539 results about "Disease patient" patented technology

The invention discloses a construction method of a dyskinesia phenotype classification model of a Parkinson's disease patient and a diagnosis system, and belongs to the field of medical auxiliary diagnosis devices. The system comprises an image acquisition module and a phenotype classification module, wherein the classification module is composed of a space-time diagram convolution feature extraction module, a hierarchical node fusion module, a functional brain network construction and feature extraction module, a local brain region feature screening module, a hierarchical brain feature pairing fusion module and an output module. According to the method, multi-modal brain image data are fused, space-time and complex relation characteristics of a brain region are deeply mined, a key lesion brain region is positioned, and dyskinesia phenotypes are accurately classified by training a neural network. The system can significantly improve the diagnosis accuracy of the Parkinson's disease dyskinesia phenotype, has good expansibility and adaptability, and provides a scientific basis for early diagnosis and personalized intervention. The invention further relates to an operation method of the system, an auxiliary diagnosis device and a computer readable storage medium.

The invention provides a social worker service method and system based on long-term diseases, and the method comprises the steps: determining a patient and case data thereof, and selecting an evaluation service group; performing pathological assessment and psychological assessment on the patient through the assessment service group; determining a service object according to an evaluation result; determining whether the family member is included in a second service object needing social worker service; determining a current social worker service stage of the first service object and generating a demand for a social worker; according to the requirements, the schedules of the social workers and the skill labels of the social workers, one or more social workers meeting preset conditions are searched for providing social worker services for the patients and / or family members. According to the method, the specific requirements of the patients and the family members are accurately analyzed through the intelligent matching algorithm, high-quality and personalized social work services can be effectively provided for the long-term disease patients and the family members thereof, it is ensured that each patient and the family members thereof can obtain customized service schemes, and the specialty and pertinence of the social work services are greatly improved.

The invention discloses application of citrullinated endolectin-1 polypeptide in preparation of rheumatoid arthritis diagnosis products, and belongs to the technical field of rheumatoid arthritis diagnosis. The amino acid sequence of the citrullinated endolectin-1 polypeptide is GD-Cit-WSSQQGSKAVYPE, and the amino acid sequence of the citrullinated endolectin-1 polypeptide is GD- The product is used for detecting the content of the anti-citrullinated endolectin-1 polypeptide antibody in a biological sample of a patient, and the antibody level is remarkably different from that of normal healthy people and other common rheumatism immune disease patients easily confused with rheumatoid arthritis in the body of an RA patient. The kit has good sensitivity for common RA, RA with normal ESR and CRP, and anti-CCP antibody / RF negative RA patients, and has good supplementary diagnostic value for RA diagnosis.

The invention relates to the technical field of medical informatics, in particular to an ADL ability training data management system based on neurological disease patients. The system comprises a memory and a processor, and the processor executes computer programs stored in the memory so as to realize the following steps: acquiring attitude images and scales of normal personnel and neurological disease patients in the ADL ability training process; according to the position difference, the depth difference and the time difference between the joint points in the to-be-evaluated image and the joint points in the reference image, evaluating the possibility value that the to-be-evaluated image and the reference image are in the same action, and screening a contrast image; synthesizing the possibility value that the to-be-evaluated image and all the contrast images are in the same action and the acquisition time of the to-be-evaluated image to obtain a motion completion index; and determining an effective evaluation value of training according to the difference between the change characteristics of each type of indexes of the neurological disease patient at each moment and other indexes and each type of indexes. According to the invention, an accurate comprehensive result of ADL capability training data can be obtained.

The invention discloses a cognitive feature extraction and classification method and system based on electroencephalogram signals, and the method comprises the steps: constructing a cognitive divergence mapping network, so as to integrate a multi-branch brain region topology module and a neurodynamics physical information network module; the multi-branch brain region topology module divides detection branches of five brain regions (frontal lobe, central lobe, parietal lobe, occipital lobe and temporal lobe) according to 10-20 systems, and through feature extraction of different brain region branches, the feature learning ability of the Alzheimer's disease patient under the condition of cross-brain region signal heterogeneity is remarkably improved; the neurodynamics physical information network module can effectively extract changes of low-frequency and high-frequency components in electroencephalogram signals of the Alzheimer's disease patient by introducing frequency band separation, Fourier transform power spectrum constraint, an adaptive weighting mechanism and neural representation embedding in a frequency spectrum congruence space. The accuracy and generalization of electroencephalogram signal analysis are remarkably improved, and an efficient and non-invasive detection tool is provided for early diagnosis of the Alzheimer's disease.

