14 results about "Treatment intervention" patented technology

The invention discloses a distributed processing method for funnel chest treatment influence data in combination with an edge network, and relates to the technical field of medical information technology and edge computing intelligent processing, and the method comprises the steps: S1, collecting the dynamic image and sign data of a patient through an edge node, carrying out the preliminary classification of the collected real-time data through a distributed processing frame, and carrying out the calculation of the collected real-time data; determining a data priority sequence according to the mode applicable symptom and the historical curative effect data, and obtaining a real-time data group with the priority higher than a preset threshold value; s2, according to the real-time data groups of which the priorities are higher than a preset threshold value, executing on-site conversion operation at edge nodes, grouping converted data features by adopting a k-means clustering algorithm, and determining feature groups meeting conditions; according to the distributed processing method for the funnel chest treatment influence data in combination with the edge network, the data processing real-time performance, the treatment scheme individuation and the cross-node consistency are remarkably improved, and an efficient and accurate treatment intervention path is provided for a patient.

The invention provides a psychological health analysis and intervention method based on multi-Agent collaborative assessment, which comprises the following steps of: shunting information and dialogue contents input by a user through an initial shunting Agent module, and then performing parallel and independent multi-dimensional psychological assessment by using a plurality of different assessment Agent modules. Meanwhile, each assessment Agent module corresponds to a psychological dimension and is provided with a special vertical domain knowledge base, then, structured output results of the assessment Agent modules are received and integrated through the central coordination and treatment agent, a collaborative and unified psychological health assessment report is generated, and finally, based on the assessment report, a psychological health assessment report is established. The treatment intervention Agent module is used for generating a personalized mental health intervention scheme matched with the user, and a precise intervention scheme is provided for the user.

The application relates to the technical field of medical devices and particularly discloses a pain dynamic evaluation and treatment linkage system based on three-dimensional somatosensory interaction, which comprises the following modules: a human body modeling module for constructing a three-dimensional human body dissection model; a somatosensory interaction module for receiving pain area marking instructions and pain intensity scores; an infrared sensing module for monitoring human body infrared thermal imaging data of a patient in real time; a physiological monitoring module for monitoring heart rate variability indexes and skin electric reaction indexes of the patient based on wearable devices; a region marking module for obtaining a calibrated pain area of the patient based on the pain area marking instructions and abnormal temperature zones in the human body infrared thermal imaging data; a pain prediction module for predicting a pain intensity prediction curve in a future period based on the pain intensity scores, the heart rate variability indexes, the skin electric reaction indexes and the calibrated pain area of the patient; and a treatment linkage module for generating treatment parameter control instructions based on the pain trend prediction curve and a multi-dimensional pain characteristic vector, so that precise and dynamic treatment intervention is realized.

This invention discloses a bed-leaving detection alarm system, including a distance detection component (2) for detecting the distance from the bed, a wearable physiological indicator detection component (3) for monitoring physiological status, and a processor (4). The processor (4) receives distance and physiological indicator signals and determines whether the physiological indicators are within a preset parameter range. When not within the preset range, the analysis unit (45) determines whether the patient can return to the bed on their own: if not, an alarm command is generated to prompt medical staff to reach the patient's location and take treatment measures; if so, an alarm is generated to prompt the patient to return to the bed immediately, and the system monitors whether the return process is smooth. This system can also automatically generate monitoring directions based on the patient's medical records and department information to achieve accurate and personalized bed-leaving early warning.

This invention relates to the field of rat coronary heart disease complicated with depression treatment and intervention technology, and discloses a rat coronary heart disease complicated with depression treatment and intervention system, including a multidimensional acquisition module and an intelligent intervention module. The system acquires experimental data, metabolomics data, and efficacy data of intervention programs from all rats through the multidimensional acquisition module, and classifies them into datasets. The intelligent intervention module calculates a disease index based on the multidimensional data, accurately quantifies the severity of symptoms, and achieves dual correlation verification between metabolic mechanisms and pathological phenotypes. The multidimensional correlation has high accuracy. The intelligent intervention module evaluates the treatment effect of each rat after receiving different intervention programs, generates a treatment index, and assesses the population coverage of each intervention program's effect, generating an effectiveness index. It determines the symptom level and efficacy level of each rat, outputs corresponding evaluation results and intervention suggestions, and optimizes the intervention program through population data feedback. The intelligent intervention treatment effect is excellent.

The present application relates to the technical field of pregnancy rate prediction, and particularly relates to a model establishment method and system for predicting pregnancy rate of early-onset ovarian dysfunction, which obtains clinical data of a patient, identifies four types of prediction key points, including basic physiological characteristics, ovarian reserve function, treatment intervention and pregnancy outcome correlation. When the total amount of key points exceeds a first threshold value, a dimensionality reduction improved feedforward neural network is constructed, and basic physiological characteristics and treatment intervention data are used as inputs to predict ovarian function and pregnancy outcome. When the number of key points is small, a graph neural network is used, a directed graph structure is constructed based on clustering and hierarchical relationship of each key point, and node state is dynamically updated based on basic physiological characteristics, so as to realize classification prediction of ovarian function and pregnancy risk, improve prediction accuracy and model interpretability, and provide personalized and intelligent decision support for fertility evaluation of POI patients.

