3111 results about "Medical care" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention discloses a real-time health risk prediction method and system based on a dynamic knowledge graph, and relates to the technical field of medical information. The method comprises the following steps: carrying out multi-modal fusion and privacy protection preprocessing on medical and nursing heterogeneous data, and realizing semantic consistency of cross-mechanism data based on an entity alignment method of a cross-modal graph neural network; based on a hierarchical federated learning framework, local model parameters are subjected to hierarchical encryption and aggregation through a secure multi-party computing protocol to generate an initial global model, and prediction distribution of the global model is optimized in combination with knowledge distillation of differential privacy constraints; designing a gradient difference dynamic updating trigger mechanism of noise robustness, smoothing noise interference through a sliding window mean value, and realizing adaptive threshold calibration through linkage model performance verification; and light-weight deployment real-time reasoning is realized based on redundant edge pruning of confidence and 8-bit symmetric quantization. On the premise of protecting data privacy, the real-time performance and accuracy of health risk prediction are remarkably improved, and the method is suitable for a cross-institution medical care collaborative decision-making scene.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention relates to the technical field of health management and artificial intelligence, in particular to an old people health analysis system and method based on combination of medicine and nursing. The method comprises the steps of collecting and processing multi-modal health data, and generating a health data vector set; mapping the vector set to a standardized health knowledge ontology, constructing a health map initial structure and generating a map node vector; generating a health portrait and an evolution prediction result based on time sequence modeling; identifying risk features according to the health portrait and the prediction result, and generating a medical care service strategy set; and issuing the strategy set to an execution unit and collecting feedback, and finally updating a graph structure and optimizing model parameters based on feedback data. According to the invention, multi-modal data-driven old people health dynamic evolution modeling and personalized service strategy closed-loop optimization are realized, and the intelligent level and response efficiency of medical and nursing services are improved.

A system configured to facilitate monitoring a patient when the patient transitions between environments. The system can have an in-room display terminal configured to display indicia of a health of the patient for electronically monitoring the health of the patient within a healthcare environment. The system can receive, via the in-room display terminal, a request to initiate monitoring the patient with the in-room display terminal at the healthcare environment; access historical physiological data associated with the patient and generated by a home monitoring device before the patient enters the healthcare environment; access real-time physiological data associated with the patient and originating from a physiological monitoring device coupled to the patient within the healthcare environment; generate one or more physiological parameters from the real-time physiological data and the historical physiological data; and cause the in-room display terminal to display indicia of the one or more physiological parameters.

The invention discloses an AI-based medical and health care integrated data management method and system and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the data preprocessing and data standardization of collected multi-source medical and health care data, and obtaining a standardized medical data set; performing classification and semantic annotation on the data of the standardized medical data set by using a preset medical ontology, and constructing a structured medical knowledge graph; performing virtual mapping based on the structured medical knowledge graph by adopting a digital twinborn model to form a virtual medical twinborn body; based on the virtual medical twins, combining real-time monitoring data and historical patient flow data, through multi-dimensional feature extraction and pension mode recognition, dynamically optimizing and presetting a personalized medical scheme and a resource scheduling strategy, and outputting a medical care service optimization scheme; generating a target service list in combination with the analyzed doctor-patient dialogue text, and distributing the target service list to a preset pension service cooperation terminal; the method has the effect of improving the real-time performance and the accuracy of the old people medical care service strategy.

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

The invention relates to the technical field of medical care management, in particular to an intelligent mental disease risk assessment system which comprises a physiological rhythm extraction module, a rhythm alignment module, an interference analysis module, a branch recognition module and an assessment output module. According to the method, the sleep duration and the heart rate change are continuously recorded, the corresponding relation between the sleep rhythm and the heart rate fluctuation is constructed, a quantifiable mental rhythm parameter set is formed, dynamic mapping is established between sleep interruption and the heart rate fluctuation, and the stability of the individual physiological rhythm and the time structure of rhythm deviation are effectively revealed. Anchoring is performed based on a maximum offset day, a time contrast section is established around a specific time point, and differential features of variables in the section are finely extracted, so that micro rhythm disturbance is dynamically captured and measured. A coupling factor evaluation mechanism is constructed by using cross decision logic of sleep time history and heart rate amplitude, and the sensitivity and multi-dimensional analysis ability of rhythm change recognition are enhanced.

