4704results about "Therapies" patented technology

This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

An example operation may include one or more of storing a generative artificial intelligence (GenAI) model configured to create images, displaying a plurality of prompts on a user interface of a software application, receiving an identifier of a goal of the user and attributes of the goal via the plurality of prompts on the user interface, executing the GenAI model on the identifier of the goal of the user and the attributes of the goal to generate a custom image of the goal for the user, and displaying the custom image of the goal via the user interface of the software application.

A system for serving personalized advertisements based on biometric and identifying data captured by sensors. The system employs pre-processing, feature extraction, and recognition modules to identify individuals. A context-aware processing module adapts recognition strategies based on environmental data and analyzes personal information to confirm the identity of the targeted individual and send targeted advertisements. The system includes display and speaker modules for visual and audio advertisements, a predictive interaction modeling module for generating personalized advertisements, and a cross-platform behavior analysis module for analyzing user behavior across offline and online platforms.

An example assistant system can use a multimodal multitask medical machine-learned model to perform image processing to answer natural language queries. A device can process speech data or other natural language inputs to obtain a query. The query can be processed alongside image data that provides context for the query. The example system can receive a query associated with a particular task domain; generate, based on the query, a query input that comprises query instruction data from a first modality and query context data from a second modality; generate a combined input comprising the query input and an exemplar input, wherein the exemplar input comprises exemplar instruction data from the first modality and an exemplar context placeholder in lieu of exemplar context data from the second modality; process the combined input with a multimodal machine-learned model to generate output data; and output a query response based on the output data.

The invention provides a health management data mining method and system based on deep learning, and the method comprises the steps: firstly obtaining a target health data set, including physiological monitoring, behavior activity records and medical history texts, of a target user, and then carrying out the cross-modal noise filtering and feature alignment processing of the target health data set; the method comprises the following steps: generating a standardized health feature sequence containing a synchronous timestamp and the like, performing hierarchical feature abstraction by using parallel time sequence convolution branches and space attention branches through a cascaded spatial-temporal feature extraction network to obtain a multi-granularity health state characterization vector, inputting the multi-granularity health state characterization vector into a pre-training model, and training the multi-granularity health state characterization vector to obtain a multi-granularity health state characterization model. According to the method, a personalized health management scheme containing disease risk levels and the like is generated, finally, according to dynamic monitoring period parameters in the personalized health management scheme, the data acquisition frequency and the feature alignment strategy are adjusted in real time, a closed-loop health management data flow is formed, and precise and personalized health management is achieved.

An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

The invention relates to the technical field of data processing, and discloses an intelligent diagnosis and treatment management method and system for chronic respiratory diseases based on a large model. The method comprises the following steps: performing multi-dimensional data acquisition processing on a chronic respiratory disease patient to form a structured data set; constructing a large model and a knowledge graph by using a dual coding framework; performing risk hierarchical assessment based on the large model; generating a personalized diagnosis and treatment scheme according to an evaluation result; constructing a medical collaborative framework to execute curative effect monitoring; and customizing health education content and continuously optimizing the system. According to the application, accurate layering, personalized treatment scheme making, multi-stage medical collaborative management and dynamic curative effect monitoring of the chronic respiratory disease patients are realized, so that the defects of insufficient diagnosis and treatment accuracy, low resource allocation efficiency and discontinuous patient management in the prior art are overcome, and the overall efficiency of chronic respiratory disease diagnosis and treatment management is improved.

The invention discloses a neurology stroke patient informatization management system and method, and the method comprises the following steps: building a standardized multi-mode data set through collecting images, physiological signals, gene information, electronic medical records and other data; a causal reasoning method is adopted to identify pathological features and causal relationships thereof, a condition causal graph is generated, confounding factors are eliminated, and data analysis precision is optimized; based on a cross-modal representation learning method, extracting sharing features of the multi-modal data, and establishing a personalized health portrait; the influence of different intervention measures on disease development is evaluated by combining a time sequence causal reasoning method, and a rehabilitation path is dynamically adjusted by utilizing a reinforcement learning method; an optimal rehabilitation path is calculated through a causal dynamic programming method, feedback data are collected in real time in the rehabilitation process, and an intervention strategy is optimized. The intelligent level and the precision level of stroke patient management can be improved, personalized intervention and rehabilitation path optimization are achieved, and the rehabilitation effect of the patient is improved.

