126 results about "Patient behavior" patented technology

The invention discloses an oral diagnosis and treatment patient service platform based on reinforcement learning, and relates to the technical field of diagnosis and treatment services, and the platform comprises a health scoring module which integrates multi-source data, generates personalized oral health scores through a dynamic weight distribution model, and displays high-risk items in combination with 3D visualization; the patient management and marketing module tracks patient behaviors, clusters and classifies the patient behaviors, generates a dynamic follow-up visit path by using multi-target reinforcement learning, and matches a precise marketing verbal skill through a knowledge graph; the in-diagnosis auxiliary module synchronizes health data of the patient to the doctor terminal in real time, detects prescription conflicts and performs early warning, and generates personalized communication verbal skills in combination with historical preferences of the patient and real-time emotion analysis; through multi-dimensional data integration and intelligent processing, the whole oral diagnosis and treatment process is optimized, the diagnosis and treatment efficiency and the patient satisfaction degree are improved, the operation cost is reduced, and remarkable innovativeness and practicability are achieved.

The invention discloses an intelligent hospital guide and tracking return visit recording integrated system for medical examination, which relates to the technical field of intelligent medical treatment, and comprises the following steps: based on a comprehensive data set, using an A-star algorithm to identify a hospital guide path; in the process that the patient waits for the hospital according to the hospital guide path, the system collects behavior data of the patient in real time; based on historical data, training a waiting time difference prediction model, inputting the patient behavior data into the waiting time difference prediction model, and predicting a waiting time deviation value; dynamically adjusting a hospital guide path according to the waiting time deviation value; and after the patient finishes visiting according to the adjusted hospital guide path, the system automatically provides health propaganda and education contents, collects patient feedback data and stores the patient feedback data in the tracking return visit system. Through the A-star algorithm and the waiting time prediction model, the system can perceive waiting delay in advance and adjust a path in time, the waiting time of a patient is remarkably shortened, and the doctor-seeing experience is improved.

The invention relates to an integrated management system for diabetic patients, which comprises the following steps of: constructing a high-dimensional metabolic data model, performing multi-modal fusion on genome information, metabolome data and life behavior data, and mining a potential mode of metabolic characteristics of the patients; carrying out patient typing by utilizing a clustering algorithm comprising a self-organizing mapping network, and identifying different metabolism types comprising an insulin resistance type and an insulin secretion insufficiency type; developing a hierarchical management strategy based on a typing result, wherein the hierarchical management strategy comprises diet, exercise and medicine intervention schemes for different types; collecting dynamic quantitative indexes of the metabolic load, and integrating the blood glucose fluctuation amplitude, insulin sensitivity and daily nutrition intake of the patient; constructing a metabolic load prediction model, and combining a time sequence deep learning algorithm with real-time data, including blood glucose, diet and exercise, of a patient to measure a future metabolic load; and establishing a causal relationship model between patient behaviors and metabolic reactions, and analyzing an interaction mode of diet, exercise and metabolic loads by adopting a causal inference technology.

The invention relates to the technical field of medical care informatics, in particular to a diabetic clinical data management system and method based on feature extraction, and the method comprises the steps: obtaining patient clinical data of a diabetic, the patient clinical data including patient blood glucose data and patient behavior data; behavior matching features among different behaviors of the diabetic patient are extracted through the patient behavior data; extracting correlation characteristics of different behaviors of the diabetic patient and blood glucose fluctuation through the patient blood glucose data and the patient behavior data; evaluating the blood glucose fluctuation risk according to the behavior matching features and the associated features of the diabetic patient, and performing blood glucose fluctuation risk early warning according to a blood glucose fluctuation risk evaluation result. According to the method, the accuracy and the reliability of blood glucose fluctuation risk early warning are effectively improved by quantifying the behavior matching degree among the dining behavior, the medication behavior and the movement behavior of the diabetic patient.

