7 results about "Primary osteoporosis" patented technology

The invention relates to a metabolic marker for diagnosing diabetic secondary osteoporosis and application of the metabolic marker, and belongs to the technical field of biological medicine. The method comprises the following steps: firstly, screening out obviously different metabolites between osteoporosis (DOP) and osteoporosis-free groups (DM) in diabetic patients; and secondly, screening out significant difference metabolites between the primary osteoporosis patient and the healthy control group, and excluding the intersection of the two groups of difference metabolites to obtain metabolites which exclude the influence of the primary osteoporosis. Metabolic markers, namely cytosine nucleotide, ketoglutaric acid and triethylamine, are obtained through further screening, and when the three metabolic markers are independently used, AUCgt; the AUC during combined use is 0.99, so that the limitation that the sensitivity of the traditional bone mineral density detection on the osteoporosis recognition in the diabetic population is insufficient is effectively made up.

The present application relates to the technical field of multi-cause intelligent analysis in medical diagnosis, and particularly relates to an osteoporosis detection method and system based on data analysis and artificial intelligence. The method comprises the following steps: S1: obtaining primary osteoporosis data and secondary osteoporosis data and determining synergistic data, and determining antagonistic masking data according to the synergistic data; S2: obtaining a first layer dominant right conversion sequence according to the synergistic data and the antagonistic masking data; S3: obtaining a synergistic data internal dominant relationship sequence and an antagonistic masking data internal dominant relationship sequence respectively according to the synergistic data and the antagonistic masking data, and generating a complete dominant relationship graph in combination with the first layer dominant right conversion sequence. The present application separates and analyzes the synergistic and antagonistic modes of primary and secondary osteoporosis, constructs a dynamic dominant right sequence, quantifies the evolution of mixed cause weights, and provides data support for individualized precise diagnosis and treatment.

The invention discloses a primary osteoporosis traditional Chinese medicine clinical decision support system, and relates to the field of traditional Chinese medicine artificial intelligence, and the system comprises a data processing module which is used for carrying out the term standardization processing and syndrome element extraction of original traditional Chinese medicine literature data, and constructing a structured traditional Chinese medicine syndrome and treatment database; the dialectical reasoning module is constructed based on a machine learning model and is used for outputting a traditional Chinese medicine syndrome element diagnosis result according to the input four diagnosis information; and the prescription recommendation module is constructed based on a knowledge graph and is used for recommending corresponding treatment methods and rules and traditional Chinese medicine prescriptions according to the traditional Chinese medicine syndrome element diagnosis results and / or the specific clinical symptoms. According to the scheme, objectivity and consistency of syndrome element differentiation are remarkably enhanced, the defect that a traditional method is insufficient in composite syndrome type processing capacity is overcome, and the self-adaptive capacity of prescription recommendation along with dynamic evolution of clinical practice is achieved.

The application provides a medicine composition for treating primary osteoporosis and a preparation method thereof. The medicine composition is composed of Semen Euryae, Herba Epimedii and the like. The application uses the theory of meridian diseases in traditional Chinese medicine to discuss the etiology and pathogenesis of primary osteoporosis. The medicine composition has the effects of tonifying kidney and filling essence, nourishing yin and supporting yang, strengthening muscles and bones.

The present application relates to a kind of metabolic markers for diagnosing diabetes secondary osteoporosis and its application, belong to biological medicine technical field.The present application first selects the significant difference metabolite between the group of diabetes mellitus patients with osteoporosis (DOP) and the group without osteoporosis (DM);Second, the significant difference metabolite between the group of primary osteoporosis patients and the group of healthy controls is screened out, and the intersection of the two groups of difference metabolites is excluded, and the obtained metabolite excludes the influence of primary osteoporosis.Subsequently, the metabolic markers of the present application-cytidine nucleotide, ketoglutaric acid, triethylamine are further screened, and when the three kinds of metabolic markers are used alone, the AUC is greater than 0.8, and when the three kinds of metabolic markers are used in combination, the AUC is 0.99, effectively making up the limitation of traditional bone density detection in the population of diabetes mellitus patients to identify osteoporosis.

The embodiment of the invention discloses an osteoporosis secondary screening method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring medical data to be screened; preprocessing the medical data to be screened; performing feature extraction and combination on the processing result to obtain data features; inputting the data features into an osteoporosis secondary screening model for prediction to obtain a prediction result; outputting a prediction result; the osteoporosis secondary screening model comprises a primary osteoporosis screening model which is obtained by training a gradient lifting machine learning model through a sample set formed by processing medical data with labels indicating whether sample bone mass is abnormal or not; the second-level osteoporosis screening model is obtained by training a neural network through a sample set formed by performing feature fusion on an output result of the first-level osteoporosis screening model and a medical examination data subset. By implementing the method provided by the embodiment of the invention, the accuracy of screening high-risk groups with osteoporosis in middle-aged and elderly women can be improved.

The application provides a primary osteoporosis diagnosis or risk warning system based on tongue image of traditional Chinese medicine. The system of the application takes tongue image or tongue image and clinical data features of a subject as input, realizes the fusion application of traditional Chinese medicine tongue diagnosis and modern deep learning, and further realizes the early identification / diagnosis and risk warning of osteoporosis.