264 results about "Hospital information system" patented technology

The invention discloses an intelligent nursing training system and method based on a large language model, and belongs to the cross technical field of artificial intelligence and nursing education. The system comprises a large language model, a dynamic knowledge graph, a virtual case generation module, a multi-modal evaluation module and a federal learning framework. A dynamic knowledge network is constructed by integrating a hospital information system, a high-simulation case containing 60% of error scenes is generated in combination with a generative adversarial network, and a personalized training scheme is optimized by utilizing reinforcement learning. The method covers multi-source data management, nurse ability grading, real-time decision support (four-level alarm system) and closed-loop effect evaluation. The innovation points comprise: (1) a professional nursing large model, wherein the medical term understanding accuracy is greater than or equal to 95%; (2) hour-level updating of the dynamic knowledge graph; (3) clinical-training two-way data linkage, wherein the critical response time is less than or equal to 6 seconds; and (4) realizing cross-department collaboration by federal learning. The system provides an intelligent and personalized solution for nursing talent cultivation, and has industrial popularization value.

The invention relates to the technical field of intelligent medical treatment, in particular to an endocrine patient health management method and system integrated with an electronic medical record. The method comprises the following steps: performing multi-dimensional data acquisition of an endocrine patient through a hospital information system, and performing multi-dimensional portrait construction of the patient to obtain a multi-dimensional health portrait of the patient; performing time sequence data feature extraction on the multi-dimensional health portrait of the patient to obtain time sequence feature data; acquiring historical physiological data of the patient; establishing an individualized physiological rhythm model according to the historical physiological data of the patient to obtain the individualized physiological rhythm model; and performing non-uniform interpolation on the time sequence characteristic data according to the individualized physiological rhythm model to obtain interpolated data. According to the method, the individualized physiological rhythm model and the dynamic baseline model are constructed, the dynamic early warning threshold value and subtype layering are combined, and accurate risk assessment and individualized health information management are performed on each patient, so that the health management effect is remarkably improved.

The invention relates to the technical field of medical decision, and discloses a medical decision support system based on a knowledge graph, and the system comprises a knowledge graph construction module which constructs an initial knowledge graph based on a medical ontology library, and the knowledge graph comprises entities and association relationships of diseases, symptoms and drugs; the data acquisition module is used for acquiring data from an electronic medical record, wearable equipment, a medical literature library and a hospital information system and normalizing the data through a standardized protocol; the dynamic knowledge updating module is used for processing normalized data through an incremental graph neural network; a multi-source knowledge fusion module; a context awareness module; a dynamic deduction module; and a decision optimization closed loop module. And triggering a preset clinical rule in real time based on the pathological state of the patient, dynamically adjusting the intensity value of the related edge in the factor graph, and persistently storing the intensity value back to the knowledge graph, so that logic adaptation and individualized experience precipitation of general medical knowledge in a special pathological state are realized, and the individualized treatment accuracy is ensured.

The invention provides a common mental disease intelligent diagnosis method and system based on multiple agents, and relates to the field of medical artificial intelligence. The method comprises the following steps: S1, extracting diagnosis standards and symptom characteristics of common mental disorders, and constructing a similar patient knowledge base after verification and evaluation of the similar patient knowledge base and expert calibration; s2, acquiring clinical data from a hospital information system, and performing large medical record structuring, clinical scale simplification and scale score analysis on the clinical data to form a similar patient database; and S3, performing symptom matching and scale performance analysis on the input clinical data, constructing a multi-agent mental disease diagnosis framework, and performing multi-agent diagnosis debate based on the multi-agent mental disease diagnosis framework. The technical problems of symptom overlapping and diagnosis subjectivity among common mental diseases are solved by constructing a multi-agent cooperative diagnosis framework, introducing particle size symptom analysis and dynamically integrating structured authoritative medical diagnosis standards.

The invention relates to the technical field of medicine purchasing, and provides a hospital easy-to-expire medicine purchasing decision optimization method, which comprises the following steps of: firstly, extracting medicine inventory, consumption records and purchasing management parameters in a hospital information system, and constructing a complete inventory state data set; then, calling a demand prediction model constructed based on a time sequence and external factors, and outputting drug prediction demand values in a plurality of periods in the future; thirdly, constructing a multi-cycle purchase optimization model according to the current inventory and the prediction demand; then, dividing a planning period into rolling windows, and gradually generating an ordering scheme meeting conditions by using a heuristic greedy and local search algorithm; then, the scheme is pushed to a purchase decision-making interface, and a formal order is submitted and generated after the scheme is confirmed by a manager; after a supplier delivers goods and stores the goods, the system updates inventory information and records scrapping and shortage data; and finally, adjusting the model through a feedback mechanism so as to improve the accuracy of subsequent prediction and decision.

