52 results about "Early warning score" patented technology

The invention discloses a wearable device and method for intelligent monitoring and early warning of chronic diseases. The device includes a wearable equipment terminal, a user mobile terminal and a cloud server; the wearable equipment terminal transmits collected physiological signals, motion data and location information processed through a data signal processing module to the user mobile terminal through the wireless communication module; the user mobile terminal includes a wireless communication module, a data storage module and a data processing module; the data processing module is internally provided with an evaluation and prediction model, data in the data storage module is called for evaluating and predicting chronic diseases of users through the evaluation and prediction model in real time, and the evaluation and prediction result is sent to a user interaction interface; the cloud server includes a cloud database and an evaluation and prediction model update module, the cloud database is used for storing the data evaluation and prediction result sent from the user mobile terminal, and an updated personalized evaluation and prediction model is sent to the user mobile terminal to update the evaluation and prediction model.

The invention discloses a clinical cerebral infarction patient recurrence risk early warning scoring visual model system. The system comprises an input module, an analysis module and a result output module. The invention also discloses an evaluation method of the clinical cerebral infarction patient recurrence risk early warning scoring visual model system. A clinical cerebral infarction patient recurrence risk evaluation nomogram is concise, popular and easy to understand and convenient for clinicians and patients to operate, and a current cerebral infarction recurrence risk of the patients is predicted. And meanwhile, high-risk, medium-risk and low-risk groups can be clearly distinguished according to patient risk scores calculated by a risk scoring formula in the model so that the clinicians are assisted to formulate an efficient treatment scheme. In the invention, a doctor and the patient are guided to evaluate early warning scores of the clinical cerebral infarction patient recurrence risk in combination with a statistical method, and patient data acquisition is verified through a corresponding mathematical statistical method so that the doctor and the patient can quickly grasp the clinical cerebral infarction patient recurrence risk and provide early warning information.

The invention discloses a portable adjustable head-mounted epilepsy monitor. The monitor comprises: an electroencephalogram cap which is used for collecting EEG signals of a patient; a debugging module which is used for processing the EEG signals acquired by all the electrodes on the electroencephalogram cap during the seizure period, the pre-seizure period and the post-seizure period of epilepsyof the patient so as to determine the most suitable electrode; a training module which is used for carrying out model training according to the EEG signals collected by the most suitable electrode; amonitoring module which is used for receiving EEG signals and performing detection by using the model trained by the training module so as to judge whether the patient is currently in the early stageof epilepsy; and an early warning module which is used for carrying out early warning when the monitoring module monitors that the patient is in the early stage of epilepsy. The monitor can realize portable monitoring and immediate early warning, and effectively prevent secondary injury to the patient during epileptic seizure.

The embodiment of the invention discloses a vehicle driving risk management and control method based on artificial intelligence and related equipment thereof, and relates to the technical field of vehicle safe driving. The method comprises the following steps: receiving a video stream about a target driving user, and obtaining driving scene data; extracting face features in the video stream, and calculating a numerical value of the first driving risk score according to the face features; calculating a numerical value of an abnormal behavior score in the video stream, and determining a numerical value of a second driving risk score; calculating an early warning score based on the abnormal behavior score, the first driving risk score and the second driving risk score; and determining an alarm result, and giving an alarm to the target driving user. According to the method, during driving risk assessment, abnormal behavior detection and a face recognition technology are combined, the assessment of the driving risk is more comprehensive and scientific, the accuracy of early warning is improved, the driving risk is judged in real time through fusion with full-scene data, and the accuracyand richness of the assessment of the driving risk are further improved.

The invention discloses a method for establishing an early warning model of major cardiovascular disease risk. The method comprises the following steps: collecting heart data in real time and saving the data to a heart database; copying the data in the heart data into a training database and a test database; establishing a cardiovascular disease model and performing a training optimization model through data in the training database until a training number and a training accuracy threshold are reached; testing the training-optimized cardiovascular disease model by data in the test database until the number of tests and the test accuracy threshold are reached; outputting the cardiovascular disease model after the test is passed, and obtaining the final warning model for major cardiovasculardisease risk. The method establishes the early warning model for cardiovascular disease risk, intelligently analyzes and predicts cardiovascular diseases, and solves the technical problem in the prior art that the disease cannot be intelligently analyzed based on cardiac data, and realizes the pressure of relieving medical resources.

