33 results about "Symptom monitoring" patented technology

The invention discloses a hospital-based infectious disease symptom data acquisition system. The hospital-based infectious disease symptom data acquisition system comprises a symptom monitoring data base, a hospital information acquisition module sending monitored data to the symptom monitoring data base, a medical record information acquisition module and a clinic medical record acquisition module, wherein the hospital information acquisition module is arranged in an external hospital information system and used for acquiring target symptom information to be monitored in the hospital information system within a cycle; the medical record information acquisition module is arranged in an external hospital electronic medical record system and used for acquiring target symptom information to be monitored in electronic medical records within a cycle; and the clinic medical record acquisition module is arranged in an external hospital out-patient department and used for acquiring target symptom information to be monitored in clinic medical records within a cycle. The hospital-based infectious disease symptom data acquisition system can provide a technical example for applying a symptom monitoring early-warning technology to the health and safety guarantee of large-scale crowd-gathered activities in the future.

Monitoring and analysis of a user's speech to detect symptoms of a mental health disorder by continuously monitoring a user's speech in real-time to generate audio data based, transcribing the audio data to text and analyzing the text of the audio data to determine a sentiment of the audio data is disclosed. A trained machine learning model may be applied to correlate the text and the determined sentiment to clinical information associated with symptoms of a mental health disorder to determine whether the symptoms are a symptom event. The initial determination may be transmitted to a second device to determine (and / or verify) whether or not the symptom event was falsely recognized. The trained machine learning model may be updated based on a response from the second device.

Described here is a deep brain stimulation (“DBS”) approach that targets several relevant nodes within brain circuitry, while monitoring multiple symptoms for efficacy. This approach to multi-symptom monitoring and stimulation therapy may be used as an extra stimulation setting in extant DBS devices, particularly those equipped for both stimulation and sensing. The therapeutic efficacy of DBS devices is extended by optimizing them for multiple symptoms (such as sleep disturbance in addition to movement disorders), thus increasing quality of life for patients.

The invention provides a portable Parkinson's disease motor retardation monitoring and intervening device and a method. The portable Parkinson's disease motor retardation monitoring and intervening device comprises a wearable intelligent device, an intelligent interaction device, a motion intervention device, a network connection device and an information recording device. The wearable intelligentdevice is composed of a wearable inertia node and an intelligent bracelet; motion data of a patient is collected through an inertial sensor, and motion information data is integrated through the intelligent bracelet and sent to the intelligent interaction device. The intelligent interaction device is used for collecting data of completing an interaction task by a user and extracting characteristic values from all the data to input into an evaluation model for evaluation analysis, obtaining evaluation results and displaying the evaluation results; the motion intervention device is used for performing motion intervention correction on the 'intervention required' state, and the intelligent interaction device is used for further transmitting the evaluation result to the information recordingdevice through the network connection device so as to record the staged monitoring result in combination with the personal information of the user and store the staged monitoring result into a database. The portable Parkinson's disease motor retardation monitoring and intervening device and the method can be applied to Parkinson's disease patient symptom monitoring and curative effect evaluation.

The invention relates to a motion symptom monitoring device. The motion symptom monitoring device comprises a measurement module and a monitoring terminal, wherein the measurement module is arranged on a measured part of a measured user; the monitoring terminal is owned by the measured user; the monitoring terminal wirelessly communicates with the measurement module; the measurement module measures motion signals generated when the measured user moves and sends the motion signals to the monitoring terminal; the monitoring terminal identifies various signal peak values of the motion signals andthe peak value distance between each signal peak value and the signal peak value next the signal peak value, and monitors the motion symptoms of the measured user according to the signal peak valuesand the peak value distances. According to the motion symptom monitoring device, in the motion symptom measuring process, the signal peak values of the motion signals and the corresponding peak valuedistances serve as a reference, the influence of the motion signal amplitude noise on the motion symptom monitoring process can be reduced, and the effect of correspondingly monitoring the motion symptoms of the measured user is improved.