The invention relates to the technical field of information processing, in particular to a medicine conflict automatic detection and prescription optimization system for old multi-disease patients. According to the system, a data management module is used for collecting individual data of patients and group data of co-diseased reference groups and receiving a planned medication scheme; the parameter calculation module is used for identifying a target medicine combination with time overlapping in the patient medication record and the planned medication scheme; determining a basic risk index according to the group medication response data, the patient medication record and the work and rest text data; determining a lag risk coefficient according to the physiological indexes and the drug metabolism data; determining group medication associated risk parameters according to atypical reactions and group medication records in the medication reaction data; the risk fusion module is used for determining a drug use conflict risk value based on the basic risk index, the lagging risk coefficient and the group drug use associated risk parameter; and the prescription optimization module is used for generating an optimal medication scheme, so that the generated optimal scheme is more suitable for the actual life of the patient.

The invention discloses a Parkinson's disease detection model construction method and platform based on voice features, and the method comprises the steps: carrying out the normalization processing of an encoded questionnaire result and extracted voice features, and obtaining a normalized questionnaire result and voice features; respectively converting the normalized questionnaire result and the voice features into high-dimensional representation vectors through an embedded layer; obtaining an attention score between each embedding vector and questionnaire embedding, and normalizing the score through a softmax function; according to the attention score, performing weighted fusion on the embedded vector of the questionnaire result and the embedded vector of the voice feature to obtain a fused feature vector; and inputting the obtained fusion feature vector into a risk assessment model to obtain the risk prediction of the Parkinson's disease. Through the technical scheme provided by the invention, a convenient and accurate prediction service can be provided for the Parkinson's disease patient.

The invention relates to the technical field of image data processing, in particular to a rheumatism immune disease joint injury assessment system based on image recognition. The system comprises a joint image preprocessing module, an image segmentation and injury characteristic analysis module, a bone erosion and synovial thickening evaluation module and a joint injury evaluation and injury visualization module, can obtain joint image data of the rheumatism immune disease, perform size scaling and image denoising processing, and perform joint part image segmentation at the same time. Image data of the joint part region of the rheumatism immune disease is obtained; and performing joint injury structural feature analysis and joint image injury quantitative evaluation on the joint partial region image data of the rheumatism immune disease to obtain a joint image injury score corresponding to the rheumatism immune disease patient, and performing evaluation to obtain a joint image injury type to perform joint injury visualization so as to obtain a joint injury visual image result. The joint injury degree corresponding to the joint image of the rheumatism immune disease can be more accurately detected.

The invention relates to the technical field of cognitive impairment image processing, in particular to an Alzheimer's disease early screening method which comprises the following steps: extracting time and space coordinates of a fixation point from a visual glancing task participated by an Alzheimer's disease patient, recording direction change of a jumping behavior in the time and space coordinates, and calculating the direction change of the jumping behavior in the time and space coordinates; and generating jump behavior distribution data. According to the method, by extracting the time and space coordinates of the fixation point and combining the direction change of the jump behavior, comprehensive description of the dynamic law of the fixation behavior is realized, and high-dimensional support is provided for fine analysis of the fixation behavior of a patient in the task execution process. By constructing jump event distribution, track dynamic change and complexity quantification of the gazing path, the offset characteristic, the jump rule and the path fractal dimension in the gazing behavior are accurately revealed, and quantitative description of the abnormal behavior is provided for screening.

The present invention relates to methods of treating patients with primary immunodeficiency diseases and disorders, using a CXCR4 inhibitor.

The invention relates to an acute abdominal disease pre-examination triage progressive prediction method, system and device, and the method comprises the steps: collecting the data of an acute abdominal disease patient, and carrying out the data preprocessing, and obtaining an acute abdominal disease data set; inputting the acute abdominal disease data set into a plurality of basic learners for training based on a stacked ensemble learning method to obtain meta-features; the meta-features and the acute abdominal disease data are gathered and input into a meta-model learning device for prediction, and a pre-examination triage prediction result is obtained. The method is a triage scheme of multi-step prediction and progressive correction, according to the scheme, according to the time progress of the emergency examination process of an emergency patient as the sequence, a stacked integrated learning method is applied to four acute abdominal disease data sets, an emergency classification result is output, and when the triage process is carried out in the clockwise direction, the triage classification result is obtained. The classification prediction result of the triage prediction system is dynamically updated; the method conforms to the actual triage process, omission and misjudgment of a traditional single-time fixed manual triage method can be avoided, and the accuracy of acute abdominal triage is improved.