The invention discloses an image-based online public opinion early warning system and a method, and the method comprises the steps that an information obtaining module obtains the theme information and comment information of each topic in a network community, transmits the theme information to a classification and recognition module, and transmits the comment information to an expression analysismodule; the classification and identification module classifies the images in the theme information to obtain image categories, extracts characters in the images, and sends the image categories, the characters in the images and the characters in the theme information to an early warning and scoring module; the expression analysis module analyzes the pictures in the comment information to obtain apublic opinion analysis result of the comment information and sends the public opinion analysis result to the public opinion early warning module; the early warning and scoring module determines a public opinion score according to the image category, the characters in the image and the characters in the theme information and sends the public opinion score to the public opinion early warning module; and the public opinion early warning module determines an early warning score according to the public opinion analysis result and the public opinion score, and sends out early warning. According tothe system and the method, the public opinion early warning corresponding to the public opinion in the image can be published accurately and quickly.

The invention relates to a system and method for prediction and warning of sudden cardiac arrest. The system comprises an intelligent terminal and a sudden cardiac arrest prediction bracelet, wherein the intelligent terminal is in communication connection with the sudden cardiac arrest prediction bracelet; the sudden cardiac arrest prediction bracelet is used to acquire a current heart rate of a user and compare the heart rate with a preset referential heart rate, so an emergency warning signal can be generated when the difference between the current heart rate and the preset referential heart rate exceeds a preset difference and the situation lasts for preset time; and the intelligent terminal is used to call an emergency center and designated contact persons and send an acquired current position to the emergency center while receiving the emergency warning signal. The system and method provided by the invention have the advantages that wearing is conveniently; the warning can be conducted at soonest; and the user can be reminded to take medicines in time, and families or surround people can be reminded to rescue the user in time, so more rescue time is provided for the user.

A patient monitor (12) includes a display (14) and sensors (20, 22, 24) reading vital signs of a human subject. In a cardiovascular early warning scoring (cEWS) method, the human subject is classified using a plurality of cardiovascular deterioration classifiers (52, 152, 54, 56, 58) each trained respective to a different type of cardiovascular deterioration. The cardiovascular deterioration classifiers operate on a set of inputs characterizing the human subject including the at least one cardiovascular parameter (42) and the at least one respiratory parameter (44), such as tidal volume read by an airflow sensor (24). The cardiovascular early warning scores for the different types of cardiovascular deterioration are outputted on the display of the patient monitor. An empirical myocardial ischemia classifier (52) may be combined with at least one additional ischemia score generated by applying a set of rules (160) or a physiological model (162).

The invention discloses an early warning system for ruptured artery hemangioma and an early warning method thereof. According to the invention, an ultrathin flexible sensor, ultrathin flexible temperature sensors and an ultrathin flexible strain sensor are implanted into the body through minimally invasive surgery; the strain sensor is wound and attached to the position of the hemangioma; the temperature sensors are wound and attached to the equidistant positions upstream and downstream the hemangioma; the early warning system has ultrahigh biological compatibility and will not cause internal rejection reaction; the sensors are directly and tightly attached to the measured positions and can be used for timely and accurately measuring the measured parameters; the ultrathin flexible characteristics of the sensors will not cause new focus by resulting in external damage to blood vessels and tissues; the signal is transmitted to a mobile terminal in the manner of near field communication; a microprocessor in the mobile terminal has an alarming function; when a deforming value or local blood pressure value of the hemangioma exceeds a threshold value, the microprocessor can alarm, so that the early warning system can conveniently monitor and measure early warning for a long time.

A computer network includes computing devices and a server system having an electronic health records (EHR) application. The server system includes a logic module accessible by the EHR application and providing logic by which an early warning score for a patient is calculated. The score is calculated based on, at least in part, data from an electronic patient record that is accessed by the EHR application. The EHR application communicates the calculated early warning score to a networked device, which in turn displays the calculated early warning score on a screen for view by a user. The same networked device preferably is used to make one or more observations of the patient that are recorded in the electronic patient record, and based on which the score is calculated. Whether the score is increasing, decreasing, or remaining the same over observation intervals preferably is determined and displayed with the score.

The invention relates to a living body diagnosis or early warning device which comprises a sensor device, a signal processing device and the like. The signal processing device is suitable for detecting abnormal pulse waveforms generated by the sensor device and determining information data related to the abnormal pulse waveforms according to the abnormal pulse waveforms. The living body diagnosis or early warning device comprises a watch type diagnosis or early warning device, a handheld type diagnosis or early warning device and the like.