Embodiments described herein provide for a healthcare ecosystem of computing devices participating as nodes of a distributed ledger (e.g., blockchain). The distributed ledger includes NFTs representing corresponding patients, where the initial NET behaves logically as a token scaffold that represents a “digital twin” of the particular patient. The initial patient token represents personal profile data of patient personal data for the patient. The nodes may generate new data and / or new sub-tokens (or other blockchain entry type) representing new data, which the nodes associate with the token scaffold. The sub-tokens contain data for representing physical or digital specimens, health data, and data derived from the health data (e.g., cell lines, organoids, other model systems). As the nodes generate and associate new sub-tokens to the patient's token scaffold over time, the token scaffold graphs and facilitates integration of patient-specific data and evolves into a robust electronic health record (EHR) and digital twin.

The invention discloses a critical patient nursing intelligent monitoring system and method, and relates to the technical field of intelligent monitoring. Collecting physiological data and activity data of a patient in real time by using a multi-sensor system, and establishing an initial health file of the patient; according to the collected physiological data and activity data, daily activities and behavior modes of the patient are analyzed, and an individualized health baseline is established; monitoring physiological data and activity data of the patient in real time, identifying an abnormal trend, and generating an early warning signal; preferentially distributing nursing measures according to the state of the patient; the nursing measure effect is evaluated, and if the expected nursing effect is not achieved, the monitoring strategy is readjusted; according to the invention, the health state of the patient is comprehensively monitored and accurately analyzed in real time, so that abnormity can be found and intervened in time when the abnormity occurs, the patient can be nursed and treated in time, waste of nursing resources and nursing errors are reduced, and the treatment effect and the nursing quality of the critical patient are improved.

A computer-implemented system and method for predictive risk assessment and intervention includes a risk assessment unit that processes survey data through an integrated database architecture. The system employs machine learning algorithms to analyze relationships between community and personal risk factors, maintaining real-time correlation coefficients and reliability metrics in a geospatial database. Machine learning algorithms may process factor analysis results to generate risk prediction quotients and determine intervention thresholds. The system automatically recalibrates based on intervention outcomes, continuously updating statistical relationships while preserving geographic and demographic associations. The database architecture coordinates multiple specialized components, enabling real-time analysis of risk patterns and automated generation of evidence-based intervention recommendations. The machine learning implementation continuously improves prediction accuracy through automated learning, while maintaining statistical validity through reliability calculations and correlation analysis.

In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution. Combinations of one or more of uncertainty, use of synthetic training data, and therapy prediction may be provided.

The invention discloses an ear-nose-throat nursing scheme intelligent recommendation system based on cloud computing, and relates to the technical field of big data analysis. The intelligent recommendation platform is in communication connection with a data acquisition and preprocessing module, a knowledge graph construction module, a patient portrait construction module, a nursing scheme recommendation module, an evaluation and adjustment module and a user interaction and feedback module. According to the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing, deep analysis and mining are carried out on real-time physiological data, ear-nose-throat related symptoms, wound healing conditions and function recovery progress of patients by utilizing the powerful data processing capability of a cloud computing platform, individual differences and rehabilitation requirements of the patients are accurately recognized, and the intelligent recommendation system for the ear-nose-throat nursing scheme based on cloud computing is provided for the patients. And personalized nursing schemes are customized for the patients, so that the pertinence and effectiveness of nursing are improved, and the prolonged rehabilitation time and the discomfort of the patients caused by the improper schemes are greatly reduced.

The invention discloses a liver disease diagnosis and treatment strategy recommendation method based on a knowledge graph, a medium and equipment, and the method comprises the steps: collecting the basic information of a user, constructing a space-time correlated individual liver disease knowledge graph according to the basic information of the user, and enabling the knowledge graph to comprise a disease cause feature node, a pathology grading node, a complication early warning node and a treatment response node which are correlated with each other; inputting the individual hepatopathy knowledge graph into a hierarchical reinforcement learning model for joint reasoning, and outputting multiple groups of strategy options including an antiviral treatment scheme, a liver protection drug combination and a metabolic intervention measure; analyzing a topological propagation path of a complication early warning node through a complication prediction sub-model, and dynamically adjusting an initial candidate diagnosis and treatment strategy set; and updating the individual hepatopathy knowledge graph when obtaining the basic information of the new user. According to the method, multi-dimensional data fusion and dynamic strategy optimization of liver disease diagnosis and treatment are realized, and the accuracy and timeliness of a diagnosis and treatment scheme are remarkably improved.