The invention relates to the technical field of medical nursing, in particular to an analysis system based on a data model in the field of neurology nursing. The system comprises a neurology nursing behavior characteristic analysis module, a neurology nursing risk assessment module, a nursing demand and characteristic change rule analysis module and a neurology nursing decision support module. Neurology patient behavior dimension data and neurology nursing record dimension data can be obtained to carry out illness state nursing behavior feature analysis and neurology nursing risk assessment calculation, and a neurology illness state nursing risk assessment value is obtained; the neurology patient nursing demand is obtained, feature change rule mining analysis is carried out, meanwhile, nursing decision support analysis is carried out on the corresponding neurology patient nursing process, a neurology patient nursing decision support suggestion scheme is generated, and corresponding neurology patient nursing decision intervention measures are carried out. The nursing efficiency can be remarkably improved.

The invention provides a big-data-based Alzheimer's disease old-age care platform management method and system, and relates to the technical field of old-age care platform management.The method comprises the steps that patient behavior data information is received, missing data points are deduced according to pre-stored patient behavior file historical behavior pattern data and patient behavior data information, and the missing data points are sent to a server; by evaluating the reliability degree of the event chain and comparing the event chain with a dynamically generated reliability evaluation threshold value, when a comparison result reaches an early warning standard, early warning information with a reliability quantitative index is generated, and the problem that an existing platform is poor in reliability under a non-ideal operation condition is effectively solved. The problems of reliable transmission and accurate interpretation of key information are solved, and early warning failure caused by data deviation or missing is avoided, so that the nursing quality and safety of the Alzheimer's disease patient are remarkably improved, and the optimal time for early intervention and intervention is provided for nursing personnel.

The invention belongs to the technical field of medical data processing, and relates to a urinary surgery diagnosis and treatment data intelligent processing method and system, and the system comprises a multi-modal data collection module which is used for collecting basic diagnosis and treatment data, behavior data and diagnosis and treatment process data of a patient; the diagnosis and treatment data intelligent analysis module is used for calculating the intention value of the patient for the diagnosis and treatment item and generating a diagnosis and treatment path diagram; the intelligent decision support module is used for generating personalized diagnosis and treatment suggestions in combination with a guide and a model; the doctor-patient interaction feedback module is used for collecting patient feedback and updating an analysis weight; and the follow-up management module is used for generating a follow-up plan and relapse early warning. Basic diagnosis and treatment data and patient behavior data are fused, and diagnosis and treatment requirements are reflected more comprehensively; the dimension weight is adjusted and analyzed in real time through patient feedback, and the personalized diagnosis and treatment precision is improved; a guide rule and a machine learning model are combined to assist a doctor to quickly generate an optimal treatment scheme; through the block chain and privacy protection technology, compliance use of the medical data is ensured.

The invention discloses an Alzheimer's disease rehabilitation assistance system based on artificial intelligence. The Alzheimer's disease rehabilitation assistance system comprises a data collection module, a cognitive evaluation module, a patient classification module, a rehabilitation planning module and a comprehensive assistance module. The invention belongs to the technical field of Alzheimer's disease rehabilitation management, and particularly relates to an Alzheimer's disease rehabilitation auxiliary system based on artificial intelligence, which realizes cognitive risk level judgment by acquiring multi-modal data of voice, writing, behavior and nursing evaluation, extracting structural features and constructing a cognitive evaluation method fusing rules and a lightweight model; performing multi-dimensional symptom grading in combination with patient behaviors and living abilities, screening personalized intervention tasks through an adaptive scoring mechanism, and generating a dynamic rehabilitation path and plan; closed-loop linkage among cognitive assessment, hierarchical classification and rehabilitation intervention is realized, and the accuracy and practicability of rehabilitation assistance are improved.

The invention relates to the technical field of medical informatization, and discloses a team construction method and system for whole course case management, and the method comprises the steps: integrating medical data and non-medical Internet of Things data through a cross-domain data fusion engine to generate a patient behavior map, carrying out the reverse deduction of wearable equipment data as an inducement based on a reverse reasoning algorithm, and triggering hierarchical intervention, detachable combination and dynamic pricing of points are realized by using a block chain smart contract; according to the system, the data island limitation of traditional medical monitoring is avoided, the risk inducement can be reversely locked before the physiological indexes are abnormal through the implicit association between the smart home power utilization frequency and the medicine use record, accurate early warning is achieved, and the safety of the system is improved through multi-modal data collaborative analysis and deep coupling of sandbox rehearsal decision and the smart contract. And the technical jump from passive treatment to active health management is realized.