The invention belongs to the technical field of mouse cerebral ischemia reperfusion models and discloses a mouse cerebral ischemia reperfusion model optimization and failure assessment method. A mousecerebral ischemia reperfusion model optimization and failure assessment system comprises a video monitoring module, a time module, a master control module, an anesthesia module, a sterilization module, a cutting module, an insertion module, a pulling-out module, a cerebral blood vessel three-dimensional structure module, a symptom monitoring module and a display module. By the cerebral blood vessel three-dimensional structure module for displaying cerebral blood vessel conditions after mouse cerebral ischemia reperfusion, effectiveness in division of cerebral blood vessel thick branches can be achieved, and cerebral blood vessel tiny structures can be extracted accurately. In addition, by intraoperative monitoring of vital signs including the body temperature, pulse, breath and blood pressure, the mouse survival rate is increased; by the postsurgical symptom monitoring module, quantitative assessment of neurological function damages after mouse cerebral ischemia reperfusion can be given, and important significance to cerebral arterial thrombosis prevention, curative effect assessment and the like is achieved.

The embodiment of the invention provides an infectious disease symptom monitoring and early warning method and system based on big data, and the method comprises the following steps that an early warning place which needs to be monitored is selected, and the disease type which needs to be early warned in the early warning place is determined; disease information in the early warning site is collected, different disease information is processed, and when it is determined that an epidemic situation event occurs in the early warning site, an early warning condition is sent to a server for processing; and a server further judges whether early warning is carried out or not according to a preset early warning condition and an early warning threshold value, and after the early warning for the first time is completed, the server returns two groups of early warning results and returns data information needing to be displayed on the map to the client browser to load the map. According to the embodiment of the invention, the disease distribution information can be visually displayed on a map, the possible spreading range of pathogens can be determined, and the geographical condition and the crowd condition of the epidemic situation of infectious diseases and the spreading condition of the epidemic situation can be well comprehensively analyzed.

The invention discloses a syndromic surveillance early warning method based on a symptom proportion R value. The syndromic surveillance early warning method comprises the following steps of acquiring diagnosis and treatment data of a hospital; screening to-be-analyzed hospital data; determining a to-be-analyzed target disease and a corresponding target symptom thereof by adopting a Delphi method and a user-defined diagnosis name searching method; dividing the to-be-analyzed hospital data into symptomatic field data and asymptomatic field data according to the condition whether a symptomatic field is empty or not; counting data volume according with target symptoms in the symptomatic field data, and extracting symptom characteristics according with the target disease; solving a symptom proportion R value matrix by taking disease entities and a time period as dimensionalities according to the symptom characteristics according with the target disease; solving the case number of the target disease from the symptomatic field data and the asymptomatic field data according to the symptom proportion R value matrix; performing modeling analysis and early warning on the target disease according to the case number of the target disease. The syndromic surveillance early warning method disclosed by the invention is high in efficiency, high in generality and good in transportability, and can be widely applied to the field of disease monitoring.

The invention provides a built-in test method and system for an electromechanical hybrid system. The method comprises the steps that a fault symptom space matrix corresponding to the electromechanical hybrid system is established, each row of data of the matrix is used for representing whether output data of all monitoring points of the electromechanical hybrid system are abnormal after a corresponding LRU has a fault, each column of the matrix represents a monitoring point, and different rows of the matrix correspond to different LRUs forming the electromechanical hybrid system; after the electromechanical hybrid system runs, output data of all the monitoring points are obtained, a symptom monitoring data set is established according to the output data of all the monitoring points, and all data in the data set are used for representing whether the output data of different monitoring points are abnormal or not; when abnormal data exists in the data set, fault diagnosis is carried out based on the matrix and the data set, and the diagnosed fault type comprises at least one of a determined fault and an undetermined fault; and when the diagnosed faults include undetermined faults, fault diagnosis is continued until a determined fault is obtained.