94 results about "Diagnosis standards" patented technology

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 auxiliary medical diagnosis, and particularly discloses a three-branch diagnosis and evaluation method and device based on multi-modal remote medical data. Multi-modal remote medical data of a patient is collected and input into three diagnosis and evaluation models for analysis, and if a diagnosis result is clear, whether the patient is ill or not is determined, and a corresponding result is directly output; and if the diagnosis result is not clear, further evaluating the disease risk of the patient and outputting an evaluation result. The method not only can effectively diagnose the illness state and the non-illness state of the patient, but also breaks through the limitation that a traditional intelligent diagnosis model cannot accurately evaluate the illness risk when the illness state of the patient is uncertain or incompletely meets the diagnosis standard.

The invention discloses a skin cancer pathological image analysis system and method based on a Transform model. The system comprises an image acquisition and quality control module, a multi-scale preprocessing module, a hierarchical feature extraction module, an intelligent diagnostic reasoning module, a knowledge graph aided decision-making module, a result output and feedback module and a federal learning training module. According to the method, multi-spectral image acquisition and quality control are carried out, multi-modal enhancement and dyeing standardization preprocessing are carried out, pathological features are extracted in a layered manner by using an improved Transform model, subtype identification and malignancy degree evaluation are realized in combination with multi-task learning, uncertainty is quantified by means of Monte Carlo dropout, cases and guidelines are associated through a knowledge graph, privacy is protected through federal learning, and the model is optimized. According to the scheme, the diagnosis efficiency and accuracy are greatly improved, the model interpretability is enhanced, various clinical scenes are adapted, diagnosis standardization is promoted, and improvement of basic medical capacity is assisted.

The invention discloses a large model hospital infection judgment method and system based on an intelligent agent, and relates to the technical field of risk assessment, and the method comprises the steps: obtaining a target physiological feature of a target patient; the target physiological feature at least comprises one of a disease course description, an examination result and an inspection result of the target patient; retrieving a hospital infection knowledge map according to the target physiological features to obtain a corresponding target sub-map; wherein the hospital infection knowledge graph is constructed according to a hospital infection diagnosis standard document; generating a relation vector set according to the target sub-atlas to obtain a target relation vector set; and inputting the target physiological features and the target relationship vector set into a pre-trained hospital infection prediction model to obtain a target infection outcome, thereby reducing the data set screening difficulty of the hospital infection prediction model, and improving the prediction accuracy of hospital infection prediction.

The invention discloses a heat supply energy consumption diagnosis and analysis system fusing a big language model and cloud-mist edge collaboration, and the system comprises an edge processing layer which is used for monitoring the energy consumption related data and heat supply operation data of a heat supply system diagnosis object; the fog processing layer is used for extracting strong correlation data characteristics influencing energy consumption and establishing an energy consumption prediction model of a heat supply system diagnosis object; the analysis module is also used for establishing an energy consumption anomaly analysis knowledge graph according to the heat supply operation data and energy consumption anomaly analysis mechanism knowledge; the cloud processing layer is used for training a heat supply system energy consumption abnormity diagnosis large model according to the energy consumption predicted value of the heat supply system diagnosis object in combination with a preset energy consumption diagnosis standard value; and the server is also used for obtaining an energy consumption abnormality diagnosis type and an abnormality diagnosis object according to the heat supply system energy consumption abnormality diagnosis large model, performing question and answer intention recognition and constructing an energy consumption abnormality analysis question and answer agent based on the large language model in combination with the energy consumption abnormality analysis knowledge graph, and generating an energy consumption abnormality analysis and energy-saving recommendation scheme answer.

The invention provides a depression analysis scoring method based on knowledge distillation and retrieval enhancement generation, belongs to the field of depression medical assessment, and utilizes a large language model to convert emotion self-description data of volunteers into dialogue data between doctors and the volunteers. And using a large-scale reasoning large language model to perform emotion reasoning and scoring on the part representing the volunteer in the dialogue data according to a medical scale rule. Performing supervised fine tuning on the reasoning large language model with relatively small parameter scale by using the dialogue data and the corresponding reasoning process and scoring result, and constructing a large language model specially used for depressive emotion analysis; the method comprises the following steps: constructing a CCMD-3 diagnosis system into a vector database, and performing similarity retrieval on dialogue data in a real scene and the vector database; and combining the most relevant diagnosis standard obtained by retrieval with dialogue data in a real scene, jointly inputting into the depressive emotion big language model, and finally outputting an analysis result and a score of a depressive symptom.