The invention provides a multi-mode man-machine cooperation airway evaluation method and system based on AI, and relates to the technical field of medical data processing. The method comprises the following steps: collecting multi-modal medical record data of at least one patient; the multi-modal medical record data comprises medical text data, medical audio data, medical image data, patient facial expression data, patient physiological data and patient behavior data; a preset AI airway analysis model is used, airway threat information influencing airway safety of the patient is obtained through analysis on the basis of the multi-modal medical record data, and an airway diagnosis and treatment scheme matched with the patient is obtained through evaluation; wherein the airway threat information comprises airway physiological threat information and airway psychological threat information; the airway diagnosis and treatment scheme comprises an airway physiological diagnosis and treatment scheme and an airway psychological diagnosis and treatment scheme. The physiological and psychological states of the patient can be evaluated, and while the airway physiological diagnosis and treatment scheme is provided, the matched airway psychological diagnosis and treatment scheme can be provided, so that the patient is helped to overcome psychological obstacles in the airway diagnosis and treatment process.

A vision screening device for administering vision screening tests to a patient, to determine the presence of diseases and / or abnormalities in the eye(s) of the patient, is described herein. The vision screening device may include associated methods and systems configured to perform the operations of the vision screening tests. The device may include a radiation source configured to generate near-infrared (NIR) radiation, a sensor configured to capture a grayscale image representing the radiation reflected by the eye(s) of the patient, a white light source, and a camera configured to capture a color image of the eye of the patient. The device may also be configured to generate a behavior likelihood score for a patient based on the captured data, the score indicative of a likelihood of particular behavior (e.g., violence, outbursts, withdrawal symptoms, etc.). The device may then recommend action to prepare for such behavior, if necessary.

The invention discloses a patient behavior monitoring method and system based on big data analysis. The big data analysis-based patient behavior monitoring method comprises the following steps: S110, collecting physiological feature data and behavior feature data of a patient; s120, preprocessing the collected physiological feature data and behavior feature data; s130, calculating a physiological feature abnormal value according to the preprocessed physiological feature data; s140, performing behavior pattern analysis according to the preprocessed behavior feature data and the physiological feature abnormal value; through real-time and comprehensive monitoring and analysis, a patient can better manage own health, and the quality and efficiency of medical services are greatly improved on the whole.

The invention discloses a quantitative early diagnosis system for Parkinson's disease based on behavior feature recognition, and belongs to the technical field of Parkinson's disease diagnosis, and the system comprises an intelligent collection device which is used for collecting motion data, voice data, writing data and face data of a patient, determining real-time behavior data of the patient based on the Internet of Things, and sending the real-time behavior data to the intelligent collection device; patient behavior real-time data based on the Internet of Things is transmitted to a given quantitative diagnosis platform based on a wireless network; and the quantitative diagnosis platform is used for preprocessing and analyzing the real-time data of the behaviors of the patient based on the Internet of Things, performing quantitative early diagnosis on the Parkinson's disease of the patient, and performing intelligent management and control treatment on the patient according to the diagnosis result of the patient. The method solves the problem that the diagnosis effect of the Parkinson's disease patient is poor because the Parkinson's disease quantitative early diagnosis cannot be carried out on the patient based on behavior feature recognition in the prior art. According to the invention, quantitative early diagnosis of Parkinson's disease can be carried out on the patient based on behavior feature recognition, and the diagnosis effect of the Parkinson's disease patient can be improved.

The invention discloses a dementia intervention scheme recommendation method and system based on a knowledge graph, and relates to the technical field of intelligent medical treatment and artificial intelligence. And constructing a dementia intervention knowledge graph containing patients, symptoms, intervention measures and environmental elements. And performing intelligent semantic reasoning on the short-term state, the long-term behavior mode and the nursing environment of the patient by fusing multi-level reasoning and situational reasoning, generating a candidate intervention scheme matched with the current state of the patient, and outputting a personalized recommendation result. In the intervention implementation process, the knowledge graph association relation is dynamically updated based on patient behavior feedback and symptom changes, and continuous optimization and self-adaptive adjustment of an intervention scheme are achieved. According to the method, the individuation level, long-term adaptability and safety of dementia intervention recommendation are improved, and the method is suitable for clinical and long-term nursing scenes.

