial sample data set, determining main influence factor data and a corresponding data set, and then training a constructed initial prediction model of the social distraction risk based on the main influence factor data and the corresponding data set to obtain an initial prediction model of the social distraction risk; according to the method, a fully trained social distraction risk prediction model is obtained, and the social distraction risk of the patient is accurately predicted based on the fully trained social distraction risk prediction model, so that the risk that the colorectal cancer chemotherapy patient encounters the social distraction in the treatment process can be effectively prevented in advance.

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 service quality evaluation, and discloses an internet nursing service evaluation system based on a Kano model, which comprises a nursing data module and a service evaluation module, the system is in network connection with a database through a nursing data module, obtains medical record data, nursing records and satisfaction questionnaires of all patients, classifies the medical record data, the nursing records and the satisfaction questionnaires to form a data set, a service evaluation module analyzes the basic frequency of nursing services needed by each patient and the actually accepted nursing frequency, and finally obtains the nursing service according to the questionnaire data set. The service evaluation module is used for analyzing the change trend of the patient on article preparation satisfaction, service technology satisfaction and nursing plan satisfaction, generating a corresponding fluctuation data set and comprehensively analyzing patient service experience, and the service evaluation module is used for training to generate a Kano model which is used for evaluating nursing services and marking priority levels required by the patient; a nursing service management platform can be helped to fully understand medical requirements of different patient groups, and the efficient nursing service quality is good.

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 techniques may include receiving patient data of patients of a service facility, each patient assigned to a service unit. In addition, the techniques may include inputting patient data into a classification model trained to output classifications. The techniques may include determining a classification for each patient. Moreover, the techniques may include assigning each patient to a patient group based at least in part on a classification common with members of the patient group. The techniques may include determining a recommendation associated with at least one of: (1) servicing a first service resource associated with a third service unit, or (2) procuring a second service resource associated with a fourth service unit. This recommendation can be based at least in part on the classifications. Further, the techniques may include presenting on a user device of a patient care provider for coordinating patient care among service units of the service facility.

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 belongs to the technical field of breast cancer treatment, and particularly relates to a patient layering method based on a breast cancer single cell space proteome multicellular enrichment mode, which comprises the following steps: S1, breast cancer single cell space proteome image data cell segmentation and cell type identification; s2, constructing a cell network diagram, and simulating a tumor microenvironment; s3, detecting closely connected cell communities, and extracting cell community characteristics; s4, carrying out cell community feature clustering, and representing a tumor microenvironment distribution mode of the patient; and S5, calculating the similarity of the tumor microenvironment distribution modes of the patients, and layering the patients. By means of the method, the patients can be layered, and prognosis of the patient groups has significant difference.

The invention discloses an AI auxiliary inquiry method, and relates to the technical field of intelligent inquiry, personalized diagnosis is realized through combination of a dynamic weighting model and individual features of a patient, electronic health records and medical history data of the patient do not only serve as static references any more, but directly influence the weight of each symptom in diagnosis, and the diagnosis accuracy is improved. The importance of symptoms can be dynamically adjusted according to different patient groups, high-risk symptoms of patients can be more accurately recognized through individualized dynamic weighting, reasonable diagnosis inference is made according to individual characteristics of the high-risk symptoms, the possibility of misdiagnosis or missed diagnosis is greatly reduced, meanwhile, a symptom relation map is introduced, and the diagnosis accuracy is improved. According to the method, the mutual dependency relationship among different symptoms can be visualized, the symptom relationship map presents the correlation degree among the symptoms in the form of nodes and edges, and a doctor can quickly know the overall symptom expression and potential cause of disease of a patient through the map, so that diagnosis is more efficient and accurate.

The invention discloses a method for identifying somatic depression by using brain structure and function abnormality, which comprises the following specific steps: selecting depression patients into groups, dividing the depression patients into a somatic depression patient group and a non-somatic depression patient group according to whether the score of a health questionnaire somatic symptom group scale of the patients is greater than or equal to 10, and recruiting a health control group at the same time. The three groups of subjects are subjected to resting state structure and functional magnetic resonance imaging scanning, and the brain grey matter volume and the brain region with significant difference are analyzed through a voxel-based morphological index and functional connection method. Pearson correlation analysis is adopted in SPSS26.0, and the relevance between the gray matter volume and the abnormal function connection of the patients in the SD group and the NSD group and the PHQ-15 scale score is compared. The method has the advantages that the illness state of the depression patient can be found in time, the patient can be treated at the best treatment opportunity, and early recognition of the body symptoms of the depression is beneficial for reducing waste of medical resources and relieving the pain of the 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 present disclosure provides systems and methods for detection of hyperkalemia from Lead I electrocardiogram (ECG) signals, particularly in patients with critically high potassium levels. The methods and systems of the disclosure are further demonstrated to identifying hyperkalemia across both acute kidney disease (AKD) and chronic kidney disease (CKD) patient groups, with performance varying according to serum potassium levels.

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.