23 results about "One chronic disease" patented technology

The invention relates to the technical field of medical health informatization, and discloses a chronic disease tracking management system, which is characterized in that a data acquisition module, a data analysis module, a health monitoring module, a user interaction module, a control module and a tracking management optimization module are established, key factors of physiology, life and environment influencing chronic diseases are comprehensively covered; the data analysis module is used for deeply mining data values based on a scientific calculation formula and accurately identifying potential health risks; the health monitoring module realizes automatic and personalized health early warning and management suggestion pushing according to an analysis result and threshold setting, all the modules cooperate closely, and finally the system has the advantages of data comprehensiveness, risk pre-judgment accuracy, management personalization and service continuity. The problems of data dispersion, risk assessment lagging and single intervention measure in traditional chronic disease management are solved, and a whole-process and intelligent health management service is provided for chronic disease patients.

The invention relates to the technical field of medical information management, in particular to a chronic disease patient home nursing remote management system which comprises a volatility analysis module, a symbol recognition module, a correlation analysis module, a task decoupling module and a nursing task generation module. According to the invention, by analyzing the fluctuation amplitude of the continuously collected physical sign data in the specific time window, the short-term sudden change trend which cannot be revealed by single-time manually reported data can be explored, and the data change direction is further symbolized to identify a continuous change mode with clinical significance; and the time overlapping degree of the continuous sudden change time periods of different physical sign data can be calculated, so that the synchronous association reaction among various physical sign indexes is accurately identified, the potential composite physiological risk is revealed, and the dependency decoupling and execution sequence reconstruction of the high-risk task are realized based on the quantitative evaluation of the task emergency degree and the delay degree.

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 utility model discloses a chronic disease auxiliary diagnosis and treatment tracking monitoring image analysis device which comprises a sliding frame and a film viewer arranged at the top of the sliding frame. The bottom of the dismounting device is in sliding connection with the inner wall of the sliding frame, and the inner wall of the dismounting device is fixedly connected with the outer wall of the film viewer. According to the utility model, the dismounting device is arranged, so that the connection and separation operation of the film viewer is simple and convenient; the inserting blocks are inserted into the inserting grooves, under the action that the magnetic blocks exert attraction force on the iron blocks, the two film viewer bodies are rapidly connected, when only one film viewer body needs to be used, the two film viewer bodies can be easily separated by pressing the pressing blocks, and the device can meet the requirement for comparing two images in chronic disease diagnosis and treatment and is convenient to use. When a doctor performs diagnosis, treatment, tracking and monitoring on a chronic disease patient, the doctor can conveniently and intuitively compare examination images in different stages and judge disease development and treatment effects.

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 discloses a chronic disease medical data collection, analysis, management method and cloud platform, comprising the following steps: firstly, acquiring the first chronic disease symptom description of the patient side and the second chronic disease symptom description of the doctor side through an interactive terminal respectively, obtaining a target chronic disease description through a preset large language model integration, and determining the target chronic disease type through a target chronic disease analysis model. Then, the feedback information of the doctor for the type is acquired, the feedback information and the target chronic disease type are input into the large language model to generate a management suggestion, and the management suggestion is pushed to a preset interactive interface of the interactive terminal, so that the chronic disease data is effectively processed and managed.

The invention discloses a chronic disease medical data acquisition, analysis and management method and a cloud platform, and the method comprises the steps: firstly, obtaining a patient-side first chronic disease symptom description and a doctor-side second chronic disease symptom description through an interaction terminal, and carrying out the integration through a preset large language model, thereby obtaining a target chronic disease description; and the target chronic disease type is determined by the target chronic disease analysis model. Feedback information of a doctor for the type is obtained, the feedback information and the target chronic disease type are input into the large language model to generate a management suggestion, the management suggestion is pushed to a preset interaction interface of the interaction terminal, and effective processing and management of chronic disease data are achieved.

The utility model relates to the technical field of chronic disease medication devices, and discloses a chronic disease medication device which comprises a medicine box, a medicine tray is rotatably arranged in the medicine box and is annular, a plurality of partition plates are fixed to the top face of the medicine tray and divide the top face of the medicine tray into a plurality of medicine storage cells, a medication structure is arranged in the medicine box, and the medicine storage cells are arranged in the medicine box. The medicine applying structure comprises a plurality of tooth grooves formed in the inner circle wall face of the medicine disc. According to the medicine taking device, through the arrangement of the medicine taking structure, a user starts the motor, the motor drives the gear to rotate, the gear is meshed with the tooth groove to drive the medicine tray to rotate, the medicine storage grids containing medicine move to the position of the medicine taking hole, and then the user can take out the medicine to take out the medicine by opening the cover plate; a patient who is inconvenient to move can conveniently and quickly take out stored medicine, and the medicine taking effect of the device is improved.