The embodiment of the invention belongs to the field of big data, and relates to a visual group complaint risk identification method, which comprises the steps of obtaining all associated users havingan association relationship with a target user, and constructing an association graph between the target user taking the target user as a center and all the associated users, and scoring the target user and all the associated users according to a preset scoring rule, reconstructing an associated graph according to a scoring result, performing group complaint risk early warning scoring on the reconstructed associated graph to obtain a group complaint risk early warning scoring result, and displaying the group complaint risk early warning scoring result in a visual display area. The invention further provides a visual group complaint risk identification device, computer equipment and a storage medium. In addition, the invention also relates to a blockchain technology, and the association relationship can be stored in the blockchain. According to the method, accurate positioning of the user group complaint risk can be realized, and timely prediction and discovery of the group complaint risk are facilitated.

The invention discloses an acute hypotension hybrid early warning method based on an LSTM network. The method comprises the following steps: firstly, a collected physiological data sequence is imported; hypotension event detection is carried out; if a patient is in a hypotension state at present, a result is output for hypotension early warning, if the patient is not in the hypotension state at present, predicting of an acute hypotension event is carried out, if the acute hypotension is predicted to occur in a next time period, the result is output for hypotension prediction early warning, then a blood pressure protection value is detected, and if the blood pressure exceeds the protection value, returning to two results is carried out for early warning. The prediction and early warning canbe carried out on acute hypotension, so that time is bought for monitoring personnel, the safety of the patient during rehabilitation training is guaranteed, and cardiac exercise rehabilitation becomes more scientific, efficient and safer.

The invention provides a patient consciousness disturbance degree monitoring and early warning system and method. The method comprises the steps that through the contact between electro encephalography electrodes and the human body, the electro encephalography signals of the human body are collected and subjected to filtering processing and digital processing respectively to obtain monitoring data; the monitoring data is saved, the saved monitoring data is read, and the monitoring data is subjected to analytical processing to obtain various data quantized values; an early warning threshold is set, and an alarm is started when the data quantized values exceed the early warning threshold. The patient consciousness disturbance degree monitoring and early warning system and method have the advantages that through continuous electro encephalography signal monitoring and the quantitative analysis indexes of an energy spectrum and nonlinear dynamics, medical staff can know the changes of the patient consciousness disturbance degree in real time for a long time, the defects of a traditional medical consciousness disturbance degree assessment scale is made up for, and long-term and continuous monitoring and early warning for a patient with consciousness disturbance are emphasized so that the medical staff can adjust the therapeutic schedule according to the changes of the patient consciousness disturbance degree in time, thereby truly improving the condition of the patient.

The invention belongs to the technical field of medical treatment, and discloses a cardiovascular disease risk control method based on big data. A cardiovascular disease risk control method system based on the big data comprises a heart rate detecting module, a blood pressure detection module, a blood flow velocity detecting module, a single chip microcomputer control module, a cloud service module, a parameter calculation module, an early warning module, a data storage module, a displaying module, a real-time analyzing module, a predicating model module, a data managing module and an analysisresult reporting module. The analyzing and calculation speed of cardiovascular disease risk data can be greatly improved through the cloud service module, and the detecting efficiency is improved; arterial pressure waveform data of a subject is provided and analyzed by the parameter calculation module, and therefore it is determined whether or not the subject experiences an abnormal state. A multivariate statistic model is applied to the arterial pressure waveform data, and therefore cardiovascular parameters of the subject are determined.

A method for providing an early warning system for a patient includes: allowing a caregiver to select among a plurality of early warning score protocols; allowing the caregiver to modify the selected early warning score protocol; automatically calculating an early warning score for the patient; and displaying the early warning score for the caregiver. A medical device includes at least one health care equipment module configured to measure a physiological parameter of a patient. The medical device is configured to allow the caregiver to select among a plurality of early warning score protocols. The early warning score protocols may be targeted to specific patients based on demographic information. The medical device also allows the caregiver to modify the selected early warning score protocol. The medical device also automatically calculates an early warning score for the patient using a physiological parameter captured by the at least one health care equipment module.

The invention relates to the technical field of computer information, and discloses a fraud risk early warning method, device and equipment, and the method comprises the steps: carrying out the scoring of a target customer from at least two dimensions based on the obtained credit data of the target customer, and obtaining a fraud early warning score of the target customer, the dimension is determined and obtained based on at least one of the following dimension determination basis: basic information of the customer, credit information of the customer and bank behavior information of the customer; and displaying and outputting a fraud risk early warning result table of the target customer based on the fraud early warning score of the target customer. According to the method and the device, the technical problem of relatively low prediction accuracy caused by predicting the fraud risk of the customer by using basic data provided by the customer and subjective evaluation of a bank customer manager in the prior art is solved, and the technical effect of predicting the current fraud risk of the customer from multiple dimensions is realized; the error intervention caused by human subjective evaluation is avoided, and the prediction accuracy is improved.