The invention relates to the technical field of psychological prediction, and particularly discloses a webpage calculator construction and evaluation method for anticipated sadness occurrence probability of a family caregiver of a cancer patient, and the method comprises the steps: collecting the related data of the family caregiver and the cancer patient, determining the diagnosis standard of anticipated sadness through potential profile analysis, and evaluating the anticipated sadness occurrence probability of the family caregiver. And screening key predictive variables by using LASSO-logistic regression, constructing a predictive model, and developing an online webpage calculator based on a shiny technology. According to the method, the expected sadness risk of the family caregiver can be rapidly evaluated, the method has good distinction degree, calibration degree and clinical practicability, and a scientific basis is provided for medical staff to identify high-risk family caregivers and provide targeted support.

The invention discloses an intelligent respiratory disease detection system based on lung function measurement indexes, and relates to the technical field of intelligent medical detection. The problems that diagnosis standards are not uniform and depend on experience, primary doctors are prone to missed diagnosis or misdiagnosis due to insufficient experience, and the efficiency of the detection process is low are solved. According to the method, a machine learning model is constructed based on lung function indexes, parameters are optimized through five-fold cross validation, automatic classification of asthma, COPD and ILD / DPLD is achieved, the overall diagnosis accuracy rate reaches 81.3%, the limitation that traditional diagnosis depends on doctor experience is broken through, and the basic medical diagnosis capacity is especially improved. Healthy people are rapidly eliminated through the FVC / FEV1 ratio, invalid calculation is reduced, meanwhile, multi-model weighted fusion is adopted for complex cases, diagnosis robustness is improved, it is ensured that diagnosis results conform to medical logic, the recognition degree of doctors is enhanced, a data basis is provided for clinical research and curative effect tracking, and respiratory disease diagnosis is promoted to develop towards the direction of automation and precision.

The invention discloses a monitoring and diagnosing system for a tower drum of a wind generating set. According to the method, the information of the tower drum of the wind generating set is acquired from multiple dimensions, a system architecture with information acquisition, feature extraction, intelligent diagnosis and autonomous learning functions is constructed, a set of complete diagnosis standards for the running state of the tower drum of the wind generating set is generated, the timeliness and accuracy of fault diagnosis of the tower drum of the wind generating set are improved, and the fault diagnosis efficiency is improved. The influence of wind load on tower drum fault diagnosis is reduced, and a self-adaptive diagnosis strategy of the tower drum of the wind generating set is realized.

The invention discloses a diagnostic marker combination for senile weakness and application thereof, and relates to the technical field of biological medicines. The diagnostic marker combination comprises a GDF15 protein, an FABP4 protein, an ITGA11 protein, an LEP protein, an REN protein and a CTSB protein. Aiming at the urgent medical problems that no unified elderly weakness diagnosis standard exists in the world at present, a blank exists on the basis of blood diagnosis indexes, and serious obstacles are brought to community screening, clinical decision making and health management, on the basis of sample screening and sample verification research, a systematic epidemiology method is applied, and the elderly weakness diagnosis standard is established. The biomarkers are identified in a multi-level and omnibearing manner, and a diagnostic marker combination for senile weakness and a diagnostic product based on biomolecules are developed so as to be applied to community screening and clinical diagnosis and lay a key foundation for disease prevention and treatment and clinical application.

The invention discloses a plasma protein-based stomach cancer early-stage multi-molecular diagnosis model construction method, and aims to solve the problems that the existing stomach cancer early-stage diagnosis standard and minimally invasive or non-invasive method are imperfect, the sensitivity and specificity of the existing diagnosis molecular marker are low and the like, an Olink proteomics PEA technology is utilized and a specific detection panel is selected to construct a stomach cancer early-stage multi-molecular diagnosis model. And identifying the protein markers of gastric cancer, early gastric cancer and gastric precancerous lesions in a discovery queue (n = 88). In a discovery queue, a group of multi-protein molecular marker models consisting of 13 proteins are successfully identified by utilizing an LASSO regression model, and the multi-protein molecular marker models have good diagnostic performance for distinguishing gastric cancer, early gastric cancer and pregastric lesion and contrast in the discovery queue and verification pair. Furthermore, in a verification queue sample, the diagnosis model also shows good diagnosis performance, and the marker model is expected to become a powerful tool for early diagnosis of gastric cancer.