The invention provides a three-dimensional simulation and infection scene decision optimization system and method based on digital twinning, and relates to the technical field of intelligent medical treatment and digital twinning crossing technologies. The three-dimensional simulation and infection scene decision optimization system and method based on digital twinborn comprises the following modules: a data acquisition module, a path optimization module, a risk analysis module, a disinfection scheduling module and a collaborative prevention and control module, by arranging an air microorganism sampler, ultra-wideband positioning equipment and a temperature and humidity sensor, pathogen concentration, personnel trajectory and environmental parameters are collected in real time. Through a multi-source sensor fusion algorithm and a space-time attention mechanism, weight fusion and noise filtering are carried out on pathogen concentration, personnel tracks and environmental parameters in a hospital environment, the problems of information isolation and noise interference in traditional data collection are solved, and the space-time relevance of multi-modal data is remarkably enhanced.

The invention relates to the technical field of wireless communication, and particularly discloses a self-adaptive anti-interference communication method and system based on star flash-Bluetooth dual-mode, and the method comprises the steps: monitoring channel interference parameters in real time through a dual-mode radio frequency module on a communication terminal, including the out-of-band radiation power and the pulse interference duty ratio at the switching moment; instantaneous interference intensity features are extracted by using synchronous compression wavelet transform and sparse time-frequency analysis, periodic pulse interference features are identified by combining Hawkes process modeling, and a comprehensive anti-interference feature vector is constructed by fusion; the feature vectors are input into a pre-trained gradient boosting tree model for communication quality evaluation, an optimal communication mode is dynamically selected according to a scoring result, intelligent response to a complex interference environment is achieved, the stability and reliability of a communication system in a high-interference scene are improved, energy efficiency optimization and resource scheduling are considered, and the method is suitable for large-scale popularization and application. The method is suitable for industrial, medical, Internet of Things and other application scenes with strict requirements on communication quality.

A digital healthcare architecture including a cloud-based virtual clinic platform configured to facilitate remote therapy of one or more patients based on biometric authentication systems and methods. In an example arrangement, one or more biometric indicia of a user (e.g., a clinician and / or a patient) as well as one or more non-biometric input factors may be used in authenticating the user prior to granting access to a protected resource or application (e.g., a therapy application executing on a UE device) configured for effectuating remote programming of an implantable medical device (IMD) operative to provide therapy to the patient.

The invention relates to the technical field of artificial intelligence, and discloses an interaction method and device based on voice recognition, equipment and a storage medium, and the method comprises the steps: obtaining collected user voice; a recognition model is adopted to convert the user voice into voice characters, and the identity of a speaker and the emotion of the user are obtained through recognition; obtaining the above-mentioned reply text, converting the voice text, the speaker identity and the user emotion into a prompt word text by adopting a language model, and generating a target reply text according to the above-mentioned reply text and the prompt word text; and converting the target dialogue text into target dialogue voice by adopting a text-to-voice model, and controlling a loudspeaker to play the target dialogue voice. The method can be applied to business management program systems of financial science and technology, medical treatment and the like, the problem of double limitation of intelligence and emotional interaction faced by existing human-computer interaction is solved, and the industry service quality and the user stickiness are improved.

An AI-powered system is disclosed for conducting medical interviews, generating comprehensive medical history reports, and providing clinical insights. The system leverages a large language model (LLM) fine-tuned on domain-specific medical datasets to generate interview prompts, iteratively refine responses based on natural language processing (NLP), and identify incomplete or inconsistent information. The system provides patients with education specific to their situation and tailored to varying levels of health literacy. The system ensures compliance with data protection regulations such as HIPAA and GDPR by employing advanced encryption techniques, multi-factor authentication, and secure data storage. The generated medical history report includes a structured clinical summary, and may include provisional diagnoses with associated ICD-10 codes, clinical analysis, suggestions for diagnostic evaluation and treatment, and other pertinent information. Additional features include EHR integration, customizability, scalability, multilingual support, bias mitigation techniques, and adaptability to diverse demographic groups, ensuring equitable and inclusive healthcare delivery.