The invention provides an intelligent cognitive movement rehabilitation task generation method based on knowledge graph dynamic adaptation, and the method comprises the steps: collecting the behavior response data of a patient in real time through a multi-modal sensor array, and inputting a pre-trained state evaluation neural network model after preprocessing; outputting a four-dimensional ability state vector representing cognitive load, motion coordination, attention and fatigue degree, taking the vector as a dynamic query condition, starting multi-hop semantic reasoning in the cognitive motion rehabilitation knowledge graph, screening a candidate task set matched with the current state of a patient and associated with a rehabilitation target, and performing task selection on the candidate task set; according to the matching relation between the capability state and the task difficulty, execution parameters such as task presentation duration and action amplitude are dynamically adjusted, task sequence sorting is optimized based on the path distance, and finally generated personalized rehabilitation tasks are presented in a three-dimensional virtual scene mode through an interaction terminal. A closed-loop control process of real-time data acquisition, state evaluation, task generation and execution is formed, and dynamic personalized adaptation of the rehabilitation training process is realized.

The invention provides an automatic evaluation method and system based on a Grasgow coma score. The method comprises the following steps: collecting behavior response, speech response and eye-opening response data of a patient, and generating an initial evaluation data set; based on the initial evaluation data set, performing advanced sequence feature extraction processing by applying a gating loop unit algorithm, performing deep emotion tendency analysis by adopting a deep emotion analysis technology, and generating a behavior speech reaction score; based on the behavior speech reaction score, extracting an eye opening reaction score from the eye opening reaction data by using a multi-modal adaptive fusion algorithm, performing dynamic weighted fusion processing through a preset rule, and generating a comprehensive evaluation index by using a deep reinforcement learning technology; and based on the comprehensive evaluation index, comparing the comprehensive evaluation index with a preset threshold range, automatically judging the current coma level of the patient, and generating a personalized medical suggestion report. According to the technical scheme provided by the invention, the automation and refinement degree of patient coma grade evaluation is remarkably improved.

A mathematical model of type 1 diabetes (T1D) patient decision-making can be used to simulate, in silico, realistic glucose / insulin dynamics, for several days, in a variety of subjects who take therapeutic actions (e.g. insulin dosing) driven by either self-monitoring blood glucose (SMBG) or continuous glucose monitoring (CGM). The decision-making (DM) model can simulate real-life situations and everyday patient behaviors. Accurate submodels of SMBG and CGM measurement errors are incorporated in the comprehensive DM model. The DM model accounts for common errors the patients are used to doing in their diabetes management, such as miscalculations of meal carbohydrate content, early / delayed insulin administrations and missed insulin boluses. The DM model can be used to assess in silico if / when CGM can safely substitute SMBG in T1D management, to develop and test guidelines for CGM driven insulin dosing, to optimize and individualize off-line insulin therapies and to develop and test decision support systems.

The invention relates to the technical field of health management, in particular to an intelligent follow-up visit management method and system for Alzheimer's disease, and the method comprises the steps: collecting physiological characteristic attribute features of a plurality of aspects of behaviors and physiological characteristics of a patient; receiving the Alzheimer's disease correlation degree of the physiological characteristic attribute characteristics in different aspects, and outputting an Alzheimer's disease risk assessment result in combination with the aspect number of patient behaviors; outputting gene characteristics of the physiological characteristics based on the associated conditions of the physiological characteristics; receiving associated behavior information of the to-be-associated patient behavior based on the gene characteristics of the physiological characteristics and the difference between the physiological characteristic attribute characteristics of the physiological characteristics and the physiological characteristic attribute characteristics of the to-be-associated patient behavior, and outputting a forgetting prediction sequence of the to-be-associated patient behaviors in combination with the occurrence frequency deviation so as to perform sequential forgetting prediction on the to-be-associated patient behaviors. According to the invention, the abnormal forgetting assessment can be carried out from the aspect of combining the source with the congenital gene and the acquired environment, so that the Alzheimer's disease of the patient is controlled.