The application discloses a diagnosis data processing method and device and a storage medium, relates to the technical field of data processing, and comprises the following steps: acquiring a heart-lung auscultation sound signal collected by a collection device and a traditional Chinese medicine (TCM) diagnosis standard description received based on an interactive interface; determining a western medicine quantitative diagnosis index according to the heart-lung auscultation sound signal, and correlating a TCM syndrome differentiation type according to the TCM diagnosis standard description and the western medicine quantitative diagnosis index; and generating and outputting a TCM medication recommendation scheme corresponding to a diagnosis result according to the western medicine quantitative diagnosis index and / or the TCM syndrome differentiation type. The application provides strong objective evidence and quantitative reference by differentiating the TCM syndrome differentiation type according to the auscultation sound. When diagnosing and treating diseases, the TCM highly depends on and integrates the auscultation result, and takes the auscultation result as one of key bases for differentiation, prescription selection, treatment effect evaluation and prognosis.

The invention discloses a sacroiliac arthritis SPARCC automatic scoring method, system and device and a medium, and relates to the technical field of medical image artificial intelligence. The method comprises the following steps: acquiring a target image; the target image is a patient sacroiliac joint oblique coronal STIR sequence MRI image conforming to the ASAS diagnostic standard; preprocessing the target image to obtain a training set after data enhancement; based on the training set, parameter tuning is carried out on a pre-training network composed of an asymmetric encoder-decoder architecture, and a trained automatic scoring model is obtained; inputting all MRI images of the sacroiliac joint oblique coronal STIR sequence into the automatic scoring model, predicting inflammation SPARCC scores layer by layer, outputting six layers of images with the highest scores, and accumulating the six layers of images into a total score. According to the method, the dependence on the annotation data can be reduced, the overfitting problem of the CNN model is solved, and meanwhile, the interpretation deviation caused by subjective difference of scorers is reduced.

The invention discloses an AI-based schizophrenia clinical thinking training simulation system, which relates to the technical field of artificial intelligence, and is characterized by comprising the steps of constructing a dynamic knowledge base, receiving and preprocessing multi-modal input, performing standardized mapping by extracting keywords, and then performing judgment of a rule layer and a semantic layer. A student question method and a standard answer method in the dynamic knowledge base are sent into a model for response generation, then four-dimensional quantitative scoring and clinical operation multi-dimensional scoring are carried out, error codes and positioning sentences thereof are output, and finally personalized training courses are recommended to students; and hot-updating the dynamic knowledge base continuous optimization model. According to the method, four dimensions of the ICD-10 diagnostic standard are converted into a quantitative scoring algorithm capable of being automatically executed, quantitative assessment of clinical operation specifications is integrated, a diagnosis and operation double-track evaluation system is constructed, the problems that a traditional method is lack of evaluation standards and high in subjectivity are solved, and the evaluation efficiency is improved. Therefore, the evaluation of the clinical thinking ability of the medical students becomes objective, comprehensive and evidence-based.

The invention relates to an intelligent tongue picture analysis system and method. The system comprises a high-definition camera, an image processing unit, an image segmentation module, a color space conversion module, a feature extraction module, an analysis module, a classification module and a diagnosis matching module. The system shoots a tongue image through the high-definition camera, the image processing unit carries out image preprocessing, the image segmentation module separates a tongue area, the color space conversion module extracts color features, and the feature extraction module calculates color mean and variance and shape features. The analysis module analyzes tongue coating distribution, thickness and color, the classification module performs feature classification based on a training data set, and the diagnosis matching module matches the features with a diagnosis standard to obtain a conclusion. The system provides scientific and objective tongue picture analysis, improves the diagnosis efficiency, and promotes the modernization and intellectualization of traditional Chinese medicine diagnosis.

