22 results about "DIAGNOSTIC STANDARD" patented technology

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 relates to a dual-mode paper-based sweat chloride ion sensor, a preparation method and application of the dual-mode paper-based sweat chloride ion sensor in rapid diagnosis of cystic fibrosis, and belongs to the crossing field of a biomedical detection technology and electrochemical / optical sensing materials and devices. The dual-mode paper-based sweat chloride ion sensor is composed of a colorimetric detection module and an electrochemical detection module. According to the invention, by optimizing the collaborative design of the colorimetric module and the electrochemical module and combining the integrated process of the silver nitrate / potassium chromate sensitive layer and the screen-printed electrode, the detection sensitivity limit is remarkably broken through, and the integrated process from preliminary screening to clinical definite diagnosis is realized. More importantly, a low-cost cellulose substrate and a silk-screen printing process are adopted, the single-time detection cost is lower than 0.1 dollar, the 180-second quick response characteristic is combined, the method completely meets the economical, practical, high-sensitivity, good-specificity, simple and quick to operate and repeatable diagnosis standards proposed by WHO, and the method is suitable for instant detection scenes of basic medical institutions.

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 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.

The invention relates to a network (1) in a motor vehicle, wherein the network has n control devices (2), wherein the control devices (2) have software (7) which is configured to generate data for a first diagnostic standard and a second diagnostic standard, wherein the control devices (2) have at least one local memory area (8, 9), wherein the control devices (2) are connected to one another via at least one bus system (4), wherein the control devices (2) each have protocol software (11, 12) for the first diagnostic standard and for the second diagnostic standard, wherein a control device (2) is configured as a master (3) such that it defines the protocol software (11, 12) to be used of the other control devices (2). The invention also relates to a control device (2) and to a method for generating diagnostic data.

The invention provides an FPGA (Field Programmable Gate Array) fixed fault diagnosis method based on an improved qualitative graph model, and solves the problems of blindness and high cost of manual screening of test points and large search space, long time consumption and low resolution of a traditional qualitative graph model. The method comprises the steps of fault modeling and fault injection. Selecting a preliminary test point; normal and fault simulation; optimizing a diagnostic standard and protecting a genetic algorithm of an elite gene site; and the two qualitative graph models complete fault diagnosis. According to the method, adaptive probability crossover and variation are adopted; elite gene sites are protected; a select process is added; the fault propagation directed graph is modeled in a layered mode and then divided into layers, edge weights are analyzed and defined according to reliability, and small-world network nodes are defined based on information entropy. The global search capability and the convergence speed are improved, the diagnosis efficiency and the resolution are improved, and the problems that the fault probability and the fault propagation time are difficult to obtain and the fault information is difficult to quantify are solved. The method is used for optimizing test points and performing FPGA system fault diagnosis.

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.

This invention discloses a bolt tightening anomaly diagnosis method based on historical data and deep learning, belonging to the field of intelligent manufacturing technology. It solves the problems of excessive reliance on human expert experience, inconsistent subjective diagnostic standards, lagging and low coverage of anomaly identification, and lack of actionable diagnostic results in existing technologies. This invention constructs a dynamically updated statistical baseline model, establishes an objective deviation quantification scoring system for multi-dimensional tightening parameters, and introduces a hybrid model of autoencoder-long short-term memory network to learn the deep temporal features of normal tightening patterns. Finally, it employs an information entropy weighted fusion strategy to achieve collaborative decision-making between the two independent models, significantly improving the accuracy and robustness of anomaly detection. Furthermore, this invention enables the direct conversion of intelligent diagnostic results into production execution, ultimately constructing an autonomous, continuously evolving bolt tightening quality assurance system with zero human intervention.

The present invention relates to the technical field of electronic control fault diagnosis, and specifically to a fault self-diagnosis system and method for a motorcycle electronic control system, including a data acquisition module for detecting data according to the motorcycle electronic control system; a first fault diagnosis module for constructing first diagnostic data based on the detection trigger frequency, duration, and execution data, processing the first diagnostic data to obtain first fault data, and obtaining a fault diagnosis result of the ECU control unit according to a first preset diagnostic standard; a second fault diagnosis module for constructing second diagnostic data based on the operating parameters collected by the first detection and the second detection, processing the second diagnostic data to obtain second fault data, and obtaining a motorcycle fault diagnosis result according to a second preset diagnostic standard.

The invention discloses a saliva flow rate detection strip for auxiliary diagnosis of xerostomia and a detection method thereof. The detection strip comprises strip-shaped filter paper pretreated by fluorescein sodium, the filter paper is packaged in a food-grade plastic shell, one end of the filter paper extends out of the shell to form a 0.5 cm sampling part, and scale marks are arranged on the filter paper. During detection, the sampling part is placed under the tongue for 3 minutes, saliva infiltrates along the filter paper under the capillary action and dissolves the fluorescein sodium for color development, and the flow rate of the unstimulated saliva can be obtained through conversion by reading the infiltration length. The detection strip can effectively distinguish a control person from a sicca syndrome patient, a xerostomia patient and the like. The problems that a traditional method is long in consumed time, poor in experience and inconvenient to operate are solved, and a detection tool which is rapid, objective and standardized and is in seamless butt joint with an existing diagnostic standard is provided.