The invention relates to the technical field of artificial intelligence, in particular to a psychological intelligent diagnosis robot, equipment and a storage medium. Comprising a robot position adjustment module, an environment detection and analysis module, a physiological data acquisition module, a physiological data analysis module, a behavior data acquisition module, a behavior data analysis module, a psychological test module, a symptom evaluation module and a management database. The system obtains the influence coefficient of the current diagnosis environment on the patient according to the environmental factor analysis of the diagnosis environment, obtains the influence degree of the psychological state on the physiological reaction according to the physiological index analysis of the patient, and obtains the body relaxation degree of the patient through the behavior data analysis of the patient. The emotion recovery degree of the patient is obtained through voice recording and the emotion test scale, the psychological health condition of the patient is comprehensively analyzed, the corresponding suggestion scheme is provided, the psychological health condition of the patient can be comprehensively mastered, and more accurate psychological health service is achieved.

The invention belongs to the technical field of ward nursing, and provides a ward nursing alarm system comprising a physiological parameter monitoring module provided with a non-contact sensor; an artificial intelligence algorithm is built in the data analysis and processing unit; the alarm module adopts a multi-stage alarm mechanism; the environment monitoring and adjusting module is used for monitoring environment parameters in the ward; the patient behavior analysis module monitors the activity state of the patient through a camera and an action recognition algorithm; an integrated management platform; by integrating a plurality of modules such as a physiological parameter monitoring module, a data analysis and processing module, a multi-stage alarm module, an environment monitoring and adjusting module, a patient behavior analysis module and an integrated management module, continuous non-inductive monitoring of physiological parameters of a patient, intelligent identification and graded alarm of an abnormal state and automatic adjustment and monitoring of a ward environment are realized; and real-time monitoring and abnormal alarm of patient behaviors are realized, so that a convenient and efficient patient management and nursing means is provided for medical staff.

The invention discloses a family member participation type vascular intervention diagnosis and treatment postoperative cerebral infarction early warning method. The method comprises the following steps that patient data are acquired through a client module based on wrist strap data of a patient, and a cerebral infarction monitoring module is started; based on the patient data, binding the patient data with a client module through living body detection and information authentication of a cerebral infarction monitoring module, and starting a monitoring process of the cerebral infarction monitoring module; the behavior data and cerebral infarction risk level data of the patient are obtained through the monitoring process, and the monitoring process is a periodic task and is started and executed at fixed time in 1 hour, 3 hours, 6 hours, 12 hours and 24 hours after the operation; and acquiring early warning data through a medical care response module based on the patient behavior data and the patient cerebral infarction risk level data, wherein the early warning data is used for providing a postoperative cerebral infarction intervention voucher for the patient by medical staff.

The application relates to the field of data processing, and particularly provides a monitoring source patient signal data analysis method and system. The method comprises the following steps: obtaining target patient behavior signals matched with to-be-detected behavior signals, wherein the target patient behavior signals cover behavior signals of different categories; obtaining matching degrees between the behavior signals of different categories covered by the target patient behavior signals and the to-be-detected behavior signals based on influence factors of the behavior signals of different categories; screening selected category behavior signals from the behavior signals of different categories based on the matching degrees between the behavior signals of different categories and the to-be-detected behavior signals; and performing highlight display on signal segments in the selected category behavior signals matched with the to-be-detected behavior signals. The application can simplify the highlight display signals, highlight the signals with more analysis significance, and help more accurate recognition of signal behavior patterns.

A patient support apparatus for inferring a patient's future behavior tracks data related to patient bed exits from the patient support apparatus. A controller may be in communication with the patient support apparatus. The controller may include a processor and a non-transitory memory device. The memory device may include instructions that, when executed by the processor, acquire data related to patient bed exits.

Systems, devices, and methods are provided that allow detection of episodes in analyte measurements and the prompting of a subject to self-report possible causes for the episodes and take action in response to episode detection. Correlation of possible causes with detected episodes assists patient behavior modification to reduce the occurrence of episodes. Also provided is function and structure for associating episodes with visits between the subject and a Health Care Provider (HCP) and scheduling interfaces and reports for assisting the HCP in advising numerous patients.

The invention belongs to the technical field of smart medical information, and discloses a smart medical information processing and analyzing system based on big data, which comprises a data acquisition module used for acquiring a medical behavior data set and preprocessing the medical behavior data set, the patient medical behavior data set comprises patient attention data on diseases, patient staying data in a certain disease related diagnosis and treatment link and patient interaction data with medical services or information; the data processing module is used for processing the patient medical behavior data set; according to the method, the patient interest reference value is obtained by analyzing the patient medical behavior data set, the attention tendency and interest preference of the patient for specific diseases or medical services can be deeply informed, and the patient preference classification result and the required adjustment result are obtained by analyzing the patient behavior data. And a medical institution can clearly know the demand popularity and resource utilization efficiency of medical services related to different diseases.

