38 results about "One chronic disease" patented technology

The invention discloses a chronic disease medical and anti-medical fusion collaborative management and control system, belongs to the technical field of health data processing, and solves the problem that when an existing system extracts an electronic case file of a user, the health condition change of a patient cannot be reflected in real time, and consequently key feature extraction is inaccurate. The system comprises a user side, a fusion cloud platform, a medical defense judgment module and a follow-up visit customization module, and the fusion cloud platform is used for executing a risk assessment model and outputting a risk assessment result by taking a normalized data set associated with a user as input; a risk assessment model based on deep learning and an attention mechanism is constructed by fusing a cloud platform, so that the risk assessment model can identify and analyze a normalized data set collected by a user side, and the user side can collect a multi-source heterogeneous normalized data set which is good in timeliness and reflects the health condition of a patient in real time; therefore, it is ensured that the risk assessment model provides an accurate assessment path for chronic disease medical prevention and control.

The invention relates to the technical field of chronic disease information management, in particular to a chronic disease information management method which comprises the following steps: S1, acquiring physiological index data, medication record data and behavioral habit data of a patient through a multi-source data acquisition module; s2, performing feature extraction on the multi-source data by using a data analysis module to generate multi-dimensional health assessment parameters; and S3, dynamically comparing the health assessment parameters with a preset assessment value through a risk early warning module, triggering hierarchical early warning when an abnormal parameter combination is detected, realizing multi-modal data deep mining by using an intelligent analysis module, performing association analysis on wearable device sensor data, an electronic medical record and a medication compliance record, and determining whether the health assessment parameters are abnormal or not. An individualized medication portrait of the chronic disease patient is constructed, parameters such as gene detection and metabolic indexes are combined, through medicine-disease conflict detection and dose self-adaptive calculation, an accurate medication adjustment strategy and a chronic disease patient health behavior guidance scheme are generated, and individual differences are fully reflected.

The invention discloses a chronic disease tracking management system, and belongs to the technical field of chronic disease patient management, and the system comprises a primary medical worker training module which is used for carrying out the skill and knowledge training of primary medical workers; the patient baseline data collection module is used for collecting patient baseline data and performing data analysis; the patient tracking guidance module is used for providing main control index monitoring guidance, daily nursing guidance, prevention control guidance, insulin injection guidance and the like for the patient; the prevention and improvement action plan module is used for monitoring health indexes of the patient, and regularly evaluating and timely adjusting a treatment scheme; analyzing and providing improvement suggestions according to the living habits of the patient; and the medical nutrition treatment module is used for formulating a personalized diet scheme with balanced nutrition for the patient. The system is intelligent, personalized and comprehensive, the chronic disease management level is integrally improved, and chronic disease prevention and treatment work is facilitated.

The invention relates to the technical field of data processing, in particular to a chronic disease monitoring method, system and device and a storage medium, comprising: acquiring disease information of a patient, and determining the disease category of the patient according to the disease information; obtaining a to-be-collected information type associated with the illness state type according to the illness state type; acquiring follow-up visit body parameters of the patient according to the type of the to-be-collected information and a preset return visit period; establishing follow-up visit body change trend information according to the follow-up visit body parameters; performing trend prediction on the follow-up visit body change trend information based on a preset prediction period to obtain body prediction change trend information of the patient in the prediction period; generating a monitoring analysis result according to the body prediction change trend information and the illness state information; by setting the fixed return visit period, collecting the specific return visit indexes of the patient and combining the indexes and the illness state, the illness state development trend of the patient is predicted, and therefore the monitoring efficiency of the chronic disease patient in the treatment process is improved.

The invention relates to the technical field of computers, and discloses a chronic disease patient archive data analysis method and device, a storage medium and equipment, and the method comprises the steps: dividing training archive data into discrete variables and continuous variables, carrying out the processing of each class value in the discrete variables through one-hot coding, and carrying out the processing of each class value in the continuous variables through one-hot coding; a user-defined normalization method is applied; the discrete variables and the continuous variables after standardization processing are combined into condition vectors, two discrete variable combinations with equal probabilities are randomly selected, all combination probabilities are calculated, and sampling is completed; the generative adversarial network framework is used for training the generative model and the discrimination model for multiple times, the discrimination model is used for discriminating the generative model to obtain a trained generative model, synthetic data are generated, and a neural network used for patient feature analysis is trained to predict chronic diseases. According to the invention, the problem that complete analysis and privacy protection of archive data of chronic disease patients cannot be achieved at the same time in the prior art is solved.

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 invention discloses a chronic disease health management online consultation method and system, and relates to the technical field of chronic disease health online consultation. A medical record uploading unit obtains medical record data, and then after an interaction management unit receives the medical record data, an access table of the consultation is constructed in real time; then, a plurality of segment arrays converted from medical record data are converted according to a scale disk to obtain safety management data, and in the conversion process, a basic identification point and first and second conversion sites of each segment array are determined through the positions of inner circle scale points and outer circle scale points respectively mapped on a scale inner circle and a scale outer circle by adjacent segment arrays; the segment arrays with the same content have different conversion bit sequences due to the difference between the segment arrays adjacent to the segment arrays, the complexity and the cracking difficulty of medical record data conversion are further enhanced, meanwhile, the encryption logic is used for replacing a traditional secret key for encryption, and the safety of data of a user in the online consultation process is further guaranteed.