The application discloses an out-of-hospital hierarchical diagnosis method based on IBS diseases, and relates to the technical field of medical diagnosis, and comprises the following steps: S1: acquiring multi-dimensional information related to IBS diseases provided by a patient in an out-of-hospital scene; S2: performing standardization preprocessing on the out-of-hospital multi-dimensional information to obtain feature data that can be input into an algorithm model; and S3: inputting the standardization feature data into a preset IBS hierarchical diagnosis algorithm model to output an IBS disease hierarchical diagnosis result of the patient. The out-of-hospital hierarchical diagnosis method based on IBS diseases integrates multi-dimensional out-of-hospital information, eliminates data errors by combining standardization preprocessing, adopts multi-feature screening and model optimization, references the Rome IV diagnostic standard to train the model, simultaneously judges the diagnosis reliability through the confidence, reduces the misdiagnosis and missed diagnosis rate, the model iteration updating mechanism can continuously improve the diagnosis accuracy, and adapts to the change of clinical requirements.

The invention belongs to the technical field of animal model construction, and particularly relates to a construction method and application of a bronchiectasis animal model. According to the method, the bronchiectasis animal model is constructed in a manner of alternately infecting the animal with the self-separated pseudomonas aeruginosa and injecting the protease into the airway of the animal, so that the stable bronchiectasis animal model is successfully obtained, and the modeling success rate is relatively high. It is confirmed through imaging and pathological detection means that the constructed bronchiectasia animal model meets the diagnostic standard of bronchiectasia, chronic pseudomonas aeruginosa airway infection in bronchiectasia can be accurately simulated, structural changes of bronchiectasia are copied, and auxiliary clinical research and application are facilitated.

Embodiments of the present application disclose a disease diagnosis standard library construction method, device and equipment and a storage medium. The method can obtain first historical medical data from a target disease sample library; first image feature data of the first historical medical data is extracted to obtain a first key feature training data set, first short-term memory parameters and first long-term memory parameters are obtained based on the first key feature training data set and a detection standard model; second historical medical data is obtained; second image feature data of the second historical medical data is extracted to obtain a second key feature training data set, second short-term memory parameters and second long-term memory parameters are obtained based on the second key feature training data set and the detection standard model; target disease diagnosis standard data in a time dimension is obtained based on the short-term memory parameters and the long-term memory parameters, and a disease diagnosis standard library is constructed based on the target disease diagnosis standard data. The scheme can improve the accuracy of disease detection.

The invention discloses a system for endometriosis diagnosis and a computer storage medium. Aiming at major adjustment of endometriosis diagnosis standards, biomarkers and ploidy information of circulating endometrial cells in easily available samples such as peripheral blood of a patient are detected, and comprehensive risk assessment is performed by utilizing an optimization algorithm model, so that the diagnosis accuracy of endometriosis is improved. The invention provides an objective and quantifiable noninvasive diagnostic tool adapted to the adjusted standard, and the method solves the problem of missed diagnosis caused by false negative in the existing surgical diagnostic method, and has important clinical application value, social benefit and market prospect for improving the overall diagnostic level of endometriosis.

The invention belongs to the technical field of transmission casing bearing fault diagnosis, and particularly relates to a typical small bearing weak fault comprehensive diagnosis standard construction method, which comprises the following steps: respectively carrying out inner ring, outer ring and rolling body bearing fault simulation tests; a filtering frequency band is selected based on a weighted kurtosis value improved Protugram algorithm, an envelope spectrum is extracted after filtering and noise reduction, and a bearing fault diagnosis standard threshold value is established based on an envelope spectrum characteristic value method; based on a preset fault test, comprehensively analyzing envelope spectrum characteristic values of different faults and different measuring points, and establishing a comprehensive early warning diagnosis standard; based on a failure fault test, envelope spectrum characteristic values of different faults and different measuring points are comprehensively analyzed, and a comprehensive failure diagnosis standard is established. And after judging that the bearing has no fault, respectively carrying out a preset fault test and a failure fault test, and establishing an early warning diagnosis threshold value and a failure standard threshold value for the fault characteristic sensitive measuring points on the basis of measuring point optimization analysis, thereby realizing early warning and failure judgment of the bearing fault.

The invention relates to the field of livestock breeding, and discloses an automatic dairy cow veterinary management and analysis system, which comprises a standard management module, a disease declaration module, a treatment execution module and a data analysis module, and the modules realize data interaction and instruction transmission through a unified data transmission protocol. The reference module is used for providing a standardized disease diagnosis and treatment scheme reference and supporting dynamic updating; the disease declaration module is used for realizing accurate input and case filing of cattle disease information; and the treatment execution module is used for matching a standard prescription according to the case information and executing a traceable treatment process. According to the system, a'diagnosis-treatment 'whole-process standardization system is constructed through the standard management module, a standard disease library defines disease diagnosis standards from three dimensions of'typical symptoms, detection indexes and identification key points', an attached symptom picture library assists visual judgment, and misdiagnosis and missed diagnosis caused by experience differences are reduced.