The invention relates to a Parkinson's disease treatment effect prediction method based on a multi-modal image model and a related device. The method comprises the following steps: acquiring a multi-modal vector of a Parkinson's disease patient; aligning the multi-modal vectors on a time axis, and constructing a time point data element sample sequence; deploying a double-flow cross attention encoder for a sample sequence in each time point data element, and outputting a fused multi-modal feature through the double-flow cross attention encoder; and connecting the fused multi-modal features with digital clinical treatment scheme vectors corresponding to corresponding time points to form time point comprehensive feature vectors, inputting the time point comprehensive feature vectors to a time sequence information aggregation gating circulation unit, outputting final time point aggregation features, and inputting the final time point aggregation features to a multi-task adaptive prediction head. A UPDRS total score or a specific sub-scale score for the patient at a future preset point in time is predicted. According to the method, time sequence modeling is carried out on multi-mode and multi-time-point data, so that the accuracy and interpretability of Parkinson's disease treatment effect prediction are improved.

The invention provides an intelligent diagnosis and treatment method, system and equipment based on individualized blood pressure management, a computer readable storage medium and application thereof, and relates to the field of intelligent medical treatment. The method comprises the following steps: acquiring blood pressure, electroencephalogram signals, cerebral blood flow velocity and arterial blood pressure data of a subject; analyzing based on the electroencephalogram signal and cerebral blood flow velocity data of the subject to obtain a neurovascular coupling index; obtaining an individualized blood pressure range based on the nerve and blood vessel coupling index and the arterial blood pressure data of the subject; whether the blood pressure of the subject is within the individualized blood pressure range or not is judged, and when the blood pressure of the subject is not within the individualized blood pressure range, information prompt for blood pressure control is output. According to the method disclosed by the invention, the blood pressure management is guided by determining the individualized blood pressure range of the cardiovascular and cerebrovascular disease patient, valuable resources are provided for researchers and clinicians in the field of cardiovascular and cerebrovascular disease diagnosis and treatment, and the method has important scientific research significance and clinical value.

The invention provides a big-data-based Alzheimer's disease old-age care platform management method and system, and relates to the technical field of old-age care platform management.The method comprises the steps that patient behavior data information is received, missing data points are deduced according to pre-stored patient behavior file historical behavior pattern data and patient behavior data information, and the missing data points are sent to a server; by evaluating the reliability degree of the event chain and comparing the event chain with a dynamically generated reliability evaluation threshold value, when a comparison result reaches an early warning standard, early warning information with a reliability quantitative index is generated, and the problem that an existing platform is poor in reliability under a non-ideal operation condition is effectively solved. The problems of reliable transmission and accurate interpretation of key information are solved, and early warning failure caused by data deviation or missing is avoided, so that the nursing quality and safety of the Alzheimer's disease patient are remarkably improved, and the optimal time for early intervention and intervention is provided for nursing personnel.

The invention belongs to the technical field of artificial intelligence, and discloses a signal data processing method of an Alzheimer's disease and frontotemporal dementia diagnosis method, which comprises the following steps: selecting an electroencephalogram channel by adopting PSO (Particle Swarm Optimization) and converting computer signal time sequence data into a time-frequency diagram by using wavelet transform, so as to realize accurate diagnosis of Alzheimer's disease patients and healthy subjects. And patients with frontotemporal dementia and healthy subjects as well as patients with Alzheimer's disease and patients with frontotemporal dementia can be accurately and efficiently classified. The PSO is used for selecting a channel combination with good electroencephalogram energy and reducing information interference caused by redundant electroencephalogram channels, a time-frequency graph obtained through wavelet transformation can provide time information and frequency information of electroencephalogram signals at the same time, the model can learn features more effectively, and the classification performance of the model is improved. According to the method disclosed by the invention, the accuracy of classification of the Alzheimer's disease patients and healthy subjects, the frontotemporal dementia patients and healthy subjects, and the Alzheimer's disease patients and the frontotemporal dementia patients is remarkably improved.

