5723 results about "Internal medicine" patented technology

A noninvasive blood pressure monitor. The noninvasive blood pressure monitor may include an inflatable cuff, a pressure transducer, one or more air pumps, and a processor. The processor may control the air pump(s) so as to initiate inflation of the cuff. The processor may also identify an oscillometric signal in an output of the pressure transducer, and may determine an envelope of the oscillometric signal. The processor may also determine one or more characteristics of the envelope of the oscillometric signal, and may control the air pump(s) so as to stop inflation of the cuff based on the one or more characteristics of the envelope of the oscillometric signal.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

Systems and techniques to protect against abuses in the transfer and maintenance of tokens within NFT platforms are illustrated. One embodiment includes a method to facilitate filtration in blockchains. The method recovers, from a list of prospective entries to be appended to a primary blockchain, a reference to a blockchain entry. The method obtains a certainty level associated with the blockchain entry. The method assesses a classification of the blockchain entry based on the associated certainty level. The method determines whether to record the blockchain entry on the primary blockchain based on the classification.

The invention discloses a breast cancer prognosis real-time evaluation system and method fusing a multi-modal image and a co-disease network, and relates to the technical field of breast cancer prognosis evaluation. According to the system, a comprehensive feature matrix fusing images, genes and clinical features is constructed by acquiring a mammary gland medical image, extracting focus features and combining gene expression information of a focus area and co-disease data of a patient, feature weighting is carried out based on an attention mechanism, and finally a prognosis risk score is output by utilizing a multi-layer perceptron model. And risk grading and intervention suggestion generation are realized. According to the system, a co-disease network and a gene interaction network are introduced for modeling, relevance expression among multi-source information is enhanced, and pathological features and systematic health status of patients can be reflected comprehensively. The intelligent level of prognosis evaluation can be improved, and good clinical popularization value is achieved.

Provided is a method for treating and / or preventing of cancer using Compound A or a pharmaceutically acceptable salt thereof.

A medical apparatus for a patient comprises an external system and an implantable system. The external system is configured to transmit one or more transmission signals, each transmission signal comprising at least power or data. The implantable system is configured to receive the one or more transmission signals from the external system, and to deliver stimulation energy to the patient. Methods of delivering stimulation energy are also provided.

The invention discloses a method and system for evaluating infusion operation of intern medical staff, and belongs to the field of infusion, and the method comprises the steps that the operation of the intern medical staff is collected in multiple modes, and the multiple modes comprise a visual mode, a touch mode and a physiological signal mode; inputting the collected operation of the intern medical staff into the multi-modal infusion evaluation model, performing feature extraction by the multi-modal infusion evaluation model in a modal manner, and performing cross-modal attention fusion on feature vectors of different modals; and finally, comparing the calculated total feature tensor Hfusion with a standard total feature tensor Hfusion ', and outputting a final score of the practice medical personnel according to a preset scoring standard. According to the scheme, multi-dimensional data such as visual, tactile and physiological signals are synchronously collected through a multi-modal fusion method, operation details are comprehensively covered, comprehensive evaluation is carried out, and evaluation objectivity is improved.

Aspects of the disclosure provides composition and methods for treating a subject having a blood loss condition, the method comprising administering to the subject a hemojuvelin (HIV) antagonist (e.g., anti-HJV antibody).

The invention discloses a lung squamous cell carcinoma survival prediction method and system based on SHAP interpretability, and relates to the technical field of medical data analysis. Comprising the following steps: constructing a dynamically updated physiological feature information table based on a lung squamous cell carcinoma clinical data set of a target under multiple time nodes; and associating different survival analysis models with the physiological feature information table to predict the survival probability of the lung squamous cell carcinoma in the target body, the marginal contribution of each input feature to the output of each model is obtained by using the SHAP interpretation algorithm, and the feature uniqueness score is transparently displayed in the fusion process, so that the survival probability of the lung squamous cell carcinoma in the target body can be predicted in the actual use process. The method can visually understand how each feature affects the final prediction, and achieves automatic arbitration or manual recheck through providing a model to explain conflict measurement, invalid variable elimination and pseudo high risk verification and weighted scoring, thereby guaranteeing the reasonability and safety of prediction and intervention suggestions, reducing the decision risk, and enhancing the clinical trust.