The invention provides a clinical body temperature monitoring system, which comprises a fixed terminal, at least one thermometer and a mobile terminal, wherein the at least one thermometer is coupled with the mobile terminal in a communication way, and is used for collecting body temperature data of a user and transmitting the body temperature data of the user to the mobile terminal; and the mobile terminal transmits the body temperature data to the first fixed terminal through a local area network. The body temperature data collected by the thermometer can be transmitted to the mobile terminal in real time, so that a doctor / a nurse can know the body temperature change condition of a patient in time; and if the mobile terminal is arranged in a sickroom / at a sickbed, the convenience, the speed, the accuracy and the real-time performance of the system during the showing of the body temperature information of the patient to a sickroom inspection doctor / nurse can be improved. The temperature data collected by the thermometer can be transmitted to the first fixed terminal in real time; and if the first fixed terminal is arranged in a nurse desk, the system can show the body temperature information of the patient in a sickroom to the doctor / the nurse at the nurse desk, so that the doctor / the nurse can conveniently and timely master the body temperature change of the patient.

The invention discloses an early warning system for a chronic renal failure, and belongs to the field of medical detection. The early warning system for the chronic renal failure comprises a data acquisition unit used for collecting the urine volume, urine creatinine and serum creatinine of a patient to generate detection values, a calculation unit used for calculating creatinine clearance rates according to the detection values, a prediction unit used for establishing a Markov-grey model, and calculating predicted values of the creatinine clearance rates in future time periods according to the N creatinine clearance rates calculated through the detection values of N detection time periods, wherein N is an integer greater than 3, and an early warning decision unit used for determining the early warning level of the chronic renal failure according to the predicted values of the creatinine clearance rates. According to the invention, modeling analysis is carried out on the creatinine clearance rates to predict the probability that a kidney has the chronic renal failure and timely discover non-overt renal injuries in early kidney asymptomatic lesions so as to provide effective help in clinical early confirmed diagnosis and later treatment.

The invention discloses an early warning method and an early warning system. The early warning method includes: acquiring the excited state data of a pet, the excited state data including physiological excited data and / or action excited data, and combining the excited state data with a preset The excited state model is matched, and if the excited state data is successfully matched with the excited state model, it is determined that the pet is in an excited state. The technical solution provided by the invention matches the excited state data of the pet with the preset excited state model, and identifies whether the pet is in the excited state according to the matching result, thereby improving the accuracy and timeliness of identifying the pet's harmful behavior, and finally reminding people to Avoid pet hazards.

The invention relates to an early warning method and early warning device for loss of an identity document. The early warning method comprises the following steps of collecting identity information; encrypting the collected identity information, so as to obtain encrypted identity information; pushing the encrypted identity information and identity document loss information; receiving the encrypted identity information and the identity document loss information; decrypting the identity information so as to obtain original identity information; and performing matching verification on the original identity information by using an identity information database in a third-party server, and if matching succeeds, sending notifications of identity document loss and business cessation. The method and device can help warn loss of the identity document in advance and push identity document loss information to related business handling department rapidly, so as to prevent identity information from being utilized for cheating.

An example mobile computing device can include: an engine programmed to receive one or more sources of patient data that provide patient identification information, vital signs information, alert information, and task information; and a display of the mobile device that renders a screen has: a first window having a listing of patients assigned to a caregiver; a second window having a plurality of vital signs and an early warning score associated with a patient of the caregiver.

The invention discloses a factory production state abnormity detection model design method. The method comprises the following steps: A, constructing a support vector domain; B, designing a nonlineargeneralized kernel function support; C, setting early warning scores; D, evulating a model. A factory overall condition monitoring system can be established, production condition real-time monitoringabnormity monitoring and early warning can be achieved, more convenient refined management and decision-making bases are provided for relevant subjects, and the method is beneficial to combination ofbig data and artificial intelligence in the industrial manufacturing industry and has very important significance for enterprise development and national development.