Described are systems and methods for artificial intelligence (AI)-based clinical decision support. Systems can include a platform configured with a user input processing module, a context matching module, a retrieval-augmented generation (RAG) module, and an output generation module. Outputs of the platform can include a differential diagnosis, an assessment and treatment plan, or a clinical reference. The platform can further include an AI-copilot module and an AI-notebook module.

According to an embodiment, disclosed is a system comprising a processor wherein the processor is configured to receive an input data comprising an image of an ocular region of a user, clinical data of the user, and external factors; extract, using an image processing module comprising adaptive filtering techniques, ocular characteristics, combine, using a multimodal fusion module, the input data to determine a holistic health embedding; detect, based on a machine learning model and the holistic health embedding, a first output comprising likelihood of myopia, and severity of myopia; predict, based on the machine learning model and the holistic health embedding, a second output comprising an onset of myopia and a progression of myopia in the user; and wherein the machine learning model is a pre-trained model; and wherein the system is configured for myopia prognosis powered by multimodal data.

The invention discloses a doctor-patient negotiation method and device fusing LLM and a multi-agent architecture, equipment and a medium, and relates to the technical field of doctor-patient negotiation. The doctor-patient negotiation method comprises the following steps: acquiring doctor-patient background information, and constructing a doctor agent, a patient agent and a coordination agent based on a large language model. The doctor agent generates a preliminary treatment regimen. The patient agent generates feedback to the preliminary treatment regimen. The coordination agent parses the feedback, generating a structured acceptance score and a semantic vector. The dynamic belief is updated based on the semantic vector doctor agent. And performing fuzzy utility evaluation on the current treatment scheme, and judging whether strategy adjustment is triggered or not. And the coordination agent calculates the adjustment amount of each issue based on the acceptance difference value, the patient yielding willingness coefficient and the maximum yielding amplitude, and obtains a new quotation scheme. And triggering a belief calibration and strategy reconstruction process at a preset stage node of multiple rounds of negotiation to obtain a new strategy. And when the negotiation round reaches a preset maximum round number, outputting a final treatment scheme.

The invention relates to the technical field of medical aid decision making, in particular to a system and method for generating an individualized nursing scheme based on the comprehensive evaluation condition of the elderly. According to the method, various physiological time sequence data of the old people in a specific life scene are collected and the periodic fluctuation characteristics of the data are extracted, so that the subtle change trend of the daily rhythm of the old people can be accurately identified, the dynamic influence of the chronic disease progress on the physiological state is revealed by further comparing the rhythm deviation degree and continuity in different time periods, and the accuracy of the physiological state of the old people is improved. Then, in combination with the trend change rate and directionality thereof, an interaction mode between physiological changes and chronic disease indexes is described in continuous observation, so that the measurement of the coupling adaptation degree between individual physiological responses and chronic disease conditions is achieved, through normalized amplitude difference and matching interval screening, individual physical ability and chronic disease adaptation labels are accurately given, and the accuracy of the physical ability and chronic disease adaptation labels is improved. And further comprehensively measuring the adaptation priority of each intervention element in the individual in a multi-dimensional scoring mode, and constructing a nursing intervention combination oriented to a specific crowd state.

The invention discloses an effect evaluation method for a non-dialysis diabetic kidney disease, which comprises the following steps: monitoring the change trend of data by collecting renal function related data of a patient, and generating a basic data set; a dynamic nonlinear weighted regression algorithm is adopted to analyze the multi-dimensional renal function data, data weights are dynamically adjusted, and influence weights of different time periods are distributed according to the change mode of the illness state; the data influence is adjusted in real time through a time correlation mechanism, and a disease assessment curve is generated; according to the method, historical data and real-time data are combined, a patient condition trend line and a prediction result are generated, personalized treatment feedback is output, and intervention suggestions are provided for clinicians based on long-term prognosis prediction. The method is suitable for disease monitoring and treatment feedback of patients with non-dialysis diabetic kidney diseases.