The invention is suitable for the technical field of intelligent medical treatment, and provides an intelligent decision-making system for clinical diagnosis decision analysis, which comprises a data acquisition module, a knowledge graph module, a multi-modal analysis engine, a clinical evaluation module, a clinical treatment scheme decision-making module and a feedback optimization module, the data acquisition module is used for integrating multi-source heterogeneous clinical data; the knowledge graph module is used for dynamically storing a medical entity relationship; the multi-modal analysis engine extracts features through deep learning; the clinical treatment scheme decision module is used for outputting diagnosis suggestions and providing explanations; the feedback optimization module is used for receiving a doctor correction result, automatically iterating a training model and forming closed-loop learning; according to the system, real-time association medical new discovery is supported through a dynamic knowledge graph, and hospital information system data are not effectively integrated; through cooperative work of the rule engine and the AI model, clinical medical quality and efficiency are improved.

The invention relates to the technical field of treatment scheme optimization, and provides an emergency treatment critical patient first-aid path optimization method and system based on big data, and the method comprises the steps: building a standardized event sequence through extracting historical diagnosis and treatment data of a hospital information system, and analyzing the time sequence correlation between events through a graph structure mining technology, key diagnosis and treatment nodes and high and low frequency paths are identified, and a layered first-aid path state transition network is constructed based on the key diagnosis and treatment nodes and high and low frequency paths. When a real-time patient is processed, the system matches an electronic health record of the patient with a network core state, dynamically plans a subsequent standard transfer path, and avoids a low-frequency risk path marked as a taboo, so that an emergency path optimization scheme of risk avoidance and path dynamic optimization is realized, and the treatment efficiency of emergency critical patients is improved. The treatment effect is improved, and the bad prognosis risk is reduced.

The invention discloses a myocardial infarction patient risk assessment model construction method and system, and particularly relates to the field of cardiovascular disease prediction, and the method comprises the following steps: I, collecting modal data of a hospital information system, an image filing system and wearable equipment, and integrating the modal data; according to the method, the accuracy of myocardial infarction risk prediction can be optimized, risk assessment not only depends on statistical characteristics, but also fuses pathophysiological mechanisms, the clinical interpretability of prediction is improved, the limitation that a traditional data-driven method is only based on correlation modeling is avoided, the optimal treatment scheme can be dynamically adjusted, data-driven personalized medical treatment is achieved, and the risk assessment efficiency is improved. The scientificity of intervention decision making is improved; the method can quickly adapt to new patient data, improve the individualized prediction ability, improve the data utilization rate, enhance the generalization ability of the model for different patient groups, avoid training the whole model from zero, greatly reduce the consumption of computing resources, make up for the problem of insufficient data of a single patient, and improve the prediction ability of rare cases.

The invention relates to a coronary artery risk prediction method and system for a coronary heart disease patient, and aims to solve the limitation that a traditional static evaluation model is difficult to integrate multi-source dynamic data. Collecting multi-source heterogeneous data of a patient from a hospital information system, a gene database and wearable equipment; the method comprises the following steps: respectively extracting key features through a special processing module: analyzing unstructured text and image plaque features by adopting a deep learning model, calculating a multi-gene risk score, capturing a physiological parameter attenuation trend by utilizing time sequence analysis, constructing a three-stage cascade integration model, screening high-contribution features at the first stage, and constructing a three-stage cascade integration model; the secondary stage integrates space-time dynamic characteristics through an attention mechanism network, the final stage integrates static and dynamic factors to generate a dynamic risk score, a visual risk trend chart and a high-risk factor thermodynamic diagram are output, and real-time early warning and linkage are performed through a medical terminal to generate a personalized intervention scheme. According to the method, collaborative analysis and dynamic risk tracking of multi-dimensional data are realized, and the early warning capability and the clinical decision-making efficiency are remarkably improved.

