17 results about "Patient status" patented technology

An alarm notification system can enable a clinician to respond to an alarm notification received via a computing device, which may have more advanced functionality than a pager. The clinician's device can include a notification client which can respond to alarm notifications. The notification client can also provide one or more user interfaces that enable the clinician to view information about an alarm, such as information about a patient's status, physiological parameter values, trend data, audio / video of the patient, combinations of the same, or the like. Further, the notification client can provide functionality for a clinician to respond to an alarm, annotate an alarm, and / or indicate that the clinician can or cannot respond to the alarm, among other features. In addition, the clinician device can also (or instead) include an admit module that provides for automatic association of a patient to a device or location.

This invention relates to the field of data analysis technology, specifically disclosing a method and system for secure sharing and exchange of ICU data. The method and system are characterized by the following steps: Patient status data is input into a pre-built clinical pathway prediction model to generate predicted pathway data; based on key department identifiers in the predicted pathway data, a pre-authorization token is generated for each key department identifier, and the generated pre-authorization token is sent to the corresponding department's server; according to the pre-authorized data range identifier, matching target patient data is filtered from the patient status data, and the target patient data is pre-fetched to the edge cache node corresponding to the department server; access intent data is extracted from data access requests; it is determined whether there is a target pre-authorization token that matches the department identifier and is in a valid state; and it is determined whether the data type identifier of the requested access falls within the pre-authorized data range identifier associated with the target pre-authorization token. The advantage is that it improves the efficiency of secure data sharing and exchange.

This application discloses a medical data processing method based on generative AI, belonging to the field of medical data processing technology. The method includes: performing AI question-and-answer sessions with patients and receiving feedback text; obtaining standard symptom vectors based on mapping functions; calculating the cosine similarity with each vector in a standard entity library to determine fuzzy text; extracting speech emotion features to obtain input vectors; reconstructing the current state vector and uncertainty vector based on latent variables; identifying the patient's communication intent; and constructing a continuous quantization mapping mechanism to transform the patient's inexpressible vague feelings into a high-dimensional state vector sequence, overcoming the shortcomings of existing technologies in capturing patients' fuzzy information and implicit states, and achieving refined quantitative perception of the patient's condition. By decoupling the patient's state vector, which contains multiple factors, into independent factors with clear clinical semantics, the method clearly distinguishes the dominant factors leading to changes in the condition, solves the problem of causal attribution of changes in the condition, and improves the value of follow-up visits.

A model-assisted system for extracting patient information. The processor may be programmed to access a database storing one or more medical records associated with a patient and to use a first machine learning model to determine whether the patient is associated with a condition based on unstructured information included in the one or more medical records. The processor may be further programmed to identify a date associated with the patient and to use a second machine learning model to determine whether the patient is associated with the condition associated with the date based on the unstructured information. The processor may generate an output indicating whether the patient is associated with the condition and whether the patient is associated with the condition associated with the date.

This invention provides a postoperative data analysis and summarization system for obstetrics and gynecology, comprising: a data acquisition module for real-time acquisition of multi-source heterogeneous data from postoperative obstetrics and gynecology patients, wherein the multi-source heterogeneous data includes vital sign time-series data, biochemical test data, and electronic medical record text data; this invention constructs a hybrid feature matrix containing vital sign time-series features, biochemical indicator discrete features, and text semantic features, establishes a postoperative complication risk prediction model using an improved weighted random forest algorithm, and introduces the principle of information entropy reduction to summarize and reduce the dimensionality of massive postoperative data, thereby solving the problems of low utilization rate of postoperative obstetrics and gynecology data, delayed risk warning, and difficulty in summarizing unstructured data in existing technologies, realizing real-time monitoring and accurate assessment of postoperative patient status, and possessing significant novelty and inventiveness.

A medical device and an information display method are disclosed. The medical device includes a memory for storing an executable program, a processor and a display. The processor is used for executing the executable program to perform following operations: acquiring patient data of a patient, which includes monitoring data of one or more vital sign parameters of the patient; analyzing the patient data according to preset rules to obtain the patient state of the patient and a comparison result of at least one vital sign parameter associated with the patient state and a preset alarm condition; and presenting in the display the patient state on a human body diagram, and via text or graphics, the comparison result of the at least one vital sign parameter associated with the patient state and the preset alarm condition.

