398 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 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 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 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 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 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.

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 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 risk prediction method for patients with Parkinson's dysphagia, and relates to the technical field of medical health. The method comprises the steps that S1, a multi-modal data fusion framework is built, the multi-modal data fusion framework aims at effectively fusing biomarkers, dynamic swallowing kinematics characteristics and clinical data, the core of the multi-modal data fusion framework comprises characteristic alignment, inter-modal interaction modeling and fusion decision, and a first risk prediction probability is output; through a multi-modal data fusion algorithm, a dynamic swallowing function quantification technology and a lightweight edge deployment scheme, high-precision prediction, early warning and personalized intervention of the Parkinson's dysphagia risk are realized, the method has remarkable clinical value, technical barriers and market competitiveness, and the core demand of medical productization landing is met.

The present invention discloses a closed-loop neural regulation system based on the medication and movement state of Parkinson's patients, including: a parameter setting module for determining the low beta frequency band, the high beta frequency band, the upper threshold and the lower threshold; a signal acquisition module for collecting and preprocessing the local field potential signal of the STN of Parkinson's patients; a feature calculation module for calculating the low beta frequency band energy and the high beta frequency band energy through short-time Fourier transform, and calculating the average value of the ratio of the two; a judgment output module for comparing the average value with the upper threshold and the lower threshold; if it is greater than the upper threshold, it is judged that the patient is in the drug failure-movement state and outputs high-intensity stimulation; if it is greater than the lower threshold and less than the upper threshold, it is judged that the patient is in the drug failure-resting state and outputs medium-intensity stimulation; if it is less than the lower threshold, it is judged that the patient is in the drug effective state and outputs low-intensity stimulation. The present invention can achieve precise closed-loop deep brain stimulation based on medication and movement state.

The invention relates to the technical field of intelligent medical treatment and remote medical treatment, and discloses a Parkinson's disease patient home information management system based on an internet hospital. Comprising the following steps: (1) a multi-source illness state acquisition module for monitoring illness states in real time by combining wearable equipment and a mobile terminal through a diary recording template and scale self-evaluation based on an internet hospital; (2) a medication management module which comprises medication reminding, illness change and side effect recording; (3) a data analysis and early warning module which uses incremental learning and model fine tuning technologies, optimizes a prediction model, analyzes the correlation between medication parameters and illness state changes, and realizes early warning of illness state deterioration; and (4) a remote collaborative diagnosis and treatment module which constructs a visual data platform, establishes a two-way communication channel and realizes immediate intervention. The system is based on an internet hospital, and an intelligent solution is provided for long-range management of Parkinson's disease by innovatively constructing a'monitoring-analysis-early warning-intervention 'closed-loop management system.

The invention discloses a cognitive function intervention method and system based on multi-mode space-time dynamic decoding biological feedback, and belongs to the technical field of artificial intelligence algorithms and noninvasive nerve regulation. According to the method, brain nerve activity signals and autonomic nerve cooperation signals are captured in real time, the signals are subjected to standardization processing by adopting a multi-modal signal cascade preprocessing algorithm, and cognitive feature extraction of fusion signals and dynamic distribution of multi-modal signal feature weights are realized in combination with a circulating double-flow self-encoding model; and thus, the individual cognitive state is accurately evaluated. And based on the evaluated cognitive state, dynamically identifying and quantifying into self-perceived visual and auditory stimuli, and constructing an iterative closed-loop feedback cognitive nerve plasticity regulation loop to realize individualized targeted regulation of the cognitive function. The method is suitable for non-invasive individualized intervention of the cognitive function of the Parkinson's disease patient, and has remarkable clinical application potential and social value.

The invention belongs to the technical field of biology, and particularly relates to a Parkinson's disease diagnosis kit based on a peripheral red blood cell alpha-synuclein RT-QuIC technology and application of the Parkinson's disease diagnosis kit. The Parkinson's disease diagnostic kit based on the peripheral red blood cell alpha-synuclein RT-QuIC technology comprises a substrate protein, a reaction reagent, a negative reference substance and a positive reference substance, and a sample is selected from red blood cells in blood. The RT-QuIC technology is applied to diagnosis of neurodegenerative diseases, particularly, the sowing activity of pathological alpha-Syn protein is detected in blood red blood cell samples of PD patients of Chinese population for the first time, and the kit has extremely high sensitivity and specificity and provides important reference value for diagnosis and screening of PD. The invention provides a rapid, efficient and accurate diagnostic kit for diagnosis of Parkinson's disease, the kit can be used for accurately detecting Parkinson's disease patients and healthy control, and powerful technical support is provided for clinical diagnosis of the Parkinson's disease patients.

The invention relates to the technical field of retina analysis, and discloses a fundus optical coherence tomography image feature point automatic positioning method, which comprises the following steps: acquiring optical coherence tomography images of a fundus disease patient at different disease course stages, constructing an image database containing a training set and a verification set, and determining the feature points of the fundus optical coherence tomography image on the basis of the image database. And carrying out feature layer segmentation on the image by adopting a U-Net convolutional neural network. And determining a blood vessel shadow position in the fundus optical coherence tomography image and acquiring a boundary of the blood vessel shadow position based on the pigment epithelium gray scale curve. And determining the position of a seed point and the size of a subarea by combining an important layer boundary and a vessel shadow boundary, and assisting digital image correlation to realize retina full-field deformation measurement. According to the method, through deep fusion of deep learning and an image processing technology, the problem of automatic selection of the seed points in the fundus OCT image is solved, the efficiency, precision and reliability of retinal deformation measurement are remarkably improved, and a powerful technical tool is provided for mechanism research and clinical diagnosis and treatment of fundus diseases.