The invention belongs to the technical field of man-machine interaction, and discloses a diagnosis and treatment auxiliary system and method based on a voice gesture interaction technology, and the system comprises a voice recognition module which is used for collecting voice information; converting the voice information into text information; inputting the text information into the large language model to obtain a voice system instruction, and sending the voice system instruction to the data processing and interaction module; the gesture recognition module is used for collecting diagnosis and treatment scene information; identifying a preset operation gesture in the diagnosis and treatment scene information; mapping a preset operation gesture into a gesture system instruction and sending the gesture system instruction to the data processing and interaction module; the data processing and interaction module is used for receiving and executing a voice system instruction or a gesture system instruction; generating a calling instruction according to the voice system instruction or the gesture system instruction and sending the calling instruction to the network communication module; converting the received medical record data into diagnosis and treatment data in a display format; the network communication module is connected with a hospital information system; the calling module is used for calling medical record data after receiving a calling instruction of the data processing and interaction module and sending the medical record data to the data processing and interaction module; the display module is used for displaying the diagnosis and treatment data. The problem that a doctor cannot conveniently operate a computer to check patient data due to the sterility principle in the diagnosis and treatment process is solved, the diagnosis and treatment efficiency is improved, infection control is facilitated, and the sterility principle is met.

The invention discloses an inpatient falling early warning and intervention method based on environmental perception, and the method comprises the steps: continuously obtaining the video frames of a patient through a camera, and extracting the coordinates and confidence of seventeen key points through a pre-training deep learning posture estimation model; respectively processing the time sequence and the space-time diagram by using a hierarchical time sequence convolutional network branch and a graph neural network branch, and generating a visual behavior feature vector; in the same risk assessment period, bidirectional cross-modal attention fusion is carried out on the physiological feature vector and the visual feature vector collected by the wearable device, and a multi-modal fusion feature vector is output; then combining historical state features and static archive features in a hospital information system, carrying out unified modeling through a Transform encoder, and mapping to fall risk probability distribution; and when the risk is judged to be high, an intervention instruction is immediately issued to an intelligent sickbed or ward falling prevention execution mechanism through an Internet of Things protocol, and a perception-decision-intervention closed loop is completed.

The invention provides an intelligent infusion monitoring method and system based on multi-sensor fusion, and relates to the technical field of intelligent infusion monitoring. The method comprises the following steps: acquiring infusion process data through a visual sensor, a weight sensor and an infrared sensor; aligning the time sequence labels and synchronously generating a fusion data stream; extracting a liquid level height characteristic, a weight change rate and a real-time dripping speed value, and carrying out dynamic weighting calculation; acquiring infusion doctor's advice data of a hospital information system, and extracting a preset dripping speed range and a total liquid amount; calculating the residual infusion time based on the weighted data and the total liquid amount correlation; when the weighted real-time dripping speed value deviates from a preset range, the weighted weight change rate is not matched with the liquid level height trend or the dripping speed pulse signal is lost, an early warning instruction is generated; and the remaining time and the early warning state are visually displayed at the nurse station terminal. Through multi-source data fusion and cross validation, the problems of incomplete monitoring, time sequence dislocation and early warning lag are solved, and infusion safety and management efficiency are improved.

The invention provides a method for constructing and representing a pneumonia time sequence knowledge graph, and belongs to the technical field of big data and knowledge graphs, and the method comprises the steps: firstly obtaining pneumonia related data from a hospital information system, a vertical medicine website and a biomedical information ontology system (BIOS); then, constructing a pneumonia clinical time sequence knowledge graph, and converting serialized text data into the time sequence knowledge graph by combining the obtained data with a time relationship; and then constructing a pneumonia time sequence knowledge graph representation model (CTKGR), finally designing an experiment for verification, and evaluating the performance of the CTKGR model from two aspects of link prediction and medical tasks. Compared with other knowledge graph representation models and text models with excellent performance in clinical text representation, the effectiveness and generalization of the method are verified.

The invention relates to a smart hospital collaborative diagnosis and treatment management method and system, and the method comprises the steps: receiving the appointment registration information of a patient, building a data mapping relation with a hospital information system based on the appointment registration information, and generating a corresponding treatment identifier; after the patient arrives at the hospital, information of the sign-in patient is collected, a sign-in sequence is established, and according to appointment registration information and diagnosis and treatment service data parameters corresponding to the sign-in patient, a treatment serial number is dynamically distributed and a waiting prompt is pushed; after the doctor receives the diagnosis, generating an examination item list based on the preliminary diagnosis information input by the doctor, obtaining the real-time state and queuing data of equipment required by each examination in the examination item list, constructing an execution time window of examination items, generating an optimal examination sequence, and pushing an examination guide path to the patient terminal; and after the patient finishes seeing a doctor, generating a personalized propaganda and education task list according to the diagnosis and treatment information of each stage, and pushing the personalized propaganda and education task list to the patient terminal. The method has the effect of improving the hospital operation efficiency.