Provided in the present invention is a general practitioner workstation, comprising a resident health profile creation module, a chronic disease screening module, a chronic disease assessment module and a chronic disease follow-up module, and further comprises an identity information recognition and input module, a health information input and update module, a population tagging module, an identity card reading / radio-frequency information recognition module, a medical insurance card / electronic medical insurance code recognition module, etc. The general practitioner workstation of the present invention can solve the problems, in chronic disease management, of a resident health profile creation procedure being cumbersome, a plurality of discrete devices being inconvenient to carry and making it difficult to summarize test results, the level of intelligence in chronic disease assessment being low, it being impossible to automatically generate a follow-up plan, and it being impossible to automatically adjust a management scheme on the basis of a follow-up result, thereby comprehensively improving the quality and efficiency of medical services, and providing more scientific, comprehensive and personalized medical services. Also provided in the present invention are a chronic disease management method and a corresponding product.

The application provides a follow-up method, device and equipment for a chronic disease patient, and relates to the technical field of data processing. The method comprises the following steps: acquiring a chronic disease type, current follow-up data, historical diagnosis and treatment data and historical follow-up data of the chronic disease patient; determining blank data in the current follow-up data according to the chronic disease type; wherein the blank data is follow-up data that is not obtained when the chronic disease patient is followed up; determining a risk probability of a chronic disease condition change of the chronic disease patient according to the chronic disease type, the current follow-up data, the blank data, the historical diagnosis and treatment data and the historical follow-up data; and determining a time of next follow-up of the chronic disease patient based on the chronic disease type, the risk probability and the blank data of the chronic disease patient. The application can acquire follow-up data of the chronic disease patient in time, master the recovery condition of the chronic disease patient and ensure the effectiveness of follow-up.

The application discloses a kind of chronic disease auxiliary drug curative effect evaluation and personalized recommendation method, comprising the following steps: step one: real world data acquisition, step two: data standardization and annotation, step three: curative effect evaluation model construction, step four: patient population stratification, step five: personalized drug recommendation, step six: data update and model iteration;The application is aimed at the clinical characteristics of three kinds of chronic diseases, liver disease, diabetes and hypertension, and constructs a multidimensional real world database and a targeted curative effect evaluation model, realizes the personalized and accurate recommendation of the auxiliary drug of the three kinds of chronic diseases, breaks through the limitation of single disease drug recommendation, effectively improves the clinical curative effect of auxiliary drug, reduces the risk of adverse drug reactions, and provides scientific support for the clinical rational use of drugs for the three kinds of chronic diseases.

The application provides a chronic disease risk prediction method and related device; the method comprises: screening and unifying dimensions of patient data, obtaining initial data and inputting a deep neural network; mapping the initial data to an input matrix, performing self-attention calculation operation of each feature head, combining the outputs of each feature head to obtain a self-attention score matrix, setting residual connection operation and layer normalization operation for the input matrix and the self-attention score matrix to construct a gradient forward propagation feature extraction network; according to the time length covered by the initial data, inputting the local feature information of the output of the feature extraction network corresponding to the time length to the corresponding local risk prediction network, and splicing the local feature information output by each feature extraction layer into global feature information input to a global risk prediction network; predicting the risk probability of the global feature information and the local feature information, and obtaining a prediction result.

The present application relates to the technical field of data processing, and particularly relates to a chronic disease monitoring method, system, device and storage medium, comprising obtaining disease information of a patient, and determining a disease category of the patient according to the disease information; obtaining a to-be-collected information type associated with the disease category according to the disease category; obtaining a follow-up physical parameter of the patient according to the to-be-collected information type and a preset follow-up cycle; establishing follow-up physical change trend information according to the follow-up physical parameter; performing trend prediction on the follow-up physical change trend information based on a preset prediction cycle, to obtain physical prediction change trend information of the patient within the prediction cycle; generating a monitoring analysis result according to the physical prediction change trend information and the disease information; by setting a fixed follow-up cycle, collecting specific follow-up indexes of the patient, and combining the indexes and the disease condition, predicting the disease development trend of the patient, so as to improve the monitoring efficiency in the treatment process of the chronic disease patient.