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 invention relates to the technical field of chronic disease tracking management, in particular to a chronic disease closed-loop management system, which comprises a data acquisition module; a data analysis module; a filing module; a referral analysis module; a team building module; an adjusting module; according to the method, the health condition of the patient is preliminarily analyzed based on the mercury column height measured by the blood pressure of the patient, and the health condition of the patient is further analyzed based on the data change trend in the historical data of the patient, so that misjudgment caused by high blood pressure measured at a time due to equipment problems is avoided; when the health condition of the patient is judged to be unqualified, whether the patient is referred is analyzed based on the screening hierarchy, and when the first examination is common examination, referral further examination is carried out, so that misjudgment caused by factors such as environment is avoided, the analysis accuracy of the health condition of the patient is improved, and the accuracy of health condition analysis is improved. When the health state of the patient is judged to be unqualified, a management team is established, so that the patient can perform nearby revisit, and the reasonable utilization degree of medical resources 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 discloses a chronic disease recording device which comprises a reminder, a display screen, a timing module and a control module are arranged on the reminder, an alarm lamp and a buzzer are installed on the top of the reminder, a base plate is arranged below the reminder, a clamp is fixed to the top of the base plate, a recording chart is clamped in the clamp, and a marking piece is arranged on the recording chart. A storage box used for containing the marking piece is arranged on the back face of the base plate, and a control assembly used for controlling opening or sealing of the storage box is arranged on the back face of the base plate. When the condition of a patient in a certain day has abnormal fluctuation on data, the abnormal data are briefly recorded on the writing area, and the marking piece and the recorded page are pasted, so that a doctor can directly observe which day data is abnormal through the marking piece in the later period, and can directly find the recording page of the day, and therefore, the doctor can conveniently and quickly observe the abnormal condition of the patient. Detailed data comparison is facilitated, marking pieces do not need to be pasted on stable data, reading is not needed in the later period, and the working efficiency of doctors is improved.

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.

A method for supporting self-management of people with at least one chronic condition, and the assessment of their psychical and mental conditions is provided. The method includes a health recommender and is carried out by at least one processor. The method includes receiving by the at least one processor first and second health parameters of a user, and additional data describing the interactions of the user with previous data generated by the health recommender. The method also includes receiving by the at least one processor content data, and processing by the at least one processor the first and second health parameters to generate a first user model, and generating by the at least one processor at least one health recommendation to the user comprising content data. The method then includes selecting at least one recommendation for the user and providing the selected at least one recommendation to the user.

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 invention provides a chronic disease patient depression risk prediction method, and relates to the field of chronic disease patient depression risk prediction. The chronic disease patient depression risk prediction process provided by the invention comprises the steps of constructing a chronic disease patient depression risk prediction data set, constructing a deviation value sequence based on a median value of each factor data, mapping the deviation value sequence into a high-dimensional feature sequence, calculating a tension value and an overall tension mean value of the high-dimensional feature sequence, and predicting the depression risk of the chronic disease patient. Introducing an adaptive switching factor and nonlinear phase transformation to obtain a fusion feature sequence; extracting a local segment of the fusion feature sequence by adopting a sliding window; calculating the local complexity and trend bending rate of the local segment; introducing a local control factor; the final feature representation of each factor data is generated, the full-connection neural network is input to calculate the chronic disease patient depression risk prediction value of each factor data, the mean value is taken as the final prediction value, and the method can cooperatively utilize local change and overall trend to enhance prediction accuracy.

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 discloses a chronic disease treatment cost prediction method, which comprises the steps of setting a data acquisition rule, and acquiring multi-source chronic disease medical data based on the data acquisition 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; respectively training the plurality of candidate models by adopting 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. According to the method, the chronic disease treatment cost can be accurately predicted, powerful support is provided for medical insurance cost control and medical decision, and the method has important practical value and social benefits.

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 discloses a chronic disease prevention and treatment multi-sample long-time preservation sample box, and belongs to the field of sample boxes, the chronic disease prevention and treatment multi-sample long-time preservation sample box comprises a supporting base, a sealing cover body is fixedly mounted on the upper surface of the supporting base, and a first rotating groove and a second rotating groove are formed in the two sides of the upper surface of the supporting base respectively; a storage part is rotatably mounted in the first rotating groove, a display part is rotatably mounted in the second rotating groove, and the upper end of the display part is rotatably mounted on the closed cover body. By arranging the display part and the storage part, when a target sample test tube needs to be taken out, only a corresponding sliding shell needs to be pressed, and the target sample test tube can be taken out; the sample test tube at the corresponding position can be unloaded into the unloading door by rotating the rotating cylinder body, and then the target sample test tube can be taken out by opening the unloading door, so that the whole device does not need to be opened, the temperature in the closed cover body is not influenced, the temperature fluctuation is prevented, and the sample can be stored for a long time and is safer.

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.