Systems and methods for automatically populating a post-operative report of a surgical procedure are disclosed. A system may include at least one processor configured to implement a method including receiving an identifier of a patient, an identifier of a healthcare provider, and surgical footage of a surgical procedure performed on the patient. The method may include analyzing frames of the surgical footage to identify phases of the surgical procedure based on interactions between medical instruments and biological structures and, based on the interactions, associate a name with each phase. The method may include determining a beginning of each phase and associating a time marker with the beginning of each phase. The method may include populating a post-operative report with the patient identifier, the names of the phases, and time markers associated with the phases in a manner that enables the health care provider to alter the post-operative report.

The invention relates to the technical field of intelligent medical treatment and personalized nursing assistance, and particularly discloses a method and a system for generating a nursing assistance scheme for orthopedic patients. The method comprises the following steps: acquiring multi-modal data such as an electronic medical record, a medical image and rehabilitation monitoring, and constructing a semantic structure body; extracting a key rehabilitation state by using a graph neural network and time sequence modeling and generating a label graph; semantic matching and limiting condition extraction are carried out in combination with a nursing target library, and a target-constraint pair set is constructed; reasoning a nursing path based on a path generation model, and performing scoring modeling and weight updating in combination with execution feedback; and finally, cross-patient optimization and closed-loop semantic updating of the path model are realized. According to the method, the individuation and dynamic adaptive capacity of nursing path generation can be improved, and the intelligent level of nursing aid decision making is improved.

The invention relates to the technical field of medical care, in particular to an AI-based personalized mental health assessment and intervention method, which comprises the following steps: S1, data acquisition: acquiring multi-dimensional data of a user; s2, data preprocessing: preprocessing the collected multi-dimensional data; s3, psychological health portrait construction: generating a personalized psychological health portrait of the user; s4, social relation psychological influence analysis: identifying an emotion change mode, a negative emotion propagation path and a social isolation risk of the user in social interaction, and dynamically analyzing emotion propagation characteristics in a social network structure and potential influence factors of a user psychological state; s5, making a personalized psychological health intervention scheme: generating the personalized psychological health intervention scheme; the method disclosed by the invention not only reveals the potential influence of social interaction on mental health, but also provides a data-driven scientific basis for further personalized mental health intervention.

The invention relates to the technical field of medical care informatics, and discloses a laryngeal mask ventilation control method suitable for outpatient anesthesia, which comprises the following steps: before core analysis, injecting and monitoring a high-frequency signal to confirm the integrity of an original breathing waveform data stream; constructing a real-time ventilation waveform trajectory in a differential phase space only on the premise that a data stream is complete, and performing morphological comparison with an individualized baseline template; meanwhile, when a parameter adjustment event of the respirator is monitored, the baseline template is automatically re-calibrated, a collaborative analysis framework with front information source quality inspection and dynamic reference self-adaption capabilities is constructed, ventilation monitoring is converted from passive response to an isolated peak point into active insight to a system dynamics evolution trajectory, and the system dynamics evolution trajectory is optimized. Therefore, the risk of gradual deterioration of the ventilation state caused by tiny air leakage of the laryngeal mask or secretion accumulation can be clinically recognized in an early stage, and intervention time is won for anesthetists.

The invention belongs to the field of artificial intelligence, particularly relates to a medical resource scheduling optimal configuration method for data analysis, and aims to solve the problems of non-uniform resource allocation, response lag, difficulty in data integration decision and the like in existing medical resource scheduling. According to the method, multi-source heterogeneous medical data (such as patient diagnosis and treatment, medical care, equipment states and department parameters) are uniformly collected, preprocessed and deeply analyzed, and real-time resource state characterization is constructed; based on the characterization, the demand is accurately predicted, supply is evaluated, an optimization algorithm is used for dynamic scheduling configuration, and an optimal scheme is generated. Then, scheduling is executed according to the scheme, and iterative optimization is fed back in real time; the medical service efficiency and quality can be remarkably improved, the patient experience is optimized, the operation cost is reduced, emergencies are effectively dealt with, and the resource management toughness of medical institutions is enhanced.

The invention discloses a coronary intervention postoperative risk prediction model construction method and system, and belongs to the technical field of medical care information and health monitoring. A coronary intervention postoperative risk prediction model construction method comprises the steps of constructing an attending doctor experience scoring system and a nursing personnel ability scoring system, and performing weighted fusion on results of a doctor experience model and a nursing personnel model to form a final additional model for output. According to the coronary intervention postoperative risk prediction model construction method and system provided by the invention, the main model is combined with the additional model, the main model is comprehensively modeled through the regression model and the time sequence model based on the static and dynamic characteristic data of the patient, and the individual health state change of the patient is captured; the additional model evaluates the influence of medical teams and relatives on the postoperative risk by quantifying the experience of doctors and the ability of caregivers, and overcomes the defect that a traditional model only depends on patient data.