The invention discloses a sepsis risk multi-modal prediction method and system based on deep learning, and belongs to the technical field of intelligent analysis of medical data, and the method comprises the following steps: a multi-modal data collection step: collecting vital sign sequences, bedside instant inspection, laboratories, microbial culture, images and medical history data; in the time sequence feature coding step, a time sequence embedded vector is extracted through a multi-scale time sequence coding network; the cross-modal fusion prediction step is based on the dynamic attention mechanism fusion features and predicts the sepsis occurrence probability; the organ function dynamic evaluation step is used for estimating a missing test result and calculating an organ function score; the risk layering early warning step outputs four-level risk layering results and feeds back optimized coding parameters, early warning can be output 4-8 hours before the diagnosis standard is met, and compared with qSOFA, the AUC is improved by 0.15 or above.

The invention discloses a plaque psoriasis severity grading system and a plaque psoriasis severity grading method. The plaque psoriasis severity grading system comprises a case inclusion module used for selecting patients meeting plaque psoriasis western medicine diagnosis standards; the ultrasonic examination module is used for carrying out ultrasonic examination on the typical diseased part of the patient by an ultrasonic physician to obtain a high-frequency ultrasonic index; the scoring module evaluates the skin lesion of the patient and gives an improved PASI score of local lesion; the model building module takes the local improved PASI score as a dependent variable, takes a high-frequency ultrasonic index as an independent variable, adopts a stepping method to screen out factors having significant influence on the local improved PASI score, and builds a severity grading model; the verification module performs internal verification on the constructed severity grading model by using a neural network multilayer perceptron, and evaluates the prediction performance of the model; and the output module is used for visualization and output. The invention provides a new and effective technical means for grading the severity of the plaque psoriasis, and has important clinical application value and popularization prospect.

The invention discloses a medical examination report analysis system and method based on big data, and relates to the technical field of medical data processing, the system comprises a data acquisition module, a preprocessing module, a storage module, an index correlation analysis module, an abnormity early warning module, a result generation module and a user interaction module, and the method comprises corresponding data processing and analysis steps. Multi-source data such as inspection reports, historical diagnosis and treatment, clinical cases and the like are collected through a standardized interface, are subjected to cleaning, standardization and structural conversion, and are accessed by means of a distributed storage architecture; an Apriori association rule algorithm is adopted to mine index association, a random forest and a neural network model are combined to realize risk assessment, and a three-level grading early warning mechanism is matched to push accurate suggestions. According to the method, traditional manual interpretation is replaced, the analysis efficiency and accuracy are improved, the problems of insufficient associated information mining, non-uniform diagnosis standards and the like are solved, medical institution scenes at all levels are adapted, interactive optimization is supported, clinical precise diagnosis is assisted, the burden of doctors is relieved, and the method has important popularization value for intelligent application of medical data.

The application belongs to the technical field of biological medicine, and particularly relates to application of a reagent specifically binding to OARD1 protein in preparation of a breast cancer diagnosis and / or prognosis evaluation product. The product detects the expression level of OARD1 protein in a breast tissue sample by the reagent specifically binding to OARD1 protein and carries out H-score scoring, takes a preset H-score score as a breast cancer positive diagnosis threshold, and judges whether the sample is breast cancer positive. Moreover, the product can distinguish high / low expression of OARD1 protein by a preset H-score score, constructs a prognosis prediction model in combination with TNM staging of a subject, and realizes evaluation of a breast cancer patient disease progression risk. The method is simple and easy to implement, the diagnosis process is safe and effective, is easy to be accepted by patients, diagnosis standards are unified, and is less affected by personal subjective factors, which shows that OARD1 protein has important significance for diagnosis or prognosis judgment of breast cancer.

