33 results about "Diabetes risk" patented technology

The invention discloses an abnormal fasting blood glucose level early-warning method based on an integrated learning fusion model. With combination of individual physical examination data of blood routine examination, liver function, blood fat, and renal function and the like, a fasting blood glucose level is predicted by fusing a gradient regression tree, a random forest, and a linear regressionmodel and the like based on an integrated learning method. The prediction model is trained by using lots of training data, so that the accuracy, universality and robustness of the prediction model areimproved. Fasting blood glucose level prediction is carried out on timely an individual without fasting blood glucose checking, so that the high-diabetes-risk patient is warned early and effectively.

The invention provides a gestation diabetes risk monitoring system based on dynamic physics and physical and chemical factors and belongs to the field of medical instruments in the department of obstetrics and gynecology. The gestation diabetes risk monitoring system is characterized by comprising a computer, a toggle switch array, a resistor array and a gestation diabetes pathogenesis risk LED alarming module; a pregnant women information input module of dynamic physics check and physical and chemical factors, a logic switch array module of pregnant women gestation diabetes pathogenesis risk factors and logic regression value computation module of pregnant women gestation diabetes pathogenesis risk are arranged in the computer; a voltage comparison module and an LED array are arranged in the gestation diabetes pathogenesis risk LED alarming module. According to the invention, the logic switch value is utilized to control the make-and-break state of the toggle switch; a risk factor value in the logic regression manner is utilized to control the resistance value of the resistor array, so as to indicate the relative risk degree; the resistance value of the resistor array is utilized to control a voltage comparator, so as to output electrical level and light the LED alarm. According to the invention, the risk degree of the dynamic gestation diabetes can be comprehensively evaluated from multivariate physical and chemical factors and the real-time alarm can be conducted.

Methods, systems and circuits evaluate a subject's risk of developing type 2 diabetes or developing or having prediabetes using at least one defined mathematical model of risk of progression that can stratify risk for patients having the same glucose measurement. The model may include NMR derived measurements of GlycA and a plurality of selected lipoprotein components of at least one biosample of the subject.

The invention discloses a diabetes risk early warning method and system based on a depth auto-encoder. The method comprises the steps of obtaining and preprocessing a physical examination data set; dividing a label-free data set into a training set, a verification set and a test set, and generating a label of the depth auto-encoder; designing an encoder part and a decoder part of the depth auto-encoder, and training and evaluating the encoder part and the decoder part by using the training set, the verification set and the test set respectively; and predicting the diabetes risk of an individual to be detected by using the evaluated depth auto-encoder and the trained regression model. The invention further discloses diabetes risk early warning computer equipment based on the depth auto-encoder and a computer readable storage medium. The depth self-encoder is introduced, so that a small amount of label data can be fully utilized; more accurate diabetes early warning is carried out by training the deep auto-encoder in advance and combining a logistic regression model; and the method and system adapt to different diabetes types and symptoms, and are higher in practicability, generalization and expansibility.

The invention discloses computer equipment, a system and a readable storage medium. The computer equipment comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor. When the processor executes the program, multi-source heterogeneous data related to diabetes mellitus are collected; entities, attribute information of the entities and relationship information among the entities are extracted from the multi-source heterogeneous data; a diabetes knowledge graph is constructed according to the extracted entities, the attribute information of the entities and the relationship information among the entities; and a parameter group consisting of one or more parameters related to diabetes is received, and the diabetes risk corresponding to the parameter group based on the diabetes knowledge graph is obtained and output. According to the method, risk prediction is carried out by fusing the diabetes knowledge graph constructed by the multi-source heterogeneous data, the diabetes risk prediction level can be accurately given, and then health suggestions with high pertinence can be given.

The invention relates to the field of digital medical treatment, diabetes risk grade prediction is carried out according to the risk evaluation information of the target user, and the risk of the target user suffering from diabetes can be conveniently detected. The invention relates to a diabetes risk prediction equipment and device, and the equipment is implemented by the steps: acquiring risk assessment information of a target user, wherein the risk assessment information comprises personal information, diabetes index information, health condition information and family disease history information; performing disease identification on the health condition information, and determining whether the target user suffers from diabetes or not; if the target user does not suffer from diabetes, determining whether the target user is a suspected diabetic patient according to the diabetes index information; if the target user is a non-suspected diabetic patient, carrying out the diabetes risk level prediction according to the personal information, the diabetes index information and the family disease history information, and obtaining the diabetes risk level of the target user. In addition, the invention also relates to the blockchain technology, and the risk assessment information can be stored in blockchain.