The invention provides a chronic disease data dynamic desensitization method and device, equipment and a storage medium. The method comprises the following steps: acquiring case information of each chronic disease patient in a preset time period; performing preliminary desensitization on the case information of each chronic disease patient according to a preset rule to obtain multiple pieces of case information after preliminary desensitization; for each piece of case information after preliminary desensitization, calculating a risk degree corresponding to the case information after preliminary desensitization based on the case information after preliminary desensitization; and if the risk degree corresponding to the preliminarily desensitized case information is greater than or equal to a first preset threshold value, further desensitizing the preliminarily desensitized case information based on a generative adversarial network to obtain a final desensitization result. According to the method, the real-time dynamic desensitization of the data can be realized, the scientific research value of the data can be reserved as much as possible on the basis of ensuring privacy security, meanwhile, association protection can be realized, and the desensitized data is prevented from being easily cracked.

The invention relates to the technical field of information aggregation, and particularly discloses a remote consultation system and method for chronic disease management, and the method comprises the steps: comparing the body parameters and examination results of different users, calculating the condition distance of the users, and carrying out the clustering of the users according to the condition distance; when a new check result is generated, querying the class of the user corresponding to the check result, and taking the check result as a virtual check result of the class of user; when a new check result is generated, querying a user corresponding to the check result, querying a virtual check result of the user, verifying the virtual check result, and updating statistical data of the user when the verification is passed; when a consultation request sent by a user is received, feeding back the updated statistical data; according to the method, each user is placed in one type of users, and for each user, multiple times of non-personal check are equivalent, so that the data volume is greatly expanded, and the user can conveniently know the state of the user.

The application discloses a chronic disease treatment cost prediction method, comprising the following steps: setting a data collection rule, acquiring multi-source chronic disease medical data based on the data collection rule, obtaining a standardized medical data set based on the multi-source chronic disease medical data, obtaining an optimized feature set based on expert opinions and the standardized medical data set, optimizing the feature set based on recursive feature elimination and regularization processing to obtain an optimal feature subset, obtaining a final data set based on the optimal feature subset, training a plurality of candidate models based on the final data set to obtain an optimal model set, and obtaining a chronic disease treatment cost prediction value based on the optimal model set. The application can accurately predict the chronic disease treatment cost, provide strong support for medical insurance cost control and medical decision-making, and has important practical value and social benefits.

The invention provides a follow-up visit method, device and equipment for chronic disease patients, and relates to the technical field of data processing. The method comprises the following steps: acquiring a chronic disease type, current follow-up visit data, historical diagnosis and treatment data and historical follow-up visit data of a chronic disease patient; determining blank data in the current follow-up visit data according to the chronic disease type; wherein the blank data are follow-up visit data which are not obtained when follow-up visit is carried out on the chronic disease patient; according to the chronic disease type, the current follow-up visit data, the blank data, the historical diagnosis and treatment data and the historical follow-up visit data, the risk probability of chronic disease condition change of the chronic disease patient is determined; and determining the next follow-up time of the chronic disease patient based on the chronic disease type, the risk probability and the blank data of the chronic disease patient. The follow-up visit data of the chronic disease patient can be obtained in time, the recovery condition of the chronic disease patient is mastered, and the follow-up visit effectiveness is guaranteed.

This invention discloses a personalized nursing plan management system for patients with chronic diseases, relating to the fields of medical informatization and intelligent health management. The invention decomposes rhythms into time-varying phases and amplitudes and estimates them online, forming continuously updatable rhythm trajectories. This allows rhythm changes to be explicitly characterized as parametric trajectories, thereby reducing reliance on fixed steady-state assumptions. By judging unidirectional cumulative drift and cumulative decay within continuous monitoring periods and introducing uncertainty gating and graded state transitions, the warning is transformed from single-point triggering to evidence accumulation triggering, reducing misjudgments caused by noise, missing data, and uneven sampling, while retaining sensitivity to asymptotic distortion. Furthermore, through missing data marking, continuation strategies, and uncertainty propagation mechanisms, the system can maintain continuous output even when data is incomplete, and adopts a confirmation mode for low-confidence interval judgments to avoid triggering excessive intervention when evidence is insufficient.

