94 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 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 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 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 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 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.

Disclosed herein is a healthcare management system and method utilizing machine learning based predictive analytics to improve patient adherence rates for chronic care management (e.g., diabetes). An example system may include a computing device configured to establish a customer analytical record to synthetize over a plurality of selected attributes to form a patient centric view of patient behaviors, attitude, characteristics based upon data related to chronic disease state management and social determinants of health, consumer experience to determine, using a predictive artificial intelligence model, a patient's predictive adherence for next 1-3 months, and determine tailored intervention plans to engage patients for continuing adherence.

Systems and methods are provided for managing medical adherence. An exemplary method may include managing medical adherence utilizing data aggregating and processing to determine impact on a user's health based on their behavior related to prescribed medication. The method may entail utilizing data related to a medication regimen and patient behavior to determine a patient's compliance to the regimen in terms of dosage and time. These values may be utilized to calculate a medical adherence value representing a patient's adherence to a prescribed regimen. Responsive to determining low medical adherence, a notification may be generated which may result in an intervention with the patient.

Automating patient care by training an artificial intelligence using a data structure organized in a patient care matrix comprising levels of domain knowledge. The care matrix data structure is defined and populated with training data. The artificial intelligence includes a plurality of artificial intelligence nodes each trained using aspects of the care matrix data structure such that an entity AI node instance is trained using a data set comprising subset of training data utilized in training child entities node instances of the entity AI node being trained. A long form description of a patients behavior or disorder is obtained and a natural language processor is employed to generate input phrases to be supplied to the AI for analysis. The AI analyzes the obtained phrases using a plurality of the trained AI node instances to automatically generate a patient treatment profile including one or more therapies and associated measures.

The invention relates to the technical field of large models, in particular to a chronic disease management method, system and device and a storage medium. The method comprises the steps of firstly analyzing user query content, extracting query intention features and generating medical suggestions in combination with personalized parameters; collecting medical equipment data in real time, analyzing health state characteristics in combination with medical suggestion information, and outputting a health monitoring result; according to the health monitoring result and a preset medical scheme, task type features and patient behavior features are fully considered, intervention execution features are determined in a progressive task decomposition mode, and then a standardized intervention instruction is generated; acquiring related historical data from the memory system based on the intervention instruction for matching, and finally integrating a matching result and real-time data to update the health record of the patient; and an execution plan better conforming to the actual condition of the patient is made through a task decomposition mode, so that the pertinence of the medical scheme is improved.

The application relates to the technical field of internet medical treatment and artificial intelligence, and discloses an internet hospital whole-process intelligent diagnosis and treatment service management system based on artificial intelligence. The method collects multi-source digital phenotype data through a double-granularity aggregation strategy, generates continuous representation of patient behavior state by using a time sequence representation learning model based on a mask autoencoder, separates a disease activity score and a compliance probability score through a causally decoupled double-branch network, calculates a crisis emergency degree score by using a mental and psychological crisis early warning network and triggers a fuse mechanism, realizes post-diagnosis management path switching, emergency resource matching and crisis handover information package generation, and solves the problems of insufficient time resolution, data loss vulnerability, cause coupling confusion and response channel loss in mental and psychological department post-diagnosis acute crisis detection and emergency response.

Embodiments of the present disclosure may include a method of mapping patient data and representing data from electronic health records of an individual through a pictorial representation of their human body including receiving, over at least one communication network from each of a plurality of user computing devices operated by each of a plurality of users, electronic health records (EHR) respectively representing information from the plurality of user's electronic medical records, procured from the user's health care provider or the user's electronic health records (EHR) partners. Embodiments may also include providing, a decentralized and distributed patient facing method of communicating and providing robust critical formulation of health-related clinical data of a patient using artificial intelligence (AI) and natural language processing (NLP) to engage a patient that encourages a change in a patient's behavior to improve the patient's overall health.

Disclosed herein is a healthcare management system and method utilizing machine learning based predictive analytics to improve patient adherence rates for chronic care management (e.g., diabetes). An example system may include a computing device configured to establish a customer analytical record to synthetize over a plurality of selected attributes to form a patient centric view of patient behaviors, attitude, characteristics based upon data related to chronic disease state management and social determinants of health, consumer experience to determine, using a predictive artificial intelligence model, a patient's predictive adherence for next 1-3 months, and determine tailored intervention plans to engage patients for continuing adherence.

The application relates to the technical field of medical information processing, and discloses an Alzheimer's disease patient behavior monitoring method and system based on data analysis. The method comprises the following steps: collecting physiological data, behavior data and voice data of a monitoring target in a preset time interval; converting the voice data into text data, analyzing the voice data and the text data to determine a first degeneration level of the monitoring target; determining the actual occurrence frequency of each behavior type based on the behavior data; determining a standard frequency range of the behavior type based on the first degeneration level, comparing the standard frequency range with the actual occurrence frequency to determine a second degeneration level of the monitoring target; determining the diurnal activity ratio of the monitoring target based on the physiological data, and determining a third degeneration level based on the diurnal activity ratio; and comprehensively determining an overall degeneration level based on the first degeneration level, the second degeneration level and the third degeneration level. The application can collect multi-dimensional behavior data of a patient, thereby realizing accurate monitoring of the development of the patient's condition.

A system for outputting a visual representation of a brain of a patient is configured to receive sensor data representing a behavior of a region of the brain of the patient. The system retrieves mapping data that maps a prediction value to the region. The prediction value is indicative of an effect on a behavior of the patient responsive to a treatment of the region, the mapping data being indexed to a patient identifier. The system receives, responsive to an application of a stimulation to the region, sensor data representing behavior of the region. The system executes a model that updates, based on the sensor data, the prediction value for the region. The system updates, responsive to executing the model, the mapping data by including the updated prediction value in the mapping data. The system outputs a visual representation of the updated mapping data comprising the updated prediction value.