208 results about "Patient status" patented technology

The invention provides an intelligent pre-examination triage method based on multi-modal data and edge calculation, and relates to the technical field of multi-modal data, and the method comprises the steps: collecting the facial expression, wound part, sound and physiological signals of a patient through medical equipment; multi-modal features are extracted by using a deep learning network and dynamic weighted fusion is carried out to obtain a patient state evaluation result; performing data time sequence alignment and feature extraction based on edge nodes, dynamically adjusting calculation precision and realizing task migration; multi-source data are collected for time sequence alignment calibration and feature fusion, the emergency degree is calculated, and group damage distribution is predicted. According to the method, real-time processing and dynamic optimization of multi-modal data are realized, and the accuracy and efficiency of pre-examination triage are improved.

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.

The invention discloses an intelligent dynamic triage system and method for emergency patients. The triage system comprises a data acquisition module used for acquiring basic information, physiological parameters and historical records of patients; the artificial intelligence analysis module generates a preliminary triage result; the Internet of Things connection module collects hospital resource data in real time; the central control processing unit is connected with each module to receive data; the dynamic grading decision-making module divides the patients into critical symptoms, severe symptoms, subcritical symptoms and common symptoms; the resource allocation module dynamically generates a resource allocation scheme according to the grading result; the diagnosis and treatment process management module tracks the whole process of the patient and updates state data; when the state of the patient changes, the system transmits data to the artificial intelligence analysis module again for evaluation, triggers the grading decision and resource allocation module, dynamically updates the triage priority and the resource allocation scheme, and achieves closed-loop feedback and continuous optimization.

The invention provides a first-aid information processing method and system based on multi-source data fusion. The method comprises the following steps of: performing deep association matching and modeling on an emergency event by utilizing a multi-source heterogeneous data stream, a geographic position, a timestamp and environmental perception data which are acquired in real time to obtain a dynamic emergency event situation model; according to the dynamic first-aid event situation model, a resource optimization algorithm is adopted to evaluate and select the most suitable first-aid resource, and a first-aid resource configuration scheme and instant guidance information are obtained; updating the state of the patient based on the first-aid resource allocation scheme and the instant guidance information, and obtaining a dynamically adjusted first-aid process and an optimized path scheme through geographic information system path planning analysis, first-aid strategy adjustment and resource allocation path optimization; and integrating various feedback data related to the dynamically adjusted emergency process and the optimized path scheme, carrying out post analysis, and generating an optimized guide. According to the technical scheme provided by the invention, the first-aid information processing efficiency is greatly improved.

The invention relates to the technical field of physiological status monitoring, in particular to a rehabilitation assessment method and system based on patient status awareness, and the method comprises the following steps: collecting myoelectricity, heart rate and skin conductance signal data of a patient, normalizing time sequence data, setting a time window, calculating a mean value and a mean square error, and analyzing a signal covariance variation trend. And generating a cross-domain signal stability list. According to the method, the physiological signals of the patient are collected and analyzed, the method has important significance on improvement of the rehabilitation effect, the physiological signals such as myoelectricity, heart rate and skin conductance are collected and analyzed in real time, tiny changes of the state of the patient can be carefully captured, more accurate state evaluation and rehabilitation progress monitoring are achieved, and the rehabilitation effect is improved. The accuracy of data processing and the efficiency of signal decoding are ensured by analyzing the signal stability and optimizing and adjusting the time window, and the rehabilitation scheme is further adjusted according to the specific response of the patient, so that the rehabilitation process is more personalized and targeted.

The invention discloses an information management method and system for a smart ward, a medium and a program product, and relates to the field of electrical digital data processing.The method comprises the steps that physiological index data and behavior characteristic data of a target patient are collected, and a characteristic data sequence is constructed; analyzing each piece of data in the feature data sequence, and determining a current characterization behavior and a behavior change sequence of the target patient; obtaining individual information of a patient, and inputting the individual information of the patient, the current characterization behavior and the behavior change sequence into a behavior evaluation model to obtain a risk characterization behavior and a behavior risk score; determining an early warning level and an early warning response time limit based on the behavior risk score; when the early warning level is higher than a preset early warning threshold value, generating a task execution list according to the early warning response time limit and the risk characterization behavior, and sending the task execution list to the medical care terminal; and updating the behavior risk score according to an early warning response result returned by the medical terminal. By implementing the application, patient state monitoring of the smart ward can be optimized, and potential safety hazards can be avoided to a greater extent.