The invention belongs to the technical field of risk prediction, and particularly relates to an Alzheimer's disease risk prediction method, device and system. The method comprises the following steps: acquiring various detection data of a to-be-detected object; inputting the baseline data and the time accumulation operator into a first classification model, and outputting a first risk prediction result of the to-be-detected object with the time accumulation operator; inputting the follow-up data and the time accumulation operator into a second classification model, and outputting a second risk prediction result of the to-be-detected object with the time accumulation operator; and inputting the first risk prediction result and the second risk prediction result into a full connection layer, and outputting a third risk prediction result of the to-be-detected object at present and within a period of time in the future. The method is high in prediction precision and high in practicability, is helpful to timely make related preparations and prevention measures through early diagnosis of the Alzheimer's disease patients so as to slow down disease deterioration and improve the survival probability of the to-be-detected object, and has a good application prospect.

The invention relates to the technical field of data analysis, in particular to a disease consultation system based on big data. The system comprises a consultation request health data acquisition module, a disease health association map construction module, a consultation disease department navigation analysis module and a user disease consultation application agreement module. The method comprises the following steps: acquiring a user consultation request of a disease patient, and acquiring corresponding personal disease health data, patient historical health data and public medical health data; performing user current health state association analysis and disease health association map construction on the personal disease health data to generate a consultation patient user personal disease health association map, and performing consultation disease department navigation analysis and disease consultation application at the same time; and acquiring patient consultation queuing data and online inquiry active data to perform re-queuing consultation application and disease consultation processing so as to complete a disease consultation result of a corresponding consultation patient user. According to the invention, the efficiency and accuracy of disease consultation service can be significantly improved.

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 a Parkinson's disease walking state recognition method based on tensor singular value decomposition and automatic hyper-parameter optimization. The Parkinson's disease walking state recognition method comprises the following steps: acquiring a sensor signal of a target person; constructing a tensor according to the sensor signal; performing t-SVD decomposition processing on the tensor to obtain a frequency domain core tensor; the frequency domain core tensor and the sensor signal are subjected to feature extraction, the extracted features are input into a trained classification model, the binary classification recognition result of the Parkinson's disease patient and the healthy person is obtained, and the trained classification model is obtained through training of a training set marked with a patient and health contrast label. According to the method, efficient dimension reduction and noise suppression are realized by using t-SVD, and adaptive optimization is performed on the key hyper-parameters of the classification model in combination with an automatic machine learning technology, so that the dichotomy recognition accuracy and robustness of the Parkinson's disease patient and the health control are improved.

The invention relates to the technical field of cell biology, in particular to a construction method and application of intestinal organs for children with Crohn's disease, the intestinal organs for children with Crohn's disease are successfully constructed by utilizing intestinal tissues from children with Crohn's disease, and the intestinal organs have various characteristic cells of the intestinal tissues; besides, immune cells are extracted from peripheral blood of a child patient homologous with intestinal tissues, a co-culture system is established with the organ-like model, the intestinal immune microenvironment of the child patient suffering from the Crohn's disease can be reproduced, and the system provides a new model for related mechanism exploration and drug screening of the child Crohn's disease.

The invention discloses a pharmaceutical composition which comprises anisodine or salt thereof and 2-borneol or related substances thereof. The pharmaceutical composition provided by the invention has a synergistic effect in treatment of cardia-cerebrovascular diseases, especially central nervous diseases such as acute ischemic stroke, can effectively improve the effect of patients with cerebrovascular diseases, especially central diseases such as acute ischemic stroke, and improves the quality of life.

In various embodiments, provided herein are methods, devices and systems for transcatheter mitral valve replacement in a double-orifice mitral valve. These methods, devices and systems are used to treat patients with mitral valve disease, particularly those who have had failed edge-to-edge leaflet repair, or patients presently considered anatomically unsuitable for edge-to-edge leaflet repair alone.

The present invention provides a method for extracting a neuroimaging biomarker based on an interpretable ensemble three-dimensional convolutional neural network (3DCNN) to address limitations in the prior art. The present invention derives a novel neuroimaging biomarker P-score from prediction results obtained by an ensemble three-dimensional convolutional neural network model. The solution can help researchers to conduct studies on longitudinal trajectory changes of structural magnetic resonance imaging (sMRI) during the progression of Alzheimer's disease, and analyze an association of the longitudinal trajectory changes with neurodegenerative changes of Alzheimer's disease subjects. The extracted neuroimaging biomarker can provide a basis for predicting a sequence of intervention of brain regions in the neurodegenerative changes of Alzheimer's disease patients and upcoming clinical symptoms.