The invention relates to an artificial intelligence technology, and discloses an intelligent monitoring and early warning method for a logistics sorting robot, and the method comprises the steps: constructing an information portrait of an article according to the article information and the article type of the logistics article; dividing the information portrait into a plurality of portrait data blocks; extracting data features of the portrait data blocks and constructing an article feature matrix; constructing a decision tree according to the multi-level classification table, and performing classification decision on the article feature matrix by using the decision tree to obtain a sorting category; calculating a single deviation between the sorting category corresponding to the logistics articles and the article category and an average deviation of all the articles; obtaining a decision logic path of the logistics article of which the single deviation is greater than a preset deviation threshold, and calculating a path error according to the decision logic path; and calculating an early warning score according to the average deviation and the path error, and performing sorting error early warning when the early warning score is greater than a preset value. The invention further provides an intelligent monitoring and early warning device and equipment for the logistics sorting robot and a medium. The logistics product sorting early warning accuracy can be improved.

Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing intelligent touch care. More particularly, embodiments of the present disclosure utilizes contributing data elements in a community early warning score (CEWS) to predict touchpoint discipline and to recommend frequency, modality, and upstream transitions of care and outside service referrals for a patient in a community care setting. In response to a touch point (e.g., a change in condition or medication, a scheduled or unscheduled appointment, or a patient question), the CEWS can be utilized along with data in an EHR of a patient, data from a patient device, or data from a patient portal to initiate a follow-up (e.g., personal health question or encounter). Notification tools and scheduling functionality are provided via a user interface of the patient device to improve clinical workforce capacity, increase the number of patient touches, and encourage heightened patient engagement.

The invention discloses a monitoring and early warning method for buckling failure precursor information of a filling body. The method is used for determining a monitoring area of an on-site filling body and carrying out numbering; a borehole stressmeter and a pin type electrode are utilized for monitoring the stress and the resistivity value of the filling body in real time, and then the stress and the resistivity value are converted into the stress change rate and the resistivity change rate, the change tendency of the change rates is grasped, and the buckling failure of the filling body is precisely and early warned. The method is applied to the fields of mine cut and fill mining, consolidation treatment of tailings, soil slopes, mine refuse dumps and the like, and is easy to implement, high in reliability, low in cost, good in effect and capable of determining the stability state of the on-site filling body, the lime and sand ratio can be adjusted based on the circumstances, and the filling cost can be obviously reduced.

The invention discloses a business data monitoring component applied to an enterprise service bus. The component comprises an enterprise service bus of a hospital information integration platform, theenterprise service bus comprises a service access port and a service execution mechanism, and the service access port is connected with the service execution mechanism. The enterprise service bus also comprises a service data monitoring and processing interface, and the service data monitoring and processing interface is connected with the service access port. The invention further provides a business data monitoring method applied to an enterprise service bus. The business data monitoring method comprises the following steps of: (1) a patient critical value information transmission method; (2) a nursing improvement early warning scoring method; and (3) a hospital physical examination closed-loop method. Business data monitoring and service operation of an enterprise service bus are conducted in parallel, a hospital information system is actively driven to process business data in time, the treatment process in emergency can be optimized, and the emergency medical capacity is improved.

Embodiments of the present disclosure relate to systems, methods, and user interfaces for providing intelligent touch care. More particularly, embodiments of the present disclosure utilizes contributing data elements in a community early warning score (CEWS) to predict touchpoint discipline and to recommend frequency, modality, and upstream transitions of care and outside service referrals for a patient in a community care setting. In response to a touch point (e.g., a change in condition or medication, a scheduled or unscheduled appointment, or a patient question), the CEWS can be utilized along with data in an EHR of a patient, data from a patient device, or data from a patient portal to initiate a follow-up (e.g., personal health question or encounter). Notification tools and scheduling functionality are provided via a user interface of the patient device to improve clinical workforce capacity, increase the number of patient touches, and encourage heightened patient engagement.

A patient monitor (12) includes a display (14) and sensors (20, 22, 24) for reading vital signs of a human subject. In the Cardiovascular Early Warning Score (cEWS) method, human subjects are classified using multiple cardiovascular worsening classifiers (52, 152, 54, 56, 58), each with respect to a different types of cardiovascular deterioration while being trained. The cardiovascular deterioration classifier operates on a set of inputs characterizing the human subject, the set of inputs including at least one cardiovascular parameter (42) and at least A breathing parameter (44). A cardiovascular early warning score for different types of cardiovascular deterioration is output on the display of the patient monitor. The empirical myocardial ischemia classifier (52) can be combined with at least one additional ischemia score generated by applying a set of rules (160) or a physiological model (162).