The invention relates to a chronic disease staged behavior change intelligent guiding method, and belongs to the technical field of chronic disease health management. The method comprises the steps of capturing behavior, psychological, environmental and physiological data to construct a four-dimensional data set, filling missing values by adopting similar patient behavior mode interpolation, filtering abnormal values by adopting a decision tree branch pruning rule, and generating a patient health portrait vector; constructing a training set based on longitudinal behavior data, screening key dimensions through feature importance, and optimizing a behavior change dynamic decision tree in combination with an incremental dynamic iterative training mechanism; inputting the four-dimensional data set into a decision tree, generating a personalized obstacle analysis report and predicting a future stage retrograde risk; and calling a corresponding strategy library, and dynamically adjusting the guide strategy by taking the task completion rate and the index improvement amplitude as feedback signals. Accurate judgment of the chronic disease behavior change stage is achieved, the problems of single data dimension and strategy homogenization in traditional intervention are solved, and the patient behavior change compliance and the long-term management effect are improved.

The invention provides an Internet hospital patient propaganda and education method based on a recommendation algorithm, and the method comprises the steps: collecting the historical browsing behaviors of real-name patients in the hospital Internet, and constructing a relation graph between the patients and propaganda and education articles; mining similarity of propaganda and education articles according to patient behaviors; recommending by utilizing the similarity between the propaganda and education articles; and recommending the first K propaganda and education articles with the highest interest to different patients on the home page of the Internet hospital. Personalized recommendation not only can improve the disease prevention and treatment ability of the patient, but also can guide the public to pay attention to more professional and valuable health propaganda and education knowledge to a certain extent, and has social education significance.

The invention relates to the technical field of medical services, in particular to a statistical database-based disease cause formation intelligent query analysis system, which comprises a case processing module, a case analysis module and a disease cause query module. The case processing module is used for collecting historical case information and extracting a plurality of classification topics according to the historical case information, each classification topic comprises but is not limited to disease causes and influence factors, a plurality of analysis nodes are divided according to the acquired classification topics and the corresponding disease causes and influence factors, and the analysis nodes are used for analyzing the disease causes and influence factors; determining a corresponding relation of the historical case information among the analysis nodes, and constructing the historical case information into an analysis network according to pathogenesis and influence factors; the case analysis module is used for analyzing the historical case information according to the obtained analysis network, obtaining behavior information corresponding to the patient, setting behavior nodes corresponding to the behavior information, and setting a plurality of behavior tags according to different corresponding behavior nodes of patient behaviors; and the diagnosis efficiency and accuracy are improved.

Devices, systems, and techniques for analyzing video information to objectively identify patient behavior are disclosed. A system may analyze obtained video information of patient motion during a period of time to track one or more anatomical regions through a plurality of frames of the video information and calculate one or more movement parameters of the one or more anatomical regions. The system may also compare the one or more movement parameters to respective criteria for each of a plurality of predetermined patient behaviors and identify the patient behaviors that occurred during the period of time. In some examples, a device may control therapy delivery according to the identified patient behaviors and / or sensed parameters previously calibrated based on the identified patient behaviors.

The invention relates to the technical field of image monitoring, in particular to an elderly patient behavior image monitoring and falling risk analysis system and method, and the method comprises the steps: a front-end collection module collects a time sequence image of an activity area of an elderly patient; the posture detection module analyzes and extracts three-dimensional posture information of the elderly patient based on a differential geometry theory, and comprises a key point detection unit, a manifold mapping unit and a constraint optimization unit; the gait analysis module processes attitude parameters through a parameter extraction unit, a time sequence track unit and a curvature analysis unit; the risk assessment module calculates a fall risk level based on the gait feature data; according to the invention, an APMK-HCW algorithm is innovatively applied to realize high-precision 3D attitude estimation under a monocular camera, geodesic curvature analysis is introduced to quantify gait stability, and a personalized baseline assessment and adaptive optimization mechanism is adopted to realize early precise early warning of the falling risk of the elderly patient.