The present application relates to the technical field of medical Internet of Things, and in particular to a self-adaptive infusion collaborative control method and system based on intelligent decision-making. The method comprises: an end device analyzes multi-modal physiological time series data and infusion parameters through an end-to-end patient state evaluation model integrated in a sub-body infusion unit, and outputs a patient state index and infusion suggestions; combined with a pipeline pressure signal, an adaptive flow rate control algorithm based on LSTM is used to generate motor control instructions to realize closed-loop control. The edge device periodically aggregates local model parameter updates of multiple end devices through a mother base station. The cloud device fuses all updates uploaded by the edge devices based on a federated averaging algorithm, generates an optimized global model, and issues the global model. The present application realizes a paradigm shift from isolated execution to collaborative perception-decision-evolution of infusion, drives continuous evolution of group intelligence under the premise of protecting data privacy, and improves the accuracy, safety and overall intelligent level of infusion therapy in complex environments.

The application provides an intelligent scheduling method and system for a multi-mode emergency treatment process, and relates to the technical field of medical resource scheduling. Firstly, the application collects real-time vital sign data and illness description data of emergency patients to form a multi-dimensional patient data set. Secondly, the application evaluates the emergency level and the required treatment mode type and obtains the medical resource state data of the emergency department. Thirdly, the application generates a multi-mode process scheduling instruction in combination with the emergency level, the required treatment mode type and the medical resource state data. Finally, the application executes the treatment process according to the multi-mode process scheduling instruction and continuously monitors the patient state and resource changes during the execution of the treatment process to update the multi-mode process scheduling instruction. The technical scheme provided by the application realizes standardized grading and dynamic scheduling planning by fusing multi-dimensional patient and resource data, and improves the response efficiency, resource utilization rate and process robustness of emergency treatment through closed-loop adaptive optimization.

1. The name of the design product: the toolbar and information window of the physical sign monitoring graphical user interface of the electronic device. 2. The use of the design product: an electronic device. 3. The design points of the design product: the graphical user interface. 4. The picture or photo that best indicates the design points: design 1 front view. 5. Design 1 is designated as the basic design. 6. The use of the graphical user interface: the overall use of the interface is to display patient respiratory related parameters, anesthetic gas concentration and flow, display device operating status, and monitor patient status. The use of the toolbar of the interface is to quickly locate and call the required tools, and the use of the information window of the interface is to pop up the interactive window for the user to obtain information. 7. The human-computer interaction mode of the graphical user interface: in each design, the front view is the main interface displayed by the electronic device, and the user clicks any key in the toolbar on the right side of the interface, and the information window can display the corresponding information. 8. Other circumstances that need to be explained: the semi-transparent blue coated area in the figure represents the unclaimed part.

This invention discloses a patient intraoperative fall-prevention early warning system and method, belonging to the field of intelligent operating room monitoring technology. The system includes: a multimodal sensing module for real-time acquisition of multi-dimensional sensing data of the patient's intraoperative status; the multi-dimensional sensing data includes pressure distribution data, posture motion data, optical positioning data, and environmental risk data; an edge computing terminal for performing fusion processing on the multi-dimensional sensing data, including timestamp alignment, filtering, and correlation analysis; an intelligent early warning and braking module for providing graded early warnings based on the probability of a fall and triggering corresponding braking measures; and a central monitoring platform for visually displaying the patient's status and risk level, and managing historical data. This invention can integrate multi-dimensional sensing information and is suitable for patient safety monitoring in scenarios involving anesthesia, fixed positioning, or intraoperative movement.

This invention relates to the field of smart healthcare technology, providing an information aggregation service system and intelligent distribution method for smart healthcare. The system includes a data acquisition interface module, a federated learning privacy-preserving aggregation module, a dynamic medical knowledge graph construction module, a patient digital twin modeling module, and an intelligent distribution decision engine. By collecting multi-source heterogeneous medical data and achieving privacy-preserving aggregation through differential privacy and homomorphic encryption within a federated learning framework, a dynamically evolving medical knowledge graph is constructed, while simultaneously establishing a personalized patient digital twin model. Based on this, an information distribution strategy is generated based on medical knowledge, patient status, and clinical scenarios, and a multi-level response mechanism is used to achieve hierarchical information distribution and emergency handling. This invention can improve the utilization efficiency of multi-source medical data while ensuring the privacy and security of medical data, achieving accurate distribution and rapid response of medical information.