The present disclosure provides methods of treating a neurodevelopmental disorder (NDD) in a subject by administering a therapeutically effective amount of N-acetyl leucine.

The invention discloses a critical patient acute lung injury assessment system and method based on machine learning, and relates to the field of acute lung injury assessment, and the method comprises the steps: obtaining chest multi-modal image data of a critical patient, and carrying out the standardization processing to generate a standard data set; dividing the lung area into structural blocks and extracting structural indexes, and selecting compensation missing data through cross-modal space mapping or candidate blocks; identifying a modal conflict area and a structure mutation area to construct a scoring key area; after disturbance screening effective areas are applied, a mean value or weighted fusion strategy is selected according to a consistency index; calculating a lung injury score in combination with the dynamic weight and performing iterative optimization; according to the method, the recognition accuracy of the acute lung injury key area can be improved, and the accurate quantitative evaluation of the lung injury degree is realized.

The invention relates to the field of heart rate monitoring, in particular to an exercise heart rate monitoring method and device based on a smart watch. The method comprises the following steps: acquiring geographical location information of a user based on a smart watch, and performing user motion scene recognition so as to generate a motion scene type; original heart rate detection parameters are collected, time sequence heart rate feature analysis is carried out, and a heart rate feature set is constructed; performing motion mode depth identification on the motion scene type and the heart rate feature set to obtain a user motion mode; global heart rate situation perception is carried out on the heart rate feature set, and a global heart rate situation map is constructed; and performing comprehensive risk assessment and self-adaptive early warning decision on the global heart rate situation map according to the motion mode of the user. The heart rate state of the user is adaptively analyzed, and the exercise safety assessment efficiency and accuracy of the user are improved.

Disclosed herein are methods and compositions for preventing or reducing adverse events associated with administration of a RAS(ON) inhibitors. Thus, disclosed are compositions and methods for treating or preventing RAS(ON) inhibitor therapy-associated rash or mucositis. The methods involve administering a compound that binds to cyclophilin A (CypA) to compete with a RAS(ON) inhibitor for CypA binding, thereby reducing tri-complex formation in non-tumor tissues. These approaches may improve the tolerability of RAS(ON) inhibitor therapies without compromising efficacy.

The invention provides an adapter and a tumor electric field treatment system, an electrode plate comprises a plurality of electrode units and a plurality of temperature detection units, the plurality of temperature detection units are divided into a plurality of row groups and a plurality of column groups, and the grounding ends of the temperature detection units in each row group are connected to the same grounding wire. The signal end of each temperature detection unit in each column group is in short circuit with the corresponding electrode unit and then is connected to the same dual-purpose signal line; a bidirectional change-over switch of the adapter is configured to switch a dual-purpose signal line to be connected to a temperature sampling point or an alternating power line, and when the dual-purpose signal line is connected to the temperature sampling point, each grounding line is conducted in sequence so that a temperature detection signal detected by each temperature detection unit is sampled based on the temperature sampling point; and when the dual-purpose signal line is communicated with the alternating power line, an alternating current signal is transmitted to each electrode unit through the dual-purpose signal line, so that partition control and comprehensive temperature detection of the electrode plate are realized.

The invention relates to an intelligent oral disease risk prediction and analysis system, which comprises an oral health data acquisition module, a data preprocessing and standardization module, a multi-modal data fusion module, a risk prediction and suggestion module and a medical interaction interface module, the data preprocessing and standardization module is used for preprocessing original data and performing standardization operation, the multi-modal data fusion module is used for multi-modal data fusion, and the risk prediction and suggestion module is used for oral disease risk prediction and generation of personalized medical intervention suggestions. And the medical interaction interface module is used for checking a prediction report by a user, receiving medical suggestions and providing feedback information. According to the intelligent oral disease risk prediction analysis system, a multi-modal oral data feature extraction algorithm based on a convolutional neural network is proposed to perform feature extraction on data; and an oral disease risk prediction algorithm based on machine learning is provided to predict the oral disease risk.