The invention discloses a cervical cancer differential diagnosis method based on GINS1 expression quantity. The cervical cancer differential diagnosis method comprises the following steps: S1, acquiring a cell or tissue sample in a to-be-detected sample and pre-treating the cell or tissue sample; s2, measuring the expression quantity of the GINS1 gene in the sample; s3, comparing the measured GINS1 expression quantity with the known cervical cancer sample expression quantity; s4, judging whether the to-be-detected sample is cervical cancer or not according to a comparison result; by integrating the expression level, functional activity and spatial distribution characteristics of the GINS1 gene, a multi-modal detection system is constructed, and the multi-dimensional analysis not only can effectively distinguish cervical cancer from benign lesions (such as inflammation or metaplasia), but also can identify the molecular characteristics of a small number of abnormal cells in early cancerization, so that the detection result is more accurate. The problem of misdiagnosis caused by limitation of a single index in the prior art is solved; a DNA damage response dynamic correction mechanism is innovatively introduced, and a GINS1 judgment threshold is automatically adjusted by evaluating the DNA damage degree in a sample in real time, so that a diagnostic standard has environmental adaptability.

The method mainly comprises three core stages from S1 to S3. S1, a data set construction step; s2, a cold start supervision fine tuning step; s3, a progressive course reinforcement learning step; according to the method, the data is obtained from the multi-source medical field text data and is subjected to structured processing by using the knowledge distillation model, so that the private ophthalmology reasoning data set containing ophthalmology professional knowledge and reasoning logic is generated; the constructed ophthalmology model can accurately master professional contents such as complex anatomical structures, pathophysiological mechanisms, diagnostic standards and therapeutic schedules in the ophthalmology field, and absorption of the knowledge is further enhanced in the cold start supervision fine tuning stage, so that the model has a solid ophthalmology knowledge basis. Compared with a model pre-trained by directly using general corpora, the precision and timeliness of the model during ophthalmology professional problem processing are greatly improved through the targeted data set construction mode, understanding deviation and misleading information generation are reduced, and therefore the rigorous requirements of clinical application are met.

The present invention discloses a method for intelligently identifying and classifying ventilator-patient ventilation asynchronous phenomena. This method addresses the problems of low automated detection and insufficient detection accuracy of existing clinical ventilator-machine asynchronous phenomena, and provides a set of high-precision and high-accuracy identification and classification methods. Based on feature extraction and intelligent identification of ventilator waveform data, the present invention deeply mines ventilator waveform data, has high feasibility and universality, and does not require invasive data collection methods such as esophageal electrode catheter technology on patients, which will not affect the ongoing patient treatment process. At the same time, it can intelligently identify the start time of inspiration and exhalation of patients during ventilation, which can uncover more effective information. Different human-machine asynchronous phenomena can be accurately defined based on clinical diagnostic standards, achieving high-precision identification and accurate classification of ventilator-patient ventilation asynchronous phenomena.

The invention relates to a network (1) in a motor vehicle, wherein the network comprises n control units (2), wherein the control units (2) have software (7) configured to generate data for a first diagnostic standard and a second diagnostic standard, wherein the control units (2) have at least one local memory area (8, 9), wherein the control units (2) are interconnected via at least one bus system (4), wherein the control units (2) each have protocol software (11, 12) for the first diagnostic standard and for the second diagnostic standard, wherein one control unit (2) is configured as a master (3) to determine the protocol software (11, 12) to be used by the other control units (2), a control unit (2) and a method for generating diagnostic data.

The invention discloses a preparation method and application of a recombinant pig for expressing a human-derived HLA-B * 15: 02 gene. The invention provides a recombinant pig which is obtained by inserting a human HLA-B * 15: 02 gene into genome DNA (Deoxyribose Nucleic Acid) of the pig. The recombinant pig can be used as a pig model for screening drugs causing immune-mediated adverse reactions. The invention provides application of the recombinant pig in preparation of a pig model. The pig model is used for mechanism research of the immune-mediated adverse drug reaction or used for assisting establishment of a diagnostic standard of the immune-mediated adverse drug reaction or used for screening markers of the immune-mediated adverse drug reaction or used for research and development of drugs with the immune-mediated adverse drug reaction as indications. The invention has application and popularization values for related research and clinic of immune-mediated adverse reactions.