A wearable electronic device, a system and methods of monitoring with a wearable electronic device. The device includes a hybrid wireless communication module with wireless communication sub-modules to selectively acquire location data from both indoor and outdoor sources, as well as a wireless communication sub-module to selectively transmit an LPWAN signal to provide location information based on the acquired data. The device may also include one or more sensors to collect one or more of environmental data, activity data and physiological data. The device may transmit some or all of its acquired data to a larger system, including a cloud-based server to, in addition to providing location-based data, be used as a part of a predictive health care protocol to correlate changes in acquired data to salient indicators of the health of a wearer of the device. In one form, the predictive health care protocol uses a machine learning model.

The invention relates to the technical field of medical care informatics, and discloses an intelligent post-stroke cognitive impairment assessment method based on brain network dynamic reconstruction, which comprises the following steps: acquiring multi-modal brain image data of a patient, dynamically compensating the difference between haemodynamic delay and white matter conduction velocity through a nerve-blood vessel coupling model, and calculating the post-stroke cognitive impairment; precise space-time alignment of function and structure signals is realized; a pulse neural network dynamic entropy change model is constructed based on the alignment data, functional connection strength change is coded into a pulse distribution sequence, and Gamma wave band synchronization enhancement and other compensatory features are extracted; constraining rationality of brain network reconstruction by adopting an energy optimal transmission model, and generating a compensatory thermodynamic diagram; and real-time calculation of the edge end is realized through pulse time sequence compression coding. The limitation of traditional static analysis is avoided, the algorithm achieves microsecond response on embedded equipment through the pulse coding technology, and a reliable stroke rehabilitation evaluation tool is provided for primary hospitals.

The invention provides an interaction method and device based on agent cooperation, an agent and a storage medium, belongs to the technical field of artificial intelligence, particularly relates to the technical fields of deep learning, large models, agents, AIGC and the like, and is applied to application scenes such as wisdom education, video production, wisdom medical treatment and the like. The intelligent agent cooperation-based interaction method comprises the following steps: performing intention understanding on initial input information for a target object to obtain an initial intention understanding result; performing interaction mode demand detection on the initial intention understanding result by using a first agent to obtain an interaction mode demand attribute; an interaction element matched with the interaction mode demand attribute is called to interact with the target object, interaction information representing the demand intention of the target object is obtained, and the interaction element is used for responding to the interaction behavior; and controlling the second agent to execute the target task based on the interaction information to obtain an execution result, and pushing the execution result to the target object.

The invention discloses a model watermark robustness enhancement method based on antagonistic immunity, which mainly solves the problem that the existing model watermark is difficult to resist various watermark removal attacks, and adopts the scheme that a public data set and a convolutional neural network suitable for an image classification task are respectively used as original data and a target model; training a target model by using the original data; taking the generation model as a watermark generation model and training the watermark generation model by using the original data; inputting the original data into the trained generated watermark model for forward propagation to output watermark data; splicing the original data and the watermark data, and inputting the spliced original data and watermark data into the trained target model for forward propagation to obtain a model watermark; tiny disturbance is added to original data to generate an adversarial sample, the adversarial sample is spliced with the original data and then input into the model watermark for robustness training, and the model watermark with enhanced robustness is obtained. The method can effectively enhance the generalization ability of the model watermark under different data distributions, improves the robustness of the model, and can be used in the fields of information security, financial services, medical care and the like.

The invention discloses a data security authentication method and system for smart medical treatment, and relates to the field of data security, and the method comprises the steps: obtaining the identity authentication information of a user, and generating an anti-quantum root key; obtaining operation data and an operation log; according to the operation type, dynamically deriving a temporary sub-key based on the master key, and decomposing the temporary sub-key to obtain m temporary sub-key fragments and n temporary sub-key fragments; transmitting the m temporary sub-key fragments to the client terminal; generating a second signature fragment based on the n temporary sub-key fragments, receiving the first signature fragment, and combining the first signature fragment and the second signature fragment to obtain an operation signature; calculating a hash value of the operation data, and embedding the hash value into the operation signature; and encrypting the operation log based on the embedded operation signature to generate a tamper-resistant log, and marking the temporary sub-key as a failure state. The security of the medical data can be effectively improved.