The invention provides a method for verifying LA as a peripheral marker of depression, which comprises the following steps: step 1, collecting 11 plasma samples of juvenile depression patients meeting ICD-10 diagnostic standards before treatment and 11 healthy volunteer plasma samples of a juvenile control group, and detecting the LA content of peripheral plasma of each group; step 2, collecting plasma samples of 12 cases before treatment of adult depression patients conforming to ICD-10 diagnostic standards and plasma samples of 12 cases of healthy volunteers of an adult control group, and detecting the LA content of peripheral plasma of each group; 3, preparation of rats: 31 SD rats are selected; 4, a depression model rat is established through a CUMS method; 5, behavioral detection is conducted on the rats in the model group in the step 4, and finally 11 rats in the control group and 11 rats in the model group are obtained; and step 6, detecting the LA level in the peripheral blood of the model group rats and the control group rats. The invention proves that the LA can be used as an early-stage peripheral marker molecule for evaluating depression, and has important significance on psychiatric research and drug research and development.

The application discloses a kind of intelligent diagnosis and decision support method of coronary heart disease angina TCM syndrome factor based on HIS, it is related to medical information and TCM diagnosis and treatment technical field, the method will coronary heart disease angina TCM syndrome factor diagnostic criteria be deconstructed into standardization diagnostic item, build machine-readable weight rule base containing score, threshold and severity grading rule, response syndrome differentiation operation generates electronic collection form, receives structured data storage to the TCM syndrome factor fact table associated with patient main index and medical record, after structured data is submitted, trigger intelligent computing engine verification essential item, total score is accumulated, determine the establishment of syndrome factor and severity, through TCM diagnosis and treatment knowledge graph push personalized treatment or nursing suggestion in clinical business node, and continuously update rule base and knowledge graph through clinical data feedback.The application integrates corresponding function module to realize and deeply fuse with hospital information system, improves the standardization and efficiency of diagnosis and treatment, and has strong practicality.

The invention provides a health / exercise management system, which is characterized in that the system is applied to cerebrovascular disease supervision, and comprises a health management module used for collecting and recording health information, comparing the health information with a preset diagnosis standard to obtain a comparison result, and outputting a corresponding prompt according to the comparison result; the health management module is also used for collecting related scale questionnaires and recording health conditions according to the related scale and the questionnaires; the health management module is also used for receiving the recorded related diagnosis and treatment results of the cerebrovascular diseases and managing the recorded related diagnosis and treatment results; and the exercise management module is used for carrying out score evaluation on facial paralysis and upper limb and lower limb exercises according to the exercise video to be evaluated, and obtaining a detection result according to the score evaluation. Recording and feedback can be combined, and the universality from health recording to secondary prevention and the compatibility from health follow-up visit to function evaluation are met. User motion function self-test is realized, and user health self-test is facilitated.

The present application relates to the field of medical informatization and artificial intelligence technology, and specifically provides a medical record connotation quality control method and device based on a large language model, which has the following steps: S1, medical record document data access, document content preprocessing; S2, clear quality control content, construct quality control rule library; S3, construct medical knowledge base: based on clinical guidelines and evidence-based medicine evidence base, establish diagnosis standard, drug, treatment project, inspection project knowledge base; S4, entity extraction: use a large language model to extract diagnosis, drug, treatment project and inspection project name; S5, model reasoning: query the relevant information of the extracted entity in the knowledge base, according to the quality control rules, combine the queried relevant information with the medical document and submit them to the large model for reasoning to give the quality control result; S6, problem collection, model optimization. Compared with the prior art, the present application can significantly improve the intelligent level of medical record connotation quality control, effectively assist clinical doctors and quality control personnel to improve the quality of medical record writing, ensure medical safety, and improve hospital management efficiency.

The invention relates to the technical field of medical education, in particular to a teaching system for rescuing pediatric epilepsy, and the system comprises a pediatric model which is used for simulating the states of different types of epilepsy seizures and physiological parameters in different states; the teaching device comprises a teaching resource library, a teaching display module, an examination and evaluation module and medical equipment; the teaching resource library is used for providing teaching videos of etiology, pathology, diagnosis standards and first-aid processes of the pediatric epilepsy and displaying the teaching videos through the teaching display module; the medical equipment is used for rescue operation of the pediatric epilepsy; the examination and evaluation module is used for providing various examination scenes, scoring the operation process of the trainee and generating an evaluation report, so that various scenes of pediatric epileptic seizure are truly and reliably simulated by providing a teaching system capable of being operated and scored, rescue skills of the trainee are evaluated, and the training efficiency of the trainee is improved. And the diagnosis and first-aid treatment capabilities of the trainee on pediatric epileptic seizure are effectively improved.