The invention relates to a multi-frequency bioelectrical impedance-based body fat accurate measurement method and system. The method comprises the following steps: receiving a mode selection instruction sent by a user to determine a current working mode; if the mode is a rapid measurement mode, applying single reference frequency and reference current intensity excitation, obtaining a single group of bioelectrical impedance data, and calculating a rapid body fat rate result; if the measurement mode is an accurate measurement mode, acquiring bioelectrical impedance data under a plurality of preset frequencies and a plurality of preset current intensities through a complete electrode system, performing signal processing on the data, extracting impedance amplitude and phase information, constructing an impedance characteristic curve, extracting characteristic parameters related to the body fat rate from the impedance characteristic curve, and calculating the body fat rate based on the characteristic parameters. And obtaining a final body fat rate result of the accurate measurement mode. According to the method, the body fat rate can be accurately measured, and the accuracy and reliability of a measurement result are improved.

The invention provides an EGFR wild-type lung adenocarcinoma prognosis risk assessment method based on multi-omics and machine learning, and the method comprises the steps: obtaining multi-omics and clinical data of lung adenocarcinoma, obtaining a data set, and carrying out the multi-omics consensus clustering, and obtaining a molecular typing result; high-risk subtype specific candidate genes are identified, a candidate prognosis gene set is obtained, multi-algorithm machine learning comparison optimization is carried out, and a modeling strategy is obtained; performing feature screening and model training to obtain a multi-omics feature model so as to calculate an individual risk score of the to-be-tested sample; the individual risk score and the clinical staging information are utilized to obtain a clinical column diagram and a survival prediction result, then the flow of the multi-omics feature model, the individual risk score and the survival result is Web to obtain a clinical system, and a lung adenocarcinoma prognosis risk assessment result is output. The invention can realize an objective, accurate, generalizable and multifunctional prognosis evaluation and treatment guidance tool, and has important clinical application value and wide industrialization prospect.

The invention relates to the technical field of blood glucose prediction, in particular to a blood glucose fluctuation prediction method based on historical data. The method comprises the following steps: acquiring corresponding historical blood glucose monitoring data, daily behavior data, physiological status data and medical intervention data of a user, performing time axis alignment and abnormal value cleaning treatment, and extracting basic features and associated influence features corresponding to blood glucose fluctuation; performing space-time dimension fusion on the basic features and the associated influence features, and generating a blood glucose fluctuation prediction curve of the user in a future preset time period; performing comparative analysis on the blood glucose fluctuation prediction curve and actual blood glucose monitoring data of the user, calculating a prediction deviation degree and a trend goodness of fit, and generating a corresponding deviation analysis result; feature weight distribution of the dynamic prediction model is optimized based on the deviation analysis result, and a personalized blood glucose fluctuation prediction report is generated and comprises a risk early warning threshold value and an intervention suggestion triggering condition. According to the method, prediction efficiency and personalized management of blood glucose fluctuation can be realized.

The invention discloses a system and a method for intelligently monitoring blood sugar of a patient before and after diet based on food components, and belongs to the technical field of blood sugar monitoring, and the system comprises a food component quantification module which is used for converting a food image or description into accurate macro nutrient and micronutrient data; the multi-modal data fusion module is used for preprocessing and fusing the multi-modal data of the patient; the patient blood sugar prediction module is used for constructing a patient blood sugar prediction model to analyze the multi-modal fusion data of the patient and predicting the blood sugar of the patient; and the difference analysis optimization module is used for comparing actual blood glucose with predicted blood glucose after meal and analyzing difference reasons. The problems that blood glucose of a patient before and after eating cannot be intelligently monitored, and accurate quantification and prediction of the relation between diet and blood glucose cannot be achieved are solved. The system can intelligently monitor the blood glucose of the patient before and after eating, can help the diabetic to realize accurate quantification and prediction of the relationship between diet and blood glucose, and can effectively control the blood glucose and reduce the risk of complications.