A method may include receiving objective information associated with a user that uses the at least one medical device during execution of a treatment plan, and based on the objective information, generating, using one or more terms defined in at least one medical terminology database, at least one input prompt. The method may also include providing, to an at least artificial intelligence engine configured to provide one or more predictions indicating one or more treatment interventions, the at least one input prompt. The method may also include receiving at least one prediction indicating one or more treatment interventions, and controlling, while the user uses the at least one medical device, and based on at least one aspect of the one or more treatment interventions indicated by the at least one prediction, the at least one medical device.

The invention relates to the technical field of medical data processing, in particular to an acute shock early recognition and typing system based on multi-parameter fusion. Firstly, various related physical sign parameter time sequence data are acquired from the data acquisition module; the anomaly analysis module analyzes the deviation between the data value at each moment and a preset range to obtain an anomaly expression coefficient, reflect the degree of deviation from a normal range, compare the difference of the anomaly expression coefficients between the moments, determine the treatment influence expression degree and evaluate the treatment measure effect; the multi-feature fusion module analyzes the time sequence and similar features of the treatment influence expression degree, fuses the time sequence and similar features with abnormal expression coefficient difference features, calculates significance indexes, and comprehensively evaluates the importance of physical sign parameters; and finally, in a weight adjustment and neural network analysis module, adjusting a preset importance weight based on the significance index, so that the system adjusts different sign parameter weights in real time, automatically learns the feature importance degree, and improves the neural network identification and accurate typing capabilities.

The application discloses a liver care system and a whole-process management method for chronic disease prevention and control. The system comprises a perception layer, a data layer, an application layer and a display layer connected in sequence. The perception layer collects multi-dimensional health data. The data layer constructs a liver disease specialist data resource pool. The application layer realizes four core functions. The display layer supports multi-terminal visual access. The management method comprises four closed-loop stages of precise screening, diagnosis and treatment intervention, out-of-hospital management and dynamic optimization. Through hierarchical detection and artificial intelligence assisted decision-making, an individualized scheme is generated. Combined with the doctor-patient collaborative interaction mechanism, whole-process monitoring and dynamic adjustment of the scheme are realized. A simple formula is introduced to quantify the risk and intervention effect. The scheme breaks the barriers of in-hospital and out-of-hospital management, improves the precision of chronic liver disease prevention and control and the patient compliance, guarantees data security, provides support for clinical research and policy making, and is suitable for multi-scene popularization.

The invention relates to a periodic Cushing syndrome screening method. A marker of the method is annexin A1. Compared with an existing periodic Cushing syndrome identification method, the method has the advantages that only further examination and treatment measures need to be taken when cortisol exceeds normal again, so that the treatment time of a patient is remarkably shortened, the life quality of the patient is remarkably improved, medical resources are remarkably saved, missed diagnosis and misdiagnosis of the periodic Cushing syndrome are reduced, and the method is suitable for popularization and application. And the method has remarkable economic and social benefits.

The invention discloses a craniofacial skeleton digital twin growth prediction system and method, and relates to the technical field of digital medical treatment and orthodontic intelligent auxiliary decision making. The system comprises a data acquisition module for acquiring multi-modal craniomaxillofacial data and dynamic biomechanical time sequence data; the data processing module is used for preprocessing and registering the acquired data; the digital twinborn body construction module constructs an individualized three-dimensional digital twinborn body model; the mixed growth prediction module adopts a mode of coupling data driving and mechanism simulation to simulate an accumulative growth effect under the action of dynamic load circulation and generate a growth deformation prediction result; and the visualization module visually displays the deformation process of the individualized three-dimensional digital twin model on a preset time sequence based on the growth deformation prediction result. According to the method, precise individualized dynamic prediction of the craniomaxillofacial growth trajectory and quantitative prediction of the treatment intervention effect are achieved, and the scientificity of clinical diagnosis and treatment scheme design, the reasonability of treatment opportunity selection and the reliability of prognosis evaluation are improved.

Systems and methods are disclosed for providing ongoing monitoring and updating of blood volume status, where the system or method can include guidance in the form of recommendations for treatment actions or alerts about altered patient status.

This invention relates to a patient scheduling system for a hospital emergency room. An initial assessment scheduling module classifies admitted patients by urgency level. When medical resources are strained, it compares historical emergency records and simulates pre-treatment interventions based on similar cases. Simultaneously, it constructs a waiting queue containing patients at different treatment stages and predicts the impact of adding a patient to the waiting queue on the patient themselves and other patients in the queue. A secondary assessment scheduling module, after the patient receives formal intervention, schedules nurses to perform corresponding examinations and constructs a secondary examination result vector. A comprehensive assessment module combines the urgency level and the secondary examination result vector, using a pre-trained model to obtain the final patient classification, providing a basis for subsequent treatment scheduling. This invention, through pre-intervention and predictive dynamic ranking, effectively utilizes fragmented doctor time, optimizes emergency room resource scheduling, significantly shortens patient waiting time, and improves emergency efficiency and treatment success rate.