The invention relates to the field of medical instruments, in particular to a chronic disease medication device which comprises a shell and a controller, a box cover is detachably connected to the top of the shell, a plurality of medicine bins used for storing different medicines are arranged in the shell in the circumferential direction, and each medicine bin is provided with a medicine outlet and an electronic tag used for identifying medicine information. The electronic tags are all in signal connection with the controller; a hollow shaft is arranged in the middle of the shell, the hollow shaft is coaxially and rotationally matched with a dosage bin, and a medicine inlet communicated with one of the medicine outlets is formed in the outer wall of the dosage bin; and an adjusting mechanism for adjusting the dosage is arranged in the dosage bin. And a rotating mechanism for rotating the dosage bin is arranged in the shell. The electronic tag is integrated on each medicine bin and is in signal connection with the controller, so that identification and management of medicine information are realized.

The present invention proposes a method for predicting depression risk in patients with chronic diseases, which relates to the field of predicting depression risk in patients with chronic diseases. The process for predicting depression risk in patients with chronic diseases proposed in the present invention includes constructing a dataset for predicting depression risk in patients with chronic diseases, constructing a deviation sequence based on the median value of each factor data, mapping it into a high-dimensional feature sequence, calculating its tension value and the overall tension mean, introducing an adaptive switching factor and a nonlinear phase transformation to obtain a fused feature sequence, using a sliding window to extract local segments of the fused feature sequence, calculating the local complexity and trend curvature of the local segments, introducing a local control factor, generating multi-scale path weights, weightedly fusing the local segments, generating the final feature representation of each factor data, inputting a fully connected neural network to calculate the predicted value of depression risk in patients with chronic diseases for each factor data, and taking the mean as the final predicted value. This method can synergistically utilize local changes and overall trends to enhance prediction accuracy.

The invention provides a chronic disease index early warning notification method, system and device and a medium, and belongs to the technical field of medical information. The method comprises the following steps: configuring and dynamically updating normal value ranges of health index items of different chronic disease types and crowd classifications, and generating versioned rule configuration data; receiving health index data uploaded by a patient, and performing data cleaning and standardization processing based on verification rules to generate standardized index time sequence data; matching from the rule configuration data through a rule matching engine based on the patient file data and the versioned rule configuration data, and generating personalized range reference data; performing comparative analysis on the standardized index time sequence data and the personalized range reference data to generate anomaly recognition result data; and based on the abnormal identification result data, generating structured early warning information in combination with historical data of the patient, and sending the structured early warning information to an information terminal of a responsible doctor through a multi-channel distribution strategy. According to the invention, the accuracy and timeliness of the chronic disease medical service are obviously improved.

The invention discloses a matching method and system for precise conditioning of chronic diseases. The technical problems that an existing chronic disease conditioning scheme is poor in personalized adaptability, low in metabolism regulation precision and high in long-term drug dependence are solved. According to the method, target organ cell metabolism characteristics and whole body physiological indexes of a chronic disease patient are collected, a cell metabolism cooperation order quantification model is constructed, and a metabolism cooperation optimal matching threshold interval is determined; on the basis of the threshold value interval, a multi-dimensional cooperative conditioning scheme of diet intervention, targeted nutritional supplement and lifestyle regulation is matched for the patient, precise optimization of the metabolic order of target organ cells is achieved, the clinical symptoms of chronic diseases are improved, and drug use dependence is reduced. The conditioning method is high in accuracy and adaptability, can be implemented by relying on conventional medical detection equipment, and is suitable for large-scale popularization and application in the large health field.

The invention discloses a chronic disease multi-mode digital therapy AI adaptation system. The system comprises the following modules: a time decay case feature processing module, a multi-head attention-based LSTM prediction module, a diagnosis and treatment scheme acquisition module and a dynamic adjustment module. According to the chronic disease multi-mode digital therapy AI adaptation system, the timeliness of medical records can be accurately utilized, the value of the medical records at different time to a current decision is reasonably quantified through a time decay model, expired data interference is avoided, the feature processing accuracy is improved, a feature contribution report can be output through a multi-head attention mechanism, and the accuracy of feature processing is improved. AI decisions can be clinically understood, a medical-driven index system is combined, therapy recommendation fits clinical thinking and patient individual characteristics, patient illness state changes and medical research progress are continuously adapted through a closed-loop optimization mechanism, the chronic disease control standard-reaching rate is remarkably increased, the complication risk is reduced, and the patient experience is improved; and the precision development of intelligent medical treatment is facilitated.