The invention relates to a patient examination item recommendation method based on a medical knowledge graph. The method comprises the steps that firstly, historical data in the medical field is acquired, key information is extracted from the historical data to generate an entity relation triple, and then a medical knowledge graph is constructed; then, generating a patient state sub-graph based on patient input information, processing the medical knowledge graph by adopting an improved TransD model, and determining unique representation of an entity-relationship pair; mapping the patient state sub-map to a unique representation space, obtaining vector representation of a patient and an examination item related entity, and calculating a similarity value between vectors to obtain a similarity value matrix; and finally, performing priority ranking on the inspection items according to the matrix, and generating a final recommendation list through threshold screening and combination with graph neural network verification, optimization and adjustment of priorities. By adopting the method, examination items can be accurately recommended for patients, doctors are assisted to improve the treatment efficiency, the queuing time of the patients is shortened, and the medical experience of the patients is remarkably improved.

The invention discloses a patient state intelligent monitoring system and method, and relates to the technical field of medical data processing. Comprising the steps that physiological data and microenvironment parameters of a patient are collected through a multi-mode sensor, and an initial data set is formed; performing environmental error correction on the physiological data of the patient by using a dynamic compensation algorithm to generate a preliminary correction data set; calculating motion state characteristic parameters of the patient, and judging whether significant motion artifacts exist or not; for the detected artifact interference signal, correcting the physiological data in the preliminary correction data set by adopting a dynamic artifact removing algorithm to obtain a correction data set; and determining the priority of each physiological data, and combining the correction data set to form real-time output. The method can overcome monitoring data distortion caused by environmental noise, motion artifacts and other problems, improves the precision and reliability of a monitoring result, and has dynamic adaptability and convenience.

The invention provides an anesthesiology department clinical data management method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring sign monitoring data of multiple sample patients in a target anesthesia stage, performing feature extraction, and constructing to obtain a first sign parameter feature vector of each sample patient; clustering the plurality of sample patients to obtain a plurality of reference groups, and determining a sign reference vector of each reference group; obtaining individual difference data of each sample patient, and determining an individual difference reference vector of each reference group; obtaining target individual difference data and target sign monitoring data of the target patient, generating a second individual feature vector of the target patient, and screening out a target group; and calculating a sign difference factor of the target patient and generating a sign monitoring result of the target patient. According to the invention, the comprehensiveness and accuracy of evaluating the state of the patient are improved.

The invention relates to the technical field of medical informatization, and provides an information digital management system and method based on an intensive care medicine department, and the system comprises a data collection module, a data processing module, and a visualization and alarm module. The data acquisition module obtains a patient monitoring data set, and each patient corresponds to a monitoring data sequence. The data processing module executes the following operations on each patient: determining a target monitoring data sequence and four associated monitoring periods including a first monitoring period, a second monitoring period, a third monitoring period and a fourth monitoring period group, and generating a corresponding monitoring subset; generating an early warning signal through a disease deterioration early warning model; and if the signal is abnormal, analyzing an abnormal reason and an influence range, and generating a structured disease condition comprehensive report. And the visualization and alarm module dynamically displays the state of the patient according to the report, and performs graded alarm on the critical area. The system realizes accurate early warning and monitoring through multi-cycle data fusion analysis and three-dimensional visualization.

The invention provides a patient state auxiliary judgment and posture adjustment method, device and system, and relates to the technical field of medical equipment. The method comprises the following steps: acquiring real-time state data of a patient; generating a three-dimensional posture model and emotion parameters of the current body posture of the patient based on the real-time state data; according to the difference degree of the preset target posture and the three-dimensional posture model, a posture adjustment instruction is generated in combination with a manual adjustment instruction of a doctor; and based on the posture adjustment instruction, prompting the patient to adjust the posture until the difference degree is smaller than a preset threshold value and the emotion parameters are in a target emotion range and lasts for a preset duration, and confirming that the patient completes examination preparation. Through the technical scheme provided by the invention, the problems that accurate state judgment is difficult to obtain and posture auxiliary adjustment cannot be performed on the patient before the patient is examined are solved, the accuracy of state perception of the patient and the efficiency of posture adjustment are improved, and the examination experience of the patient is improved.

The present disclosure relates to a device and a method for implementing an LLM model for predicting a condition of a patient on the basis of consultation data, and the device may comprise: a memory for storing at least one piece of consultation data and at least one process for predicting a condition of a patient on the basis of the consultation data; and a processor for performing an operation according to the process, wherein the processor controls an LLM model to be trained on the basis of result data constructed by matching international classification of diseases (hereinafter, ICD) and the consultation data, inputs the consultation data to the trained LLM model to perform inference, and generates condition prediction information of the patient on the basis of an inference result.