The lung analysis system based on the parameter response diagram and the image generation system is realized. The system is composed of a data collection and management module, an image registration module, a controllable image generation module, a parameter response diagram analysis module and a disease course development evaluation module. The data collection and management module is in butt joint with a hospital information system and CT image collection equipment. The image registration module receives a double-phase CT scanning image of a patient to obtain a registered double-phase CT image; the controllable image generation module receives a single inspiration phase CT scanning image of a patient as input to generate a registered expiration phase CT scanning image; the parameter response diagram analysis module obtains multiple parameter response diagram analysis and evaluation results of organic lesion evaluation of the lung of the patient; the disease course development evaluation module predicts the lung function condition of the patient. Therefore, the problems that when a parameter response diagram method is used, biphasic CT collection is difficult, and the ability to evaluate the disease course development of a patient is lacked are solved.

The invention provides an AI-based medical record diagnosis and surgical operation code intelligent generation method and system, and relates to the technical field of medical information, and the method comprises the steps: obtaining multi-modal data from a hospital information system, including a text medical record, a medical image, a surgical video and a pathological report; entities and relationships are extracted through a multi-modal fusion model, and a hospital-level disease knowledge graph containing space-time dimensions is constructed; training a coding rule generation model and a verification model based on a reinforcement learning framework, and generating candidate codes and confidence scores thereof; and correcting the candidate codes according to manual feedback, and dynamically updating the knowledge graph and the rule base by using the graph neural network. Accurate disease diagnosis codes and surgical operation codes are generated in an intelligent mode, and the accuracy of the codes can be remarkably improved through automatic medical record code recommendation.

The invention discloses a senile syndrome intelligent scientific research data management method and system, and relates to the technical field of data processing. The method comprises the following steps: integrating structured data of a hospital information system, analyzing an unstructured text, and extracting a key entity and a time sequence relationship to obtain multi-source patient structured data and time sequence feature data; performing cleaning, standardization and term normalization on the data based on the elderly syndrome proprietary domain ontology; a special disease data matrix is generated by dragging variables, and a scientific research queue is rapidly screened in combination with a Boolean logic grouping rule; dynamically adjusting random forest model parameters by adopting a wild horse optimization algorithm, training a high-risk prediction model based on historical data, and outputting a real-time high-risk case; combining the SHAP value quantization variable contribution degree and statistical test to generate a scientific research report; according to the method, the problems of medical scientific research data islands, non-uniform terms and poor model interpretability are solved, and efficient integration, accurate prediction and interpretable analysis of the senile syndrome data are realized.

The invention discloses an operation and maintenance knowledge base analysis system and method based on deep learning, and is applied to unified operation and maintenance management of a hospital information system and medical equipment. The system collects operation and maintenance data such as operation logs, monitoring indexes, alarm events, work orders, maintenance records and the like, constructs a minute-level time window through equipment identification and technology triggering time, and merges multi-source heterogeneous data into operation and maintenance sub-events and operation and maintenance events. Log, index and text feature extraction is performed on each operation and maintenance sub-event, multi-modal feature fusion is performed to generate an operation and maintenance event feature vector, the operation and maintenance event feature vector is stored in a vector index database and is associated with an operation and maintenance knowledge graph, and similar historical event retrieval and root cause and processing scheme recommendation of a new operation and maintenance event are realized. In combination with the adoption and processing results of recommended schemes by operation and maintenance personnel, the system performs hierarchical management on the operation and maintenance knowledge and performs incremental updating on the feature modeling network, so that the operation and maintenance knowledge retrieval accuracy and the fault positioning efficiency are improved.

The invention discloses a cancer pain treatment pain assessment method and system based on intelligent data processing, and relates to the field of treatment assessment, and the method comprises the steps: 1, obtaining basic pathological data, medication records and real-time vital signs of a patient in real time through a hospital information system, extracting symptom features, and carrying out the calculation of the symptom features; identifying the self-contained pain text of the patient, extracting the pain event characteristics, and matching the symptom with the pain event characteristics to form a pain correlation factor matrix; 2, constructing a pain prediction model based on a time sequence convolutional network, and generating a multi-dimensional data acquisition trigger instruction; a mode of combining a time sequence convolutional network and a multi-head self-attention mechanism is adopted, a high-sensitivity acquisition window of a pain event is automatically identified, discontinuous event interference is inhibited through a time decay factor, and a high-weight time period is reserved by utilizing sparsification, so that a data acquisition time period with a high contribution value is automatically obtained; and the recognition precision of the key pain time period is remarkably improved.