The invention relates to a data set distribution difference-oriented electronic medical record data representation learning method and system, and the method comprises the steps: pre-training a source domain teacher model through a source domain data set of an existing disease, and extracting the health state representation information of a source domain disease patient; training a domain invariant feature extractor as a transition model, modeling general features through an adversarial training strategy, and establishing an independent extraction channel for private features to realize feature alignment among different domains; and migrating parameters of the transition model to a target domain emerging disease prediction model, and performing fine tuning in combination with target domain data to realize accurate prediction of emerging diseases. Through domain invariant feature extraction and model migration optimization, the generalization ability and prediction precision of the model on different data sets are significantly improved, clinical results of patients can be accurately predicted, accurate support is provided for medical decision, and a reliable foundation is laid for the cross-domain prediction problem in medical data analysis.

The invention relates to the technical field of data analysis, in particular to an intelligent medical patient monitoring and health data analysis system, which comprises a physiological parameter monitoring module, a health data analysis module and a data analysis module, the physiological data cross analysis module is used for receiving the real-time data from the physiological parameter dynamic monitoring module, constructing a disease specificity model and establishing a correlation analysis mechanism among the data according to different internal medicine diseases; and the illness state fluctuation and chronic deterioration trend identification module is used for monitoring the periodic fluctuation and chronic deterioration trend of the illness state of the internal medicine disease patient based on the correlation result, and identifying an early signal of potential chronic disease deterioration or sudden attack. According to the invention, potential deterioration signals among different physiological parameters can be revealed, and doctors can be helped to master disease evolution more comprehensively. Through high-dimensional feature mapping, possible disease deterioration can be predicted in advance, and the health management efficiency of the patient is improved by making a personalized treatment scheme.

The invention discloses a method and device for recognizing an Alzheimer's disease patient based on voice signals. The method comprises the following steps: acquiring a voice signal of a target object, extracting and processing a shallow feature and a deep feature of the voice signal by using a first model to obtain a first result, segmenting and processing the voice signal by using a second model to obtain a second result, and processing the voice signal and text information by using a third model to obtain a second result. The text information is obtained by transcribing the voice signal, and the first result, the second result and the third result are all used for indicating whether the target object is the Alzheimer's disease patient; and jointly determining whether the target object is the Alzheimer's disease patient according to the first result, the second result and the third result. According to the method and the device, the technical problems that a method for detecting the Alzheimer's disease in related technologies does not support wide-range application and the detection result is inaccurate are solved.

The present invention relates to the treatment of subjects having CD20-positive cell proliferative disorders (e.g., B cell proliferative disorders, such as non-Hodgkin's lymphomas or chronic lymphocytic leukemia). More specifically, the invention pertains to the treatment of subjects having a B cell proliferative disorder by subcutaneous administration of mosunetuzumab as a monotherapy or in combination with lenalidomide.

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.

In implementations described herein, sequence representations are identified having at least at threshold likelihood of corresponding to a nucleic acid molecule produced by a subject in which a tumor-related biological condition is present. The identified sequence representations can be provided to a machine learning classification model to determine an indication of the tumor-related biological condition being present in subjects.

The invention discloses a disease identification and prediction method based on social media knowledge graph embedding. The method is mainly used for identifying patients with specific diseases in a social media scene. In order to optimize a representation learning process of a social media user self-report knowledge graph triple, a knowledge embedding optimization algorithm based on a hyperbolic space is designed, a ComplEx model is embedded into the hyperbolic space, the representation capability of the model on complex data with a hierarchical structure is enhanced, a Riemannian gradient descent method is used for carrying out gradient updating on a Poincare sphere model, and a knowledge graph triple representation learning process is established. And the embedding point is always in an effective area. Based on the embedded optimization, on the basis of a BERT + LSTM model, a channel attention mechanism and a space attention mechanism are combined to carry out knowledge fusion on social media user self-report text information and knowledge graph information, and external information in a knowledge graph is utilized to enhance the spatial feature expression ability of the model. Therefore, the recognition precision of the specific disease patient user in the social media is improved.