The invention provides a medical resource dynamic allocation method and system based on patient state data driving. The method comprises the following steps: receiving a patient state data set of a target user; generating target recommended medical resources; generating a candidate hospital sequence; acquiring a first adjustable medical resource of the first hospital in a first preset time; generating a first adaptation index; and when the first adaptation index is greater than or equal to a preset adaptation index, triggering a resource allocation instruction, and sending the target recommended medical resource to a first hospital. The technical problem that the medical resource preparation time is long due to the fact that data cannot be transmitted to a hospital in real time and medical resource scheduling analysis is lacked in the prior art can be solved, the vital sign data of the patient is transmitted in real time, the hospital is matched through intelligent analysis, the medical resource allocation scheme is generated, and the medical resource allocation efficiency is improved. Before the patient arrives at the hospital, the hospital is prepared correspondingly, so that the technical effect of shortening the waiting time of the patient is achieved.

The invention relates to the technical field of medical informatization, in particular to a ward distress call method and system based on intelligent medical treatment, and the method comprises the steps: obtaining patient state data collected by a plurality of sensors in a ward; receiving a ward help-calling instruction; constructing a multi-modal data fusion matrix based on the patient state data; executing random matrix transformation and feature extraction according to the multi-modal data fusion matrix; based on the extracted features, topological feature mapping and anomaly detection are carried out; executing group theory base risk assessment according to the topological features; calculating a chaotic power system early warning index based on a group theory risk assessment result; determining a call-for-help judgment result according to the early warning index; a corresponding response strategy is executed based on the call-for-help judgment result, the system can comprehensively grasp the state of the patient through the multi-modal data fusion technology, and the evaluation accuracy is greatly improved. And secondly, the robustness of the system is enhanced by random matrix transformation, so that the system can cope with a complex and changeable medical environment.

Disclosed is a system that includes a computer-readable medium (CRM) coupling a variety of devices. The variety of devices can include one or more patient wearable sensor devices, a paired patient device, and a patient devices docking station. The system can also include a smart patient care cart, a nurse station, a lift tracker station, and / or an agnostic aggregation station. The system can also include a patient datastore, a facility datastore, a caregiver datastore, a device datastore, a patient state datastore, a patient management datastore, a facility management datastore, a caregiver management datastore, an electronic health record (EHR) system datastore, a health / fitness datastore, and an agnostic aggregation datastore.

The invention provides an intelligent bed detection method and system, relates to the technical field of medical care, and is suitable for an intelligent bed, the intelligent bed comprises a bed and a bedside guardrail, the bed is provided with a corresponding first sensor, the bedside guardrail is provided with an intelligent detector, and the intelligent detector comprises a sensor assembly. A first sensor signal collected by the first sensor and a guardrail state signal, collected by the sensor assembly, of the bedside guardrail are preprocessed to obtain first sensor information and guardrail state information; predicting a patient state of a patient corresponding to the intelligent bed through a pre-trained patient state recognition model, the first sensor information and the guardrail state information; if the patient is in the off-bed state, counting off-bed time when the patient state is in the off-bed state in real time, and detecting whether the off-bed time reaches a target off-bed time threshold value matched with the patient or not; and if yes, outputting early warning information, so that the patient state can be mastered by paying attention to the behavior actions of the patient and the use state of the sickbed, the patient safety is ensured, and the nursing quality is improved.

The invention relates to the technical field of laparoscope data processing, in particular to a laparoscope risk prediction model based on big data and a construction method thereof.The construction method comprises the following steps that S1, patient sign data before a laparoscope operation is collected from a big data platform, the patient sign data is preprocessed and converted into sign time sequence data, and the sign time sequence data is stored in a database; dividing the physical sign time sequence data into a plurality of time windows with fixed lengths, acquiring statistical characteristics of the physical sign time sequence data in each time window, judging abnormal time points in the time windows according to a preset statistical characteristic threshold value, and generating abnormal physical sign time sequence data. According to the method, through time serialization processing of the physical sign data of the patient, extraction of statistical features in the time window and recognition of abnormal points, the complex fluctuation condition of the state of the patient can be reflected comprehensively. By expanding the abnormal sign time sequence data to the multi-dimensional phase space, evolution trajectories of different types of abnormal signs can be described.

