8 results about "DIAGNOSTIC STANDARD" patented technology

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 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.

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 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 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.

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.