The invention discloses a nursing system and a monitoring method thereof, and the method comprises the following steps: collecting the physiological data of a patient through an intelligent sensor, forming a dynamic health data flow, carrying out the analysis in combination with historical health data, extracting key health features based on a time sequence attention mechanism, and carrying out the monitoring of the key health features. A machine learning model is adopted to predict the health state of a patient, a health mode between the patient and a similar patient is calculated to generate a personalized nursing scheme, and combined optimization is performed on health state prediction and a nursing recommendation scheme in combination with a time sequence collaborative multi-task learning framework, so that dynamic adjustment of the nursing scheme is realized; the system also includes an adaptive care intervention module that optimizes a care intervention strategy according to the latest health data of the patient and continuously iteratively optimizes the care scheme based on the care execution feedback. According to the invention, a complete nursing closed loop from data acquisition, analysis, prediction and recommendation to optimization and intervention is constructed, and accurate monitoring and personalized nursing management of the health state of the patient are realized.

An embodiment herein provides a system and method for generating hyper-personalized care pathways. The system includes a data ingestion module configured to receive and process computer executable data from one or more data sources. The system includes a profile generation module operatively coupled to the data ingestion module. The profile generation module is configured to synthesize the computer executable data into a hyper-personalized computer executable profile by applying a machine learning algorithm and predictive analytics. The profile generation module is configured to generate a multi-dimensional computer executable representation of current health status and predicted future healthcare needs of the subject. The system includes a pathway generator module that is configured to determine a set of next best actions for the subject based on the hyper-personalized computer executable profile.

The invention provides a neurology clinical nursing potential safety hazard analysis method and device, and relates to the technical field of neurology clinical nursing, and the method comprises the steps: inputting a risk feature matrix generated based on multi-source heterogeneous nursing data into a rule engine and graph neural network model, and outputting a preliminary screening risk event set; taking the primary screening risk event set and the risk feature matrix as input, and constructing a causal conduction map through a causal discovery algorithm; performing risk conduction quantitative integration on each risk event in the primarily screened risk event set based on a causal conduction map to generate a quantitative risk list; based on the dynamic risk priority number and the conduction path chain, reversely tracing to a root cause node along a directed conduction edge, and generating a targeted intervention strategy packet bound with the root cause node; the targeted intervention strategy package is pushed to the nursing responsible person terminal, the strategy execution effect and the risk evolution data are recorded, and a closed-loop disposal database is generated, so that the dynamics and the effectiveness of potential safety hazard analysis of clinical nursing of the neurology department are improved.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention relates to the field of medical health management, in particular to a neurosurgery postoperative care management system and method. The method specifically comprises the steps that physiological index data of a patient is monitored and collected regularly, and a comprehensive index is calculated based on the physiological index data; based on the comprehensive index, calculating a current complication risk and a future complication risk by adopting a dynamic trend risk assessment and prediction algorithm to obtain a current complication risk value and a future complication risk value; and calculating a personalized nursing index based on the current complication risk value and the future complication risk value, judging the nursing priority of the patient according to the personalized nursing index, and adjusting the nursing scheme according to the nursing priority. The problems that a traditional postoperative nursing method lacks real-time dynamic evaluation, lacks comprehensive evaluation on the overall physiological state of a patient, is low in nursing efficiency and cannot meet the nursing requirements of part of patients in time are solved.

The invention discloses an Alzheimer's disease long-term prediction and dynamic intervention method based on deep learning. Accurate dynamic intervention from group statistics to individual dynamics is realized through time sequence alignment, multi-scale time sequence feature extraction, dynamic risk assessment and personalized intervention. Through time sequence alignment and multi-scale time sequence feature extraction, a time-sensitive personalized intervention scheme can be generated. And carrying out continuous time modeling by adopting a cubic Hermite interpolation method and a neural control differential equation, and predicting the long-term risk. By establishing a double-track interaction model, a pathological track and a functional track of a user are analyzed, so that time-varying association among multi-modal data can be dynamically captured. A personalized intervention strategy is provided through reinforcement learning, the intervention strategy is dynamically adjusted in combination with risk reduction amplitude, intervention measure compliance and physiological index change, the effect of short-term behavior change and long-term prediction is balanced, and the Alzheimer's disease is further promoted to be converted from passive treatment to active intervention.