The invention relates to the technical field of monitoring sickbeds, and discloses a monitoring system of an intelligent monitoring sickbed, which comprises the following modules: a data sensing module connected with each group of sensing equipment in the intelligent monitoring sickbed, a sensing data analysis module for receiving data transmitted by the data sensing module and analyzing and processing the data, and an abnormity alarm module for alarming the abnormity of the intelligent monitoring sickbed. According to the state of a patient judged by the sensing data analysis module, an alarm signal is correspondingly triggered, the intelligent remote monitoring module allows medical staff to remotely monitor the state of the patient in real time through a network, and the data analysis module comprehensively analyzes data collected within a period of time and generates a state report of the patient. The safety of a patient can be detected through the arranged data sensing module and the sensing data analysis module, by detecting abnormal behaviors of the patient in time and giving an alarm, the patient can be effectively prevented from falling down and other secondary injuries due to the fact that the patient gets out of bed arbitrarily, and the safety of the orthopedic patient during hospitalization is guaranteed.

The invention relates to the technical field of physiological parameter monitoring, in particular to a patient physiological feature monitoring method based on nursing sampling data. Comprising the following steps: acquiring body temperature, pulse and systolic pressure data in different periods, marking time and sources to generate a period-divided sequence, dividing sampling frequencies in combination with parameter variation and historical data to generate hierarchical labels, identifying trend directions to construct a classification list, extracting time node marking trends to generate a positioning table, and marking the time node marking trends according to the positioning table. And integrating the frequency trend relationship to construct an atlas identification set. According to the method, the anomaly tracking efficiency is improved by integrating parameter data in different time periods and marking source time, enhancing data relevance, dynamically evaluating parameter change characteristics and historical fluctuation levels, dividing adaptive sampling frequencies, optimizing monitoring density and reducing redundancy, identifying trend directions and classifying structures, and constructing positioning labels; the spectrum identification is constructed by integrating the frequency and trend relation, real-time early warning is supported, the nursing strategy and the patient state are synchronized, and high-time-efficiency accurate intervention is supported.

A digital healthcare ecosystem to transform patient care with real-time queue management, including: an appointment and laboratory test booking module configured to allow patients to easily book and schedule doctor consultations and diagnostic tests; a healthcare provider module configured to manage appointments, patient records, billing, internal laboratory and pharmacy integration, and operational analytics, with the healthcare provider module configured to manage dashboard features for physicians, hospital administrators, and laboratory technicians to monitor and update patient status; a real-time queue management module coupled to the healthcare provider module and the appointment and laboratory test booking module, wherein the real-time queue management module is configured to assign a digital token number to patients upon booking once accepted by the healthcare provider module, wherein the real-time queue management module is configured to update the queue position of patients using time-stamped data; a patient interface configured to allow patients to view real-time queue updates, book appointments, and access lab reports using the digital token number; a notification system configured to send notifications to patients and physicians regarding appointment reminders, queue progress, and follow-up instructions; a cloud-based server configured to securely store patient data and enable seamless communication between the patient interface and the healthcare provider module; a display unit configured to show estimated waiting times and notify patients when it is their turn; and a dynamic scheduling module configured to take into account physician availability, patient preferences, and historical data to minimize waiting times and avoid overbooking.

The invention discloses an outpatient service management method and system based on an HIS system, belongs to the technical field of computers, and achieves precise triage and personalized treatment path recommendation by integrating multi-dimensional state information of a patient and real-time department data and combining a dynamic path planning algorithm. The method has the advantages of improving triage accuracy, optimizing treatment path planning and improving outpatient service management efficiency.

The invention discloses an intelligent service system for advanced cancer pain, which comprises an analgesia device, a massage piece, a driving source and borneol, and is characterized in that the borneol is positioned on the inner side surface of a mounting seat and is used for contacting with a cooling massage ball; the pushing module is used for pushing a predetermined examination quantization table to the patient every day, and the examination quantization table at least comprises one or more of the following items: diet condition, sleep condition, body pain condition, self-cognitive ability and mental state; the evaluation module obtains a patient state evaluation result according to the data of the patient on the examination quantification table, pushes a management scheme to the patient according to the evaluation result, adjusts an analgesia scheme of the analgesia device, and periodically obtains daily living conditions of the patient through a pushing module; and a personalized and centralized pain intervention scheme is formed through the evaluation module, so that on one hand, the life quality of the patient is evaluated in time, and on the other hand, the specific change condition can be conveniently checked through a quantitative mode, and the specific condition of the patient can be improved in a targeted manner.

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.

The disclosed systems and methods assess patient status by automatically extracting features from vascular area traces and generating cardiac health indication parameters based on the extracted features to provide a normalized hyperemia index, thereby providing improved therapeutic effects for heart failure patients. Examples include a diagnostic engine (102) receiving an area trace from a trace generator (104) and communicating patient status information over a user interface (116). The diagnostic engine may include a trace feature detector (106), an index generator (108), a boundary generator (110), an index generator (112), and decision logic (114). The disclosed system may be executed using one or more processing devices or in a networked distributed processing system.

