3 results about "DIAGNOSTIC STANDARD" patented technology

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.

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.

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.