The invention belongs to the technical field of edible fit, and particularly relates to a functional fat composition having the effects of notably preventing cardiovascular and cerebrovascular diseases and diabetes risk factors, comprising disorder lipid metabolism, lipid peroxidation, and inflammatory factors. The functional fat composition having the effects of preventing cardiovascular and cerebrovascular diseases and diabetes risk factors is characterized by being prepared from the following raw materials in percentage by weight: 74.73%-93% of low erucic acid rapeseed oil, 6.0%-15% of krill oil, 0.16%-10% of vegapure plant sterol ester, 0.008%-0.07% of fat-soluble tea polyphenols and 0.02-0.2% of vitamin E. The composition notably strengthens the intervention effect of the fat composition to the cardiovascular and cerebrovascular diseases and the diabetes risk factors, and can be used as cooking fat or special diet edible fat for target populations.

The invention discloses a diabetes key feature parameter acquisition method, which comprises the following steps of: performing algebraic combination and standardization processing on diabetes related original data to obtain a diabetes risk factor candidate feature set; the method comprises the following steps: screening features related to diabetes through an RReliefF algorithm, and constructing a maximum correlation feature set; redundant features irrelevant to diabetes mellitus are removed through an mRMR algorithm, and a maximum-correlation and minimum-redundancy feature set is constructed; and performing causal replacement by adopting an improved FCL causal discovery method to obtain a diabetes key feature set. By means of algebraic combination, the complexity of the diabetes feature set can be greatly increased, and the diabetes features with higher prediction contribution degree can be selected conveniently; a maximum-correlation minimum-redundancy diabetes feature set is obtained by using an RReliefF algorithm and an mRMR algorithm, and the dimensionality of the diabetes feature set is reduced; an improved FCL algorithm is used to carry out causal replacement before diabetes features, and a diabetes feature set with better contribution degree is obtained.

The present invention relates to a diabetes early warning system. The system comprises: a memory; and a first processor, which is based on improved k-means clustering, coupled to the memory, and configured to: according to selected first clustering centroids, obtain stable centroids for individual clusters, and put them in a diabetes piecewise function, thereby obtaining a diabetes early warning model, wherein the first clustering centroid is selected by selecting a data set, defining a clustering cluster number k and a neighborhood radius ε, and selecting a sample point on which a sum of distances between a sample point X_i and a sample is the greatest as the first clustering centroid, so as to make the first clustering centroid fall in a central portion of the corresponding cluster. The present invention improves the clustering centroid method, establishes a diabetes piecewise function early warning model, improves the diabetes early warning ability, and provides a basis for the diagnosis and treatment of diabetes at different stages. Starting from the characteristics of the diabetes data set, the key feature variables of diabetes are selected to simplify the diabetes prediction model; and the accuracy of the diabetes prediction model is improved, thereby helping to provide accurate diabetes prevention and treatment measures.

The invention belongs to the technical field of data processing, and particularly relates to an early diabetes risk prediction method based on deep PCA transformation, which comprises the following steps: inputting an early diabetes data set; preprocessing the data, calculating a Pearson correlation coefficient, and filtering redundant features to obtain input data; extracting a feature set of input data through depth PCA to serve as input of a training logistic regression classifier; training a logistic regression classifier based on the feature set for judging a to-be-evaluated case sample; inputting new case sample information, and outputting a result of judging whether the sample suffers from diabetes or not and a corresponding confidence coefficient. Effective extraction of case sample binarization information is realized through a deep PCA-based feature transformation method, and meanwhile, a logistic regression classifier is established to realize judgment of a diseased sample and output a confidence quantitative index of a result, so that early auxiliary diagnosis of an existing diabetes case is conveniently and effectively realized, and an illness state is discovered in time.

The invention belongs to the technical field of diabetes mellitus early detection, and discloses a diabetes mellitus risk early detection management system. The system comprises a monitor module, a signal processing module, a blood detection module, a central processing module, a cloud storage module and an evaluation module. The monitor module, the signal processing module, the blood detection module, the cloud storage module and the evaluation module are connected with the central processing module, and the transmitted data is transmitted to each module through the central processing module. The monitoring module is arranged, corresponding sensors are utilized, various body signals of a patient are collected, diabetes risk judgment is carried out in combination with body data of the patient, the judgment accuracy of a doctor is improved, and potential abnormity is found; according to the invention, the signal processing module is arranged to carry out noise reduction processing on signals, which is beneficial for discovering illness conditions; the system is provided with the cloud storage module, various indexes of the patient are stored in a targeted mode, and data storage is beneficial to development of the whole industry of the technology.