The present invention discloses a voice analysis method for key feature parameters of freezing gait symptoms in Parkinson's disease based on the AdaBoost algorithm. Step 1: Collect continuous and stable vowels of Parkinson's disease patients and record whether the Parkinson's disease patients have freezing gait symptoms; Step 2: Perform denoising preprocessing on the voice signals and remove the silent segments; Step 3: Extract various voice features; Step 4: Use the CART algorithm to perform feature selection on the original features and screen out the key features that can effectively represent the information of freezing gait symptoms; Step 5: Train the AdaBoost model; Step 6: Input the feature vector of the voice to be measured into the model to obtain the key feature parameters of the freezing gait symptoms in Parkinson's disease. The present invention uses the AdaBoost algorithm to analyze the freezing gait symptoms in Parkinson's disease, improves the model accuracy by using ensemble learning, and reduces the cost of early analysis of the freezing gait symptoms in Parkinson's disease.

The invention discloses a Parkinson's disease patient video analysis and evaluation system based on artificial intelligence. The method comprises the following steps: collecting a multi-modal data set; extracting face key point data in the multi-modal data set by using a Mediappe face detection algorithm, and analyzing blink frequency, eyelid movement symmetry and face muscle tension change; extracting a hand joint motion track through an OpenPose model, and calculating tremor frequency, motion completion time and motion smoothness; detecting step length, step width, step speed and coordination of left and right limbs in the gait of the human body based on a YOLOv8 target detection algorithm; dynamically weighting the facial tremor features, the limb movement features and the gait dynamics features through a multi-head attention mechanism; and outputting the symptom evaluation index of the Parkinson's disease patient through the LSTM network. And the efficiency and accuracy of illness state assessment are improved.

The present disclosure provides a novel type of drug for treating a subject suffering from an inflammatory disease. Specifically, the disclosure provides polypeptides comprising at least three immunoglobulin single variable domains (ISVDs), characterized in that at least one ISV binds to OX40L and at least two ISVDs bind to IL-13. The present disclosure also provides nucleic acids, vectors and compositions.

The present invention relates to a method for determining the disc incision depth of posterior lumbar interbody fusion, comprising the following steps: (1) Selecting patients with lumbar degenerative diseases who have undergone X-ray examination and plain CT scan of lumbar intervertebral discs as samples; (2) Removing unqualified samples; (3) Taking anteroposterior and lateral X-rays of the lumbar spine with the patient standing, and performing tomographic scanning with the patient lying flat; (4) Measuring the lengths of the lower edges of the L3 (the third lumbar vertebra), L4 (the fourth lumbar vertebra), and L5 (the fifth lumbar vertebra) vertebral bodies in the X-ray films as the X-ray depths of the L3 / 4, L4 / 5, and L5 / S1 intervertebral spaces; (5) Performing statistical analysis using SPSS 18.0 software and making a table of the analysis results; (6) Obtaining the warning value of the disc incision depth based on the data results. This method provides the warning value of the insertion depth of the nucleus pulposus forceps for medical staff performing posterior lumbar interbody fusion, improving the safety and success rate of interbody fusion.

The utility model discloses a handheld stabilizing device for a Parkinson's disease patient. The handheld stabilizing device comprises a handle, a shell, a buffering piece, an adjusting mechanism, buffering rubber, a clamping mechanism and accessories. The front end of the handle is in threaded connection with the hollow shell, the buffering piece is movably connected in the shell, the adjusting mechanism is arranged at the front end of the buffering piece, the clamping mechanism is arranged at the tail end of the buffering piece, and the buffering rubber is arranged in the center of the buffering piece to connect the clamping mechanism and the adjusting mechanism. The adjusting mechanism comprises a knob, a sliding block, a square block, a cylinder and a spring; the sliding block is a triangular block, a square block is arranged at the bottom of the sliding block, a cylinder is arranged on the other side of the sliding block, and a hexagonal groove is formed in the front end face of the buffering piece and movably connected with the sliding block. Six sliding grooves are formed in the knob and movably connected with a sliding block through a cylinder, and a groove is formed in the cylinder at the rear end of the sliding block and movably connected with a spring.

The present application relates to the technical field of postoperative rehabilitation, in particular to a postoperative rehabilitation intervention system for patients with neurological diseases based on evidence-based medical evidence. The steps implemented by the system include: obtaining characteristic data of a patient with a neurological disease to be analyzed and reference patients, a rehabilitation scheme of the reference patients and a postoperative Barthel index, calculating a reference factor of the reference patients corresponding to each rehabilitation scheme under each preoperative state; obtaining a comprehensive Barthel index of each rehabilitation scheme under each preoperative state by using the reference factor and the postoperative Barthel index, determining a correction factor under each preoperative state according to the comprehensive Barthel index, and correcting the comprehensive Barthel index to obtain a target Barthel index; determining a reference rehabilitation scheme by combining the similarity of the characteristic data of the patient with a neurological disease to be analyzed and different reference patients and the target Barthel index. The present application can improve the postoperative rehabilitation intervention effect for patients with neurological diseases.

The invention discloses a mental disease assessment method based on visual evoked potential abnormity, which comprises the following steps of: taking chessboard overturning as a stimulation graph, acquiring graphic visual evoked potential by utilizing electrophysiology visual acquisition equipment, and analyzing visual evoked potential waveform by utilizing an assessment module; and finally comparing whether the difference between the latency parameter and the amplitude parameter in the graphic visual evoked potential has significance or not, and providing a statistical index for a doctor and the patient to indicate the illness degree of the patient or whether the illness state is controlled or not. The method changes the traditional influence of clinical experience of doctors, subjective factors of patients and the like on objectivity of diagnosis results, is repeatable and low in cost, is a non-invasive and rapid evaluation method, and is expected to become a biomarker for mental disease patients and a potential tool for psychiatric diagnosis in the future.