The invention relates to the technical field of medical health data intelligent processing, in particular to a gestational diabetes patient family doctor management method and system based on artificial intelligence, and the method comprises the steps: 1, collecting patient data through a dynamic glucometer, a fetal heart monitor and a hospital information system; 2, aligning the multi-source data according to timestamps to construct a time sequence data set; 3, generating a complication risk score containing a ketoacidosis risk and a giant child risk; 4, generating a personalized sugar control scheme based on the risk score and a pre-constructed medical knowledge graph; 5, integrating opinions of multiple parties to generate a final edition scheme; and step 6, updating deep learning model parameters based on feedback data. According to the mode, the treatment efficiency and accuracy are improved, the control scheme can be adjusted in real time, and the health and safety of the patient in the pregnancy period are ensured. In addition, a feedback mechanism can continuously optimize the system, a closed loop is formed, and the management effect is further improved.

The invention discloses a spatio-temporal trajectory fusion-based closed-loop monitoring method and system for drug-resistant bacteria in a hospital. The method comprises the following steps: collecting and processing strain drug sensitive test results and patient clinical index data in a hospital information system, and screening out key features through an improved genetic algorithm; wherein the clinical index data of the patient comprises original spatio-temporal trajectory data and nursing operation records; analyzing spatio-temporal trajectory data of the patient according to the key features, combining with drug sensitivity spectrum consistency, and constructing a propagation network model by using a graph convolutional network to identify infection source nodes and corresponding topological influence; and generating an early warning signal based on the topological influence of the infection source node, and continuously optimizing prevention and control measures by dynamically adjusting model parameters and a real-time feedback mechanism. By implementing the method provided by the invention, accurate analysis and real-time prevention and control of a drug-resistant bacterium transmission link can be realized.

The invention relates to the technical field of medical artificial intelligence, and discloses an end-stage nephropathy curative effect evaluation and complication risk intelligent prediction system, which comprises a data acquisition module, which is in communication connection with a hospital information system, physiological monitoring equipment and a user terminal to obtain multi-source data of a patient, the multi-source data comprises structured physiological index data, laboratory detection data, patient medical history and other unstructured text data; and the feature fusion module is used for receiving the multi-source data from the data acquisition module, performing standardization processing on the structured physiological index data, converting the unstructured text data into a vector form in an embedded encoding manner, splicing the vector form with the structured features, and constructing uniform time sequence input features. According to the method, by introducing an incremental learning mechanism and a feature attribution method, the comprehensive effects of dynamic model updating, explainable prediction results, accurate feature characterization and controllable stability are achieved.

The invention discloses a tear total IGE detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring ocular surface images, physiological parameters and environmental allergen data, and acquiring historical medical data from a hospital information system; preprocessing and feature extraction are performed, patient symptom description is analyzed in combination with a natural language processing technology, and related image block features are recognized; adjusting a space-time attention weight based on frequency domain analysis and transfer learning, inputting a lightweight Transform model, and predicting a tear total IgE concentration and an allergy risk score; whether a trace tear sampling program is started or not is judged, and the actually measured concentration of total tear IgE is detected through a disposable micro-fluidic chip; and integrating all information by using a Bayesian adaptive filtering algorithm to determine the total tear IgE level. By implementing the method provided by the invention, the sampling amount is extremely small, the detection time is short, the environmental interference can be dynamically corrected, and the absolute concentration value is output, so that the dual requirements of clinical precise diagnosis and treatment and home monitoring are met.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

The embodiment of the invention provides a continuous breath sound monitoring application system, which is oriented to double versions of a medical professional version and a civil public version, can continuously collect and analyze breath sound, adopts a multi-level label system to carry out classified labeling, and combines expert labeling and machine learning to improve the recognition accuracy. And meanwhile, a perfect database architecture and a local / cloud mixed storage strategy are provided to ensure reliable management of data, and data security compliance is ensured through encryption and access control. The system is also integrated with an interactive teaching module to meet the requirements of professional training and popular science popularization, and is equipped with a multi-parameter linkage grading alarm mechanism to provide real-time early warning. In addition, the system has good expandability and can be integrated with a hospital information system (HIS) and a remote medical platform through standard interfaces.