The invention relates to a battle wound field real-time hemostasis method based on a biological information sensing technology, and the technical scheme comprises the steps: introducing a time sequence biological signal for modeling, and carrying out the dynamic prediction of a battle wound state; on the basis of a biosensor data flow, a multi-dimensional time sequence state space is constructed, and the multi-dimensional time sequence state space comprises a nonlinear change trend of a bleeding rate, an evolution mode of tissue physiological parameters and a dynamic evolution process of a blood coagulation process; external intervention requirements are minimized while a hemostasis strategy is dynamically adjusted through active learning; defining multi-objective optimization modeling of key objectives of hemostasis efficiency, tissue damage minimization and pressure adaptability; estimating distribution prediction of success probabilities of different hemostasis strategies; the intelligent response mechanism is composed of a nanoscale blood coagulation factor carrier and a phase change regulation and control network. An environment triggering response model is constructed, and the blood coagulation factor carrier selectively releases blood coagulation factors according to biological signals including the pH value, the temperature and the bleeding rate.

The invention discloses a traumatic orthopedic nursing data intelligent analysis method, and relates to the field of medical data analysis. A traumatic orthopedic nursing data intelligent analysis system constructed by the method comprises a scheme management module, a data acquisition module, a data processing module, a nursing stage analysis module, a nursing data analysis module and a nursing scheme optimization module. According to the method, a complete trauma orthopedic nursing scheme library is constructed, comprehensive nursing schemes of different trauma types are covered, a rich data basis is provided for subsequent analysis, and the comprehensiveness and applicability of the nursing schemes are improved. Through the nursing stage analysis model, the wound type and the nursing stage of the patient can be corrected and confirmed, and the analysis accuracy is remarkably improved.

The invention relates to the technical field of nursing scheme intelligent recommendation systems, in particular to a cardiology patient personalized nursing scheme intelligent recommendation system optimization method, which comprises a dynamic rare case recognition engine for accurately capturing and recognizing cardiology rare cases by using medical data and text data, and comprises a data preparation module, a data retrieval module, a data processing module and a data processing module, the candidate sample classification and recognition module is used for recognizing whether rare cases exist or not, and the problems existing in the prior art are effectively solved by introducing innovation modules such as a dynamic rare case recognition engine, a knowledge graph builder and a rare case simulation generator. According to the invention, continuous learning and updating can be realized, rare cases can be accurately identified, and customized nursing suggestions can be provided based on individual features of patients. Meanwhile, the decision-making process of the system has good interpretability, decision-making basis can be provided for doctors, and usability and credibility of the system in clinical practice are greatly improved.

The invention belongs to the field of rehabilitation medical treatment and intelligent computing, and particularly discloses a cerebral apoplexy rehabilitation personalized treatment recommendation system based on artificial intelligence. Comprising a multi-modal data acquisition module, a feature extraction and clustering modeling module, a personalized path making module and a recommended path generation and feedback optimization module, and can realize dynamic monitoring and intelligent analysis of rehabilitation data of a stroke patient; a multi-modal aggregation neural network model is constructed by fusing multi-dimensional data such as images, biomarkers, clinical behaviors and cognitive emotions, a rehabilitation path is formulated according to a medical data clustering result, and a recommended path is dynamically adjusted and optimized in combination with treatment feedback of a patient; compared with a traditional method, the method has the advantages that scientificity and adaptability of personalized treatment are improved, the method has the advantages of real-time updating of rehabilitation paths, closed-loop management of a system, high model robustness and the like, rehabilitation intervention efficiency and accuracy can be remarkably improved, and technical support is provided for intelligent development of rehabilitation medicine.

A stuffed, interactive children's toy for assisting individuals in self-calming and / or self-regulating exercises. The toy may include one or more weights, a microphone, a motor, one or more light emitting diodes (LEDs), and / or one or more speakers. The toy optionally further includes one or more buttons corresponding with one or more emotional states. For example, a red button, a yellow button, and / or a green button. Upon pressing the button, the toy may deliver either a random and / or a preprogrammed intervention. The toy may be used in combination with a children's book. In another example, the toy may be in communication with a mobile application (app) via a Bluetooth connection. In yet another example, the toy may be in communication with a website and / or a remote-control device. The app may be used to record emotional data, physical date, and / or to assign preprogrammed stimuli to the one or more buttons.