This vital data evaluation method includes: acquiring a first vital data set corresponding to a first vital sign of a first type from a patient in a measurement period; acquiring a second vital data set corresponding to a second vital sign of a second type different from the first type; classifying the first vital data set on the basis of a first classification condition; classifying the second vital data set on the basis of a second classification condition; generating first time integrated value information at each time point in the measurement period on the basis of first vital data in an integration interval corresponding to each time point and shorter than the measurement period; generating second time integrated value information at each time point in the measurement period on the basis of second vital data in the integration interval corresponding to each time point; and generating patient state information for estimating a state of the patient on the basis of a relationship between the first time integrated value information and the second time integrated value information.

This application relates to a hospital ward visualization management method and system based on multi-source data fusion. The method includes: collecting data from IoT devices, clinical systems, and nurse terminals in the hospital ward; generating a four-dimensional label for each data point; and outputting tagged data packets. Based on a data type priority matrix, hierarchical compression and transmission scheduling are performed on the data packets to generate hierarchical data streams. The data streams are spatiotemporally aligned to extract a standardized feature vector set. A dynamic weighting strategy based on individual medical attributes is invoked to perform weighted fusion of the feature vectors, generating a comprehensive risk score table. This table is then input into a context-aware rule engine to match alarm thresholds, generating a hierarchical alarm instruction set. This set drives the visualization system to generate a dynamic focused view of the target bed and a nursing task queue. Based on this queue, navigation paths and operation instructions are generated for nurses, and the system receives completion signals to update patient status. This application improves ward monitoring efficiency and nursing safety.

The application provides an emergency resource scheduling method and system based on multi-dimensional positioning and intelligent correction, relates to the technical field of resource scheduling, and comprises the following steps: obtaining initial positioning by fusing multi-source position data, correcting the deviation in combination with the event type and historical distribution characteristics when the deviation is detected, and obtaining the corrected patient position. Meanwhile, the emergency degree is dynamically evaluated based on the time sequence characteristics of the patient state information. Finally, a multi-objective optimization model is constructed to generate a scheduling scheme according to the corrected position, the real-time resource state and the adjusted emergency degree. The application improves the positioning accuracy and the real-time performance of state evaluation, and realizes efficient and optimized scheduling of emergency resources.

The present application relates to the field of first-aid medical information technology, and more particularly to a first-aid transfer path intelligent recommendation method and system based on AI technology, comprising the following steps: S1, collecting patient vital sign data, vehicle operating state data and traffic condition data, performing cleaning and space-time alignment, and constructing a first-aid transfer state data set; S2, performing illness criticality assessment and hospital resource matching based on the first-aid transfer state data set, locking a target hospital through a two-way resource interaction protocol, and generating a main navigation path. In the present application, by uniformly aligning the patient vital signs, vehicle operating state and real-time road condition data in space-time, and introducing illness criticality assessment, hospital resource locking and trajectory re-planning based on medical operation stability, the first-aid transfer path can be dynamically adjusted according to the patient state and road conditions, thereby significantly improving the medical continuity and path decision accuracy of critical patients during the transfer process, and achieving the improvement of overall treatment efficiency.

The application relates to the technical field of postoperative follow-up, and discloses a heart surgery postoperative follow-up management system, which comprises the following steps: a data acquisition module is used for acquiring and storing health record information and multi-source medical data of a target patient; a data processing module is used for performing semantic analysis and structural processing on the multi-source medical data to obtain structured medical data; an index identification module is used for determining follow-up indexes of the target patient based on the structured medical data and the health record information; a sequence generation module is used for generating time sequence data of each follow-up index based on the structured medical data; a strategy generation module is used for generating a follow-up management strategy based on the time sequence data; and a first interaction module is used for sending the follow-up management strategy to a target patient terminal. The system provided by the application has a dynamic adjustment mechanism and intelligent updating capability, can effectively determine a follow-up management strategy according to the state of a patient, and has follow-up flexibility and pertinence.