The invention relates to the technical field of medical big data analysis, and discloses a hospital medical record data anomaly analysis method based on big data. According to the method, multi-source medical record data including diagnosis and treatment event records, nursing operation sequences and patient sign monitoring streams in a hospital information system are collected, and structured reforming is carried out. And constructing a medical record element interaction graph based on the reformed data, and detecting an abnormal state conduction path between the data elements through the graph model. And performing hierarchical traceability calculation according to the detected conduction path, positioning an abnormal source and generating a positioning report, and finally outputting a calibration instruction set according to the report. According to the method, the abnormal conduction chain is systematically identified from the correlation perspective, and the root source of the abnormality can be automatically and accurately traced, so that the logicality and the positioning accuracy of the medical record data abnormality analysis are improved.

The invention discloses a patient perioperative period adverse event risk prediction system based on electronic medical record data, and the system comprises a data layer which is used for connecting with each information system of a hospital, collecting, cleaning and storing the electronic medical record data of a hospitalized patient, and a feature layer which is used for extracting features related to risk prediction from the original medical record data stored in the data layer. The model layer is used for constructing, training and optimizing a patient perioperative period adverse event risk prediction model, and the application layer is used for carrying out perioperative period adverse event risk prediction on a new patient based on the risk prediction model and sending out an early warning prompt to a prediction result with the prediction probability being 0 or above. And the feedback layer is used for regularly comparing prediction evaluation sent by the perioperative period adverse event risk prediction model of the patient with actual condition data of the patient, and carrying out model training and optimization. According to the invention, the risk of the perioperative adverse events of the inpatient can be effectively predicted, comprehensive risk information early warning is provided for doctors, and the medical service quality is improved.

The invention relates to an old inpatient intestinal preparation failure risk prediction method and system, and the method comprises the steps: collecting the multi-dimensional electronic medical data of a patient from a hospital information system (HIS), a laboratory information system (LIS) and a pharmacy information system (PIS), carrying out the cleaning, missing value filling and standardization preprocessing of the original data, so as to construct a high-quality data set, and carrying out the prediction of the intestinal preparation failure risk of the old inpatient. Generating three core risk characteristics based on clinical knowledge: an age-weighted complication index, a specific drug use mark and a key physiological index anomaly mark; inputting the features into a preset rule engine, performing weighted calculation according to a fixed weight coefficient to obtain a comprehensive risk score, finally dividing patients into low, medium and high risk levels according to a risk threshold value determined by historical data, and outputting a result and storing the result into a database for clinical retrieval and early warning. The method can effectively evaluate the intestinal preparation failure risk of the elderly patient, and provides an objective basis for clinical intervention.

The invention discloses a follow-up visit management system for an inflammatory bowel disease patient, and relates to the technical field of medical management. Comprising a patient management module which is in dynamic butt joint with a hospital information system, synchronizes patient data and endoscopic examination results in real time through an HL7 protocol, and establishes an electronic file containing an inflammatory bowel disease specificity tag; the diagnosis and treatment result module is used for storing structured data with temporal markers, including medication time sequences recorded by three groups of timestamps, administration routes and drug categories, auxiliary examination results accompanied with digestive tract pathological section images and hidden space feature vectors extracted from the images through a convolutional neural network; and the diet module is used for carrying out multi-level classification on diet photos uploaded by the patient through an image recognition unit based on MobileNetV3, and outputting food types and intake estimated values. According to the invention, by integrating the multi-dimensional data of the patient, the disease development trend is predicted, and personalized follow-up visit management is provided.

The invention relates to the technical field of medical information, and discloses a multi-dimensional verification medical record home page data real-time quality control and coding method, which comprises the following steps: S1, acquiring medical record home page data from a hospital information system or an electronic medical record system, the medical record home page data comprising structured data, unstructured data and time series data; and S2, classifying the home page data of the medical record according to a disease category classification standard, dividing the home page data of the medical record into different disease categories, and constructing a multi-dimensional tensor model related to the disease category for each category for subsequent data processing and feature extraction. By introducing an incremental relation updating and graph embedding mechanism, dynamic optimization and automatic expansion of the knowledge graph are realized, the problems of updating lag, limited expansion and out-of-control structure under high-frequency data of a traditional coding system are solved, and the real-time performance and the intelligent level of medical record home page coding and quality control are remarkably improved.