The invention relates to a diabetes risk analysis method based on the body conductivity. The diabetes risk is analyzed according to a human body conductivity value and symmetry associated to the concentration of Cl ions of perspiration secreted by sweat gland on two hands and two feet of the human body. The content of the method includes an integral architecture of software, an algorithm for the human body conductivity value of two hands, two feet and two sides of the forehead formed by different concentrations of Cl ions of the perspiration secreted by sweet gland and threshold voltage symmetry, a voltage test method and a threshold voltage symmetry algorithm between an electrode contact part and a non-contact part and an evaluation algorithm for evaluating the diabetes risk of a tested person according to the parameters.

The invention discloses a diabetes risk prediction device based on a diabetes map, and relates to the technical field of disease inquiry devices.The diabetes risk prediction device comprises a diabetes risk prediction device body, and a man-machine interaction touch screen is fixedly installed on the front face of the diabetes risk prediction device body; a control button is arranged on the front side of the diabetes risk prediction device body, and a self-service detection mechanism is arranged at the bottom of the diabetes risk prediction device body. A second electric push rod is controlled to contract, a storage box body on the left side of an intercepting block can slide into an inner cavity of a receiving frame through the top of an inclined table, a patient accordingly destroys a dustproof film, a movable cover is detached from the top of the storage box body, and then a blood taking needle, a cotton swab and blood glucose paper are sequentially extracted from the interior of a sponge storage block; then the stepping motor is controlled to work to drive the sealing baffle to rotate, locking of the glucometer body is relieved, the patient can operate the glucometer body to achieve the self-service detection function accordingly, and the reception efficiency of a hospital is improved.

The invention discloses a diabetes risk factor cause and effect discovery method based on improved function cause and effect likelihood, and belongs to the technical field of medical informatization.The method comprises the steps: obtaining joint distribution of diabetes risk factor variable subsets; solving the log likelihood of the observation data according to the joint distribution and the cause and effect structure; converting the logarithm likelihood of the observation data into logarithm likelihood of noise of the observation data, and then establishing a diabetes risk factor FCL model; and correcting the diabetes risk factor FCL model by adjusting a threshold value to obtain a diabetes risk factor IFCL model, and discovering the cause and effect relationship of risk factors by utilizing the diabetes risk factor IFCL model. According to the method, the adjustment threshold value is introduced, the diabetes risk factor IFCL model is constructed, the cause and effect relationshipof the risk factors is found by utilizing the diabetes risk factor IFCL model, redundant edges and wrong edges of a diabetes risk factor cause and effect structure are reduced, and then the optimizeddiabetes risk factor cause and effect structure is generated.

The invention relates to the technical field of diabetes risk early detection management, and especially relates to a diabetes risk early detection management system and method. The system comprises diabetes risk early intelligent detection equipment which is used for detecting the diabetes parameters of a user; a wireless communication device which is used for transmitting the diabetes parametersto a diabetes risk early detection management platform; and the diabetes risk early detection management platform which is used for storing the received diabetes parameters, calculating and obtaininga diagnosis result according to the received diabetes parameters, and carrying out the editing according to the preset user information to form a diagnosis report for displaying. According to the invention, the system comprises the diabetes risk early intelligent detection equipment, the wireless communication device, the diabetes risk early detection management platform and a mobile terminal with a display function, and the Internet diabetes risk early management system with a diabetes risk early detection data dynamic presentation function and an interaction function can be achieved.

The invention discloses a nucleic acid sequence for detecting a diabetes risk site, a kit and a detection method thereof. A nucleic acid sequence combination disclosed by the invention is proposed specific to five polymorphic sites (rs228648, rs114202595, rs7754840, rs7756992 and rs7574865) of diabetes, and comprises a wild upstream primer, a mutant upstream primer and a universal downstream primer of each site. The kit for rapidly detecting the diabetes risk site and prepared from the nucleic acid sequence combination has the characteristics of convenience in use, easiness and convenience inoperation, high automation degree, lower pollution in an operation process, good detection effect, high sensitivity, high specificity, high accuracy and high precision. The detection method disclosedby the invention is convenient and rapid to use through adoption of a complete closed-tube operation; a detection result is obtained by directly exploring a fluorescent signal value in a PCR (Polymerase Chain Reaction) process, so that PCR posttreatment or electrophoresis detection is not required, the technical problems of great probability of pollution and false positive value generation in theconventional PCR technology are solved, and non-specific amplification can be effectively avoided; the detection method is suitable for large-scale sample detection.