The invention discloses a medical insurance intelligent auditing and path recommending method and system based on a large language model, and particularly relates to the technical field of medical insurance auditing, and the method comprises the steps: S1, analyzing massive medical insurance policy documents through a first LLM, and generating a comprehensive confidence coefficient in combination with triple confidence coefficients of semantics, context support degree and timeliness, after grading processing, constructing a dynamically updated medical insurance rule knowledge base; s2, converting free text medical advices of doctors into structured data by means of a second LLM, quantifying the integrity of the medical advices through field weights, and dynamically asking and complementing information in combination with constraint urgency; s3, associating the diagnosis and treatment scheme with the medical insurance information of the patient, matching rules in real time, and generating a color identifier for early warning violation; s4, recommending compliance alternative schemes in combination with the medical knowledge graph, and sorting according to multi-dimensional scores such as medical insurance economy and the like; and S5, automatically extracting multi-system data, and generating a standardized medical insurance declaration report. The medical insurance management efficiency is improved, the burden of medical care and medical insurance departments is relieved, and reasonable use of medical insurance funds is guaranteed.

A communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT) may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. A method performed by a user equipment (UE) includes: receiving downlink data and / or downlink control signaling from a base station; determining uplink data and / or uplink control signaling, an uplink time unit and / or an uplink physical channel for transmitting the uplink data and / or the uplink control signaling based on the downlink data and / or the downlink control signaling; and transmitting the uplink data and / or uplink control signaling to the base station on the determined uplink time unit and / or uplink physical channel.

The invention relates to the technical field of medical care, and provides a patient risk dynamic assessment system in medical care teaching, which comprises a data acquisition module used for acquiring multi-modal image data of a postoperative gastric organ anastomosis area of a patient; the data processing module is used for preprocessing the multi-modal image data, establishing a three-dimensional model of organs around the anastomotic stoma and analyzing edge features of the anastomotic stoma; and the risk assessment module is used for constructing an anastomotic stoma state assessment model and outputting comprehensive scores of an anastomotic stoma blood perfusion condition, a tissue metabolism abnormal probability, an anastomotic stoma leakage risk, anastomotic stoma scar hyperplasia and healing trend prediction. A quantum dot fluorescence image, a Raman spectrum image, a CT image and an intraoperative high-definition white light image are enhanced through the system and evaluated, a nursing scheme is obtained in combination with opinions of medical staff, and compared with manual evaluation of the medical staff, the accuracy is higher, the feasibility of the nursing scheme is higher, and medical nursing use is facilitated.

An apparatus, including a computer including a database which stores a controllable healthcare record and information contained in a master records file, and a distributed ledger and Blockchain technology system. The apparatus facilitates a video call between a user device and a provider device. The computer, after processing information for updating the controllable healthcare record, processes information for identifying a plurality of electronic records for the individual and a second healthcare provider associated with each electronic record. The computer updates each of the plurality of electronic records, and generates a record update message. The computer transmits the record update message to each of a plurality of second provider devices associated with each second healthcare provider. Information regarding the update to the controllable healthcare record and the update to each of the second records is stored in the distributed ledger and Blockchain technology system.

A point of care analyte detection and quantification system for healthcare application is provided. Related methods are also provided.

The invention discloses a multi-modal face recognition method and system based on deep learning, and relates to the technical field of face recognition, 3D structured light, near-infrared images and PPG blood flow signals are synchronously fused, occlusion completion is achieved through a double-branch generation network, PPG spectrum constraints generate blood vessel distribution, and biological feature authenticity is ensured; low-confidence sub-regions are divided based on a structural similarity algorithm, the weight of a loss function is dynamically adjusted, only problem regions are iteratively generated, and calculation redundancy is reduced; the depth feature of the complemented image and the blood vessel frequency spectrum main frequency band overlapping rate are extracted, the identity authenticity is doubly verified, and 3D mask and photo attacks are resisted; the performance bottleneck of traditional single-mode identification in a shielding scene is broken through, closed-loop optimization is driven through data, precision, efficiency and safety are considered, and the method is particularly suitable for high-requirement scenes such as medical treatment and finance.