The invention discloses a urodynamics evaluation method based on multiple modes and related equipment. The method comprises the steps that multi-source urodynamics information is obtained, and the multi-source urodynamics information comprises urodynamics signals, urodynamics clinical characteristics and patient personal information; based on the personal information of the patient, correcting the urodynamic signals and the urodynamic clinical features through a label offset atlas model; and inputting the personal information of the patient, the corrected urodynamics signal and the corrected urodynamics clinical characteristics into a urodynamics evaluation model to obtain a urodynamics evaluation result of the patient. The problems that the urodynamic assessment process mainly depends on collaborative observation and analysis of urodynamic examination by clinicians, the degree of dependence on professional knowledge and clinical experience of the clinicians is large, no clear diagnostic standard exists in the urodynamic examination, and the diagnostic result is subjective can be solved.

The invention discloses an enterprise supply chain risk identification method and device, a storage medium and a program product, and relates to the technical field of supply chain management, risk assessment is performed on supply chain data of an enterprise through a pre-formulated quantitative diagnosis standard, and whether the enterprise has a supply chain risk is determined, so that the enterprise supply chain risk can be identified. The enterprise can optimize the enterprise supply chain by adopting a corresponding risk avoidance means according to the identified current supply chain risk. Compared with the current mode that a single-dimension evaluation result of part of enterprise data is directly used as an enterprise supply chain risk evaluation result, different quantitative diagnosis standards and corresponding supply chain related data are comprehensively considered, and the supply chain risk of the enterprise is comprehensively and accurately evaluated.

This invention belongs to the field of medical image processing, and specifically relates to a system and method for predicting the pathological differentiation degree of liver cancer by fusing multi-sequence magnetic resonance imaging and semantic information. The system includes steps such as acquiring multi-sequence lesion region images, generating structured diagnostic text, feature extraction and fusion, and predicting the pathological differentiation degree of liver cancer based on the fused features. By converting professional medical features conforming to the diagnostic standards of the liver imaging report and data system contained in the multi-sequence lesion region images into structured diagnostic text information, and using the lesion region images and structured diagnostic text as input, a bidirectional cross-modal attention mechanism is employed to align and complement visual features in the images that are difficult to quantify with semantic information in the text. This creates a synergistic effect between image and text information, enabling the model to capture deep pathological correlations that cannot be effectively expressed by a single image modality. This invention improves the accuracy and reliability of prediction, providing a more precise basis for clinical decision-making.

The invention discloses a health risk dynamic assessment and early warning method and system for old people groups, and relates to the field of health risk assessment of old people groups, and the method comprises the steps: comparing first health detection data with a preset chronic disease diagnosis standard, and building a hierarchical initial health file according to a comparison result; acquiring symptom description information, treatment condition information and detection data fed back by the elderly individuals; extracting stage health features; constructing an old people health fault tree knowledge graph; calculating the probability of occurrence of the health endpoint event of the elderly individual, and identifying a key risk conduction path causing increase of the probability; generating differentiated early warning information and intervention suggestions; and optimizing and adjusting the logic gate weight in the fault tree knowledge graph. The method has the advantages that the senile health fault tree knowledge graph is constructed, expert knowledge in the field of senile medicine is converted into a computable risk conduction logic model, and interpretable early warning and accurate traceability of senile health risks are achieved.

The invention discloses an electronic medical record intelligent entry method and system, and relates to the technical field of data processing, and the method comprises the steps: building an electronic medical record library in advance according to the reservation record of a patient, and setting a patient doctor-seeing intelligent entry mechanism through the electronic medical record library; a comparison database is established through texts, videos and voices of historical medical records to achieve keyword triggering, key information is extracted to serve as comparison detection indexes, preliminary treatment data of a patient is analyzed, a comprehensive analysis input value is obtained by combining the voices and physiological data, autonomous input types are divided, and dynamic evaluation switching is conducted. The input mode can be adjusted according to the autonomous ability of a patient, key symptom associated features of the patient are compared with a doctor-seeing standardized knowledge base, a diagnosis standard with a high matching degree is screened out, symptom types and risk estimation values are obtained by integrating multiple analysis lists, grades are divided according to the different symptom types, permission encryption is performed, and the permission encryption efficiency is improved. Triggering mechanisms are set for different risk levels, and patients with different risks are effectively managed.

The invention relates to a vehicle-mounted ECU automatic diagnosis service testing method and system based on an intelligent agent, and belongs to the technical field of vehicle-mounted network testing. The method comprises the following steps: acquiring a diagnosis questionnaire of each type of ECU and a latest diagnosis standard, and inputting the diagnosis questionnaire and the latest diagnosis standard into an external knowledge module of an intelligent agent; the intelligent agent retrieves and obtains a diagnosis questionnaire of the ECU according to a vector database, inquired by the user, of the external knowledge base module; the intelligent agent obtains ECU information by reading the diagnosis questionnaire, and then the description of the diagnosis standard on the support service is retrieved in an external database to generate a test case; generating a test script through the test script generation large model, inputting the test script into a compiler for compiling, and if compiling fails, performing self-reflection by the intelligent agent to regenerate the test script until compiling succeeds; and the intelligent agent sends the test script to the test environment, and the test environment returns a test result to the intelligent agent for analysis. According to the invention, the accuracy and integrity of the test case can be improved.