The invention relates to the technical field of intelligent medical instruments, and discloses a closed-loop electro-acupuncture therapeutic apparatus based on heart and brain coupling information feedback. The device comprises a forehead electroencephalogram signal acquisition module, a single-lead electrocardio acquisition module, a Bluetooth transmission module, a signal preprocessing module, an ECG and EEG feature extraction module, a dynamic coupling analysis module, an embedded XGBoost classifier and an electroacupuncture control module. According to the system, collected EEG and ECG signals are wirelessly transmitted through Bluetooth, HRV indexes and EEG frequency band power spectral density are extracted after preprocessing, and frequency domain coherence analysis is carried out to obtain heart and brain bidirectional coupling characteristics. The embedded XGBoost classifier outputs optimal electroacupuncture stimulation parameters based on the characteristics, and the electroacupuncture control module generates corresponding bidirectional pulse waves for stimulation. According to the therapeutic apparatus, closed-loop feedback control is achieved, therapeutic parameters can be dynamically optimized, the individuation and precision level is improved, and meanwhile safety is ensured through impedance monitoring and electrical isolation.

The invention discloses a lung squamous cell carcinoma survival probability prediction system fusing multiple models, and relates to the technical field of medical data analysis. Comprising a data acquisition module used for acquiring a lung squamous carcinoma clinical data set and an inspection index of a patient; the data preprocessing and dynamic feature table construction module is used for preprocessing the lung squamous cell carcinoma clinical data set in existing data processing and dynamically updating a physiological feature information table; the model training and predicting module is used for carrying out training and online prediction on various survival analysis models; and the SHAP interpretation and weight calculation module is used for calling a corresponding SHAP algorithm to obtain an average absolute SHAP value of each input feature. According to the method, model interpretation conflict measurement, invalid variable elimination and pseudo high risk verification are provided, and automatic arbitration or artificial recheck is realized through weighted scoring, so that the reasonability and safety of prediction and intervention suggestions are ensured, the decision risk is reduced, and the clinical trust is enhanced.

The invention discloses a breast cancer lymph node metastasis prediction system based on a gene spectrum, and relates to the technical field of breast cancer prediction systems. Comprising a data acquisition module which collects gene spectrum data of a breast cancer patient and collects detailed clinical information of the patient; the preprocessing module is used for carrying out data cleaning and data normalization processing on the collected data; and the feature extraction module is used for extracting principal component features by applying principal component analysis on the basis of the gene expression data. According to the method, gene expression data are considered, various gene spectrum data such as gene mutation and copy number variation and detailed clinical information are integrated, the biological characteristics of the breast cancer can be reflected more comprehensively, and the prediction accuracy is improved.

The invention discloses a leadless pacemaker for pace-making in a left bundle branch area and a delivery sheathing canal thereof.The leadless pacemaker comprises a pacemaker body, the center of the pacemaker body is partially provided with a first internal thread section, and a pacemaker electrode is partially provided with a first external thread section; the first internal thread section and the first external thread section are matched and screwed, so that the pacemaker electrode can be controlled to extend or retract by rotating the pacemaker electrode; when the pacemaker is used, the front end of the pacemaker body is flush with the front end of the shell, and the front end of the pacemaker electrode and the electrode spiral structure of the pacemaker electrode extend out of the pacemaker body by the maximum distance of 10 mm. The device can be safely and effectively implanted into the left bundle branch area, is stable for a long time and cannot be fractured or dislocated, and pace-making of the left bundle branch area is achieved.

The embodiment of the invention provides a real-time pulmonary ventilation impedance monitoring method and system based on multi-modal data fusion, and the method comprises the steps: obtaining real-time multi-modal data from a plurality of sensors, the multi-modal data comprising a sound signal, an airflow signal and a pressure signal; performing fusion processing on the multi-modal data to generate comprehensive pulmonary ventilation impedance data; based on the comprehensive pulmonary ventilation impedance data, analyzing data distribution characteristics to detect anomalies; and when abnormal data distribution characteristics are detected, a fault early warning system is triggered to solve burst signal distortion. Through the scheme of the embodiment of the invention, the problem of how to carry out fault early warning on the monitoring system according to the abnormal data distribution characteristics so as to solve the problem of burst signal distortion can be solved.