A system and method for processing vital information are provided. The system includes a memory, a processor, and a display. The processor performs following operations: acquiring patient data of a patient; analyzing the patient data to obtain multiple analysis results, wherein the multiple analysis results at least include a first trend of change for first information and a second trend of change for second information extracted from the patient data; determining whether the first trend of change and the second trend of change satisfy a preset condition; determining a patient state of a patient related to the preset condition when the preset condition is satisfied; and displaying indication information which indicates a patient state, the first trend of change and the second trend of change. A correlation between various trend of changes of patient data is taken into consideration in determination of the patient state, which improves the determination accuracy.

The invention discloses an intelligent monitoring system for tumor nursing. The intelligent monitoring system comprises a wearable sensing terminal group, a patient terminal, a cloud platform intelligent analysis center and a medical care terminal. The wearable terminal is used for collecting multi-modal physiological and behavior data such as electroencephalogram, near-infrared brain blood oxygen, heart rate variability, body movement, electrodermal response and gaits; the patient terminal is used for recording subjective symptoms and completing cognition and exercise task evaluation. The cloud platform intelligent analysis center constructs a multi-parameter individualized baseline through data fusion, noise suppression and time alignment, comprehensively analyzes real-time deviation, electroencephalogram abnormality, cognitive deviation and gait change by using a short-term acute early warning model and a long-term trend early warning model, and generates a risk level. The digital twinborn visualization module is used for dynamically displaying the state of the patient, and the medical care terminal performs clinical response based on the graded alarm board. According to the invention, extra-hospital multi-mode continuous monitoring, individualized trend identification and intelligent early warning can be realized, and the real-time performance of follow-up management of brain tumor patients is improved.

The invention discloses a medical data analysis early warning method and system based on inverse reinforcement learning and sequence data dynamic modeling, and the method comprises the following steps: obtaining electronic health record data, and constructing a related data set; according to the constructed data set, a Markov process model is established according to the patient state and the treatment measures, a reward function capable of evaluating the effectiveness of the treatment measures is constructed based on the patient trajectory probability of a trajectory space, and each trajectory in the trajectory space is a state-action pair sequence formed by the patient state and the treatment measures; training the inverse reinforcement learning model by using the data set to obtain an optimal reward function; and obtaining a state prediction value of the current state of the patient after corresponding treatment measures are taken by using the optimal reward function, and if the state prediction value exceeds a set threshold value, carrying out early warning. According to the method, the adaptability to the dynamic change of the patient condition in the medical data analysis and prediction process is improved, and the accuracy of patient state prediction is improved.

The invention relates to the technical field of medical information smart wards, in particular to a smart ward management method and system based on the Internet of Things. Environmental parameters in a smart ward and physiological parameters of a patient in the smart ward are obtained; performing comprehensive analysis according to the environmental parameters and the physiological parameters to obtain corresponding analysis results; and according to an analysis result, a control instruction used for adjusting environment equipment in the smart ward is generated and / or corresponding guidance information is sent to the management end, so that the ward environment and the patient state are combined, and integrity and collaboration are realized. Intelligent management and real-time monitoring and response of the ward are realized through the control instruction and / or guidance information, the timeliness of medical services is improved, dynamic allocation of medical resources is facilitated, the resource utilization efficiency is improved, the operation process is greatly simplified, and the use experience of medical staff and patients is improved.

The invention relates to an intelligent platform management system carrying infusion port patients, and relates to the technical field of data management, and the system comprises a monitoring unit which is used for collecting impedance data, temperature data and medicine use time sequence data; the intelligent analysis unit is used for constructing a dynamic health baseline of the patient, calling a preset machine learning model based on the dynamic health baseline and outputting a complication risk level of the patient; the closed-loop execution unit is used for triggering a three-dimensional life behavior guidance scheme matched with the patient state based on the complication risk level, calculating a corresponding infusion port maintenance period based on the real-time smoothness score, and generating a medicine supply instruction in combination with medicine use time sequence data; and the hierarchical interaction platform is used for generating an early warning view containing the risk level of the patient and the key attention information based on the complication risk level, and pushing maintenance appointment suggestions and planning contents at the patient side based on the maintenance period of the infusion port. The system can improve the management efficiency and safety of the patients in the infusion port.

A method for remote patient monitoring comprises the following steps: receiving medical data from at least one medical device of a patient, determining a patient state and / or patient event at least in part based on the medical data, and determining one or more recipients of a report on the patient state and / or patient event at least in part on the patient state and / or patient event.