A container for organized storage and selective accessibility of items, such as medications. The container includes a base with a base plate and a central base hub, supporting the plurality of pill cases, each with a truncated sector shape and partitioned into two compartments accessible from opposing ends. The container incorporates a magnetic coupling system, comprising a plurality of primary magnetic elements on the base hub and a plurality of secondary magnetic elements on the pill cases, thereby enabling each pill case to attach to the base in one of two orientations, providing access to either compartment.

The invention relates to the technical field of blood glucose monitoring, in particular to an intelligent blood glucose monitoring and early warning method and system based on multi-source data fusion. Comprising the following steps: S1, acquiring blood glucose monitoring information of a monitored object, and determining first combined blood glucose monitoring information and second combined blood glucose monitoring information according to the blood glucose monitoring information; the first combined blood glucose monitoring information is an absolute difference between the blood glucose instantaneous information and the blood glucose continuous information; the second combined blood glucose monitoring information is a mean value of the blood glucose instantaneous information and the blood glucose continuous information; acquiring environment information of the monitored object, and determining a first environment variable quantity and a second environment variable quantity according to the environment information. According to the method, the dynamic deviation parameter and the early warning threshold value are fused, the measurement deviation and the physiological delay are quantified in combination with the extreme value features, double-source interference suppression and individualized early warning optimization of blood glucose monitoring are achieved in the plateau environment, and the risk identification accuracy and adaptability are improved.

The invention relates to the technical field of Tokamak plasma diagnosis and data fusion, in particular to a multi-modal fusion Tokamak high-frequency Thomson scattering reconstruction method. According to the technical scheme, the method comprises the following steps: acquiring multi-modal diagnosis data on a Tokamak device, wherein the multi-modal diagnosis data comprises high-frequency auxiliary diagnosis data and low-frequency Thomson scattering data; and performing feature engineering and physical enhancement processing on the high-frequency auxiliary diagnosis data. According to the method, through multi-modal diagnosis data fusion and interpretable KAN network modeling, the Thomson scattering time resolution is greatly improved on the premise that hardware is not transformed, and high-fidelity reconstruction of sub-millisecond transient processes such as ELM and magnetic island evolution is achieved; the method has physical interpretability, can reveal the implicit relationship between diagnostic quantities, enhances the robustness under complex experimental conditions through strategies such as modal loss, and provides key high-frequency diagnostic support for Tokamak high-performance operation and real-time control.

The present disclosure provides methods for the treatment of migraine by the administration of atogepant or a pharmaceutically acceptable salt thereof.

Described herein are methods of treating narcolepsy with cataplexy, comprising administering reboxetine to a human being in need thereof. Reboxetine may also be used in the manufacture of a medicament for the treatment of narcolepsy with cataplexy. Also disclosed herein are kits comprising a pharmaceutical composition comprising reboxetine and instructions to use the pharmaceutical composition to treat narcolepsy with cataplexy in a human being.

The invention provides a respiratory tract phlegm blockage monitoring method and device, and belongs to the technical field of respiratory tract phlegm blockage monitoring, and the monitoring method specifically comprises the following steps: obtaining respiratory tract multi-mode biophysical signals of a target domain patient and a plurality of source domain patients; the method comprises the following steps: acquiring a target domain signal through a micro sensor array, and constructing an optimal source domain set by using a signal processing algorithm formed by a coding module and a classification module; inputting a target domain signal and the optimal source domain set into a global adapter, extracting shallow layer features through a feature extraction module, and realizing conditional distribution alignment through a global adaptation unit; edge distribution alignment is guided through adversarial training containing a gradient inversion layer; and the discriminator and the global adapter parameters are fixed, and a classifier is trained to identify and evaluate the sputum blockage state. According to the method, the sputum blockage monitoring accuracy is improved through multi-mode signal processing and confrontation training.