10 results about "Kidney problem" patented technology

A system and method for determining patient hospitalization risk and treating patients is disclosed. The system and method may include extracting patient data from one or more databases corresponding to a pool of patients having end stage renal disease; using a predictive model with the extracted patient data to generate, for each of the patients, a respective expected probability for hospitalization within a predetermined time period; identifying a subset of patients having respective expected probabilities that are higher than other patients in the pool of patients; identifying, for each patient, at least one factor from the patient data that increased the expected probability of hospitalization; and based on the identified factors, determining and executing clinical interventions to lower the probability of hospitalization within the subset of the pool of patients.

This invention relates to an activator of FXR, which can be used to treat autoimmune diseases, liver diseases, intestinal diseases, kidney diseases, cancer, and other diseases in which FXR plays a role, and has the formula (I): wherein L1, A, X1, X2, R 1 R 2 and R 3 As described in this article.

The application discloses a fundus lesion evaluation method and device for chronic kidney disease, and the method comprises the following steps: acquiring an ultra-wide-angle fundus image of a target object to be measured with chronic kidney disease, and extracting fundus features from the ultra-wide-angle fundus image; based on the extracted fundus features, predicting a fundus lesion state by using a multivariate regression model; performing multimodal fusion processing on the fundus lesion state, patient information of the target object to be measured and clinical information; performing lesion probability prediction processing on the dynamic confidence interval of the multimodal fusion features to generate a chronic kidney disease fundus lesion risk grade. Therefore, not only can the chronic kidney disease fundus lesion risk grade be accurately predicted, but also the reliability of the chronic kidney disease fundus lesion risk grade evaluation is improved; moreover, an evaluation standard for the fundus lesion risk grade of early chronic kidney disease is constructed, so that the purpose of early detection and early treatment of patients is achieved, and irreversible fundus damage caused by CKD is reduced.

The present application provides a polypeptide targeting renal tubular epithelial cells, and the amino acid sequence of the polypeptide is ALINDTH. The present application also provides a targeted delivery system targeting renal tubular epithelial cells, which contains the polypeptide targeting renal tubular epithelial cells. The present application also provides a preparation method of the targeted delivery system targeting renal tubular epithelial cells. The present application also provides the use of the targeted delivery system targeting renal tubular epithelial cells in the preparation of a drug for treating kidney diseases. The present application realizes precise drug delivery for kidney diseases, and effectively solves the key technical problems of insufficient targeting of exosomes and low drug loading efficiency.

The application discloses a kidney disease dialysis data processing method based on artificial intelligence and particularly relates to the technical field of medical data processing, comprising multi-modal dynamic coupling graph feature extraction: real-time collection of dialysis machine high-frequency numerical values and nurse-entered low-frequency events, construction of a cross-modal coupling graph through dynamic mutual information and an adaptive threshold, introduction of a time decay factor to correct edge weights, aggregation of output deep time sequence features through a graph attention network; stage adaptive time sequence risk prediction: input of features into a multi-scale dilated convolution network, fusion of different receptive field features through channel attention, division of non-equidistant intervals according to a dialysis process, stage sensitivity weighting using a learnable interval embedding vector and multi-head attention, and output of hypotension probability and first derivative trend; the application realizes cross-modal feature deep coupling, dialysis stage adaptive perception and executable causal intervention decision-making, and significantly improves early warning accuracy and clinical practical value.

This invention discloses a method for constructing a traditional Chinese and Western medicine-integrated kidney disease prediction model, belonging to the field of medical prediction model construction technology. The method includes: collecting Western medicine clinical data covering indicators such as kidney function and routine urine tests, and traditional Chinese medicine diagnostic data including symptoms, signs, tongue and pulse characteristics; ensuring consistent and complete fields through standardized template recording; processing missing data using multiple imputation and mode imputation methods, and removing outlier data using box plots and isolated forest algorithms; standardizing and encoding numerical and categorical data respectively; and extracting features through recursive feature elimination and word embedding techniques. This method achieves deep integration of traditional Chinese and Western medicine data, overcoming the limitations of single models. The model captures comprehensive features, has stable predictive performance, strong generalization ability, adapts to different medical scenarios, ensures data security, offers flexible deployment options, assists in precise clinical decision-making, promotes the implementation of early kidney disease prediction technology, and provides support for early detection and intervention of kidney disease.

Disclosed are compounds according to Formula (A), and related tautomers and pharmaceutical compositions. Also disclosed are therapeutic methods, e.g., of treating kidney diseases, using the compounds of Formula (A).

The current invention discloses methods for treating diseases characterized by elevated levels of the urokinase plasminogen activator receptor (uPAR) protein D2D3 wherein the disease is one or more of chronic kidney disease, insulin-dependent diabetes, or diabetic neuropathy. In addition, the invention provides methods for restoring pancreatic β-cell number and function in the pancreas of a subject diagnosed with insulin-dependent diabetes wherein the insulin-dependent diabetes is characterized by the presence of detectable levels of D2D3 Specifically, the methods comprise administration of a therapeutically effective amount of an agent that antagonizes or removes D2D3 from the circulation of the subject wherein the agent comprises an anti-D2D3 antibody or antigen binding fragment thereof that specifically binds to a D2D3 protein. Alternatively, the methods comprise removing the D2D3 protein from the circulation of the subject by an extracorporeal procedure.

In some embodiments, a method of training a machine-learning (ML) model for use in detecting acute kidney injury (AKI) is provided that includes (1) obtaining a dataset of detected biomarkers; (2) selecting a plurality of routine biomarkers from the dataset; and (3) training an ML model to identify AKI based on the plurality of routine biomarkers by employing the plurality of routine biomarkers as inputs and presence of AKI and absence of AKI as target values. Numerous other embodiments are provided.

This application relates to a blood pressure pattern recognition and intervention system for hemodialysis in end-stage renal disease. The method includes: a dialysis device for performing hemodialysis on a user; a data collection device; a memory and a processor, the processor loading a dialysis control program to execute the following steps: filtering and combining data collected by the data collection device and a historical database to obtain blood pressure-related features; generating classification factors and grouping patients into different groups; establishing training and testing sets, training a machine learning model to obtain a blood pressure prediction model, and validating and adjusting the model based on the testing set; inputting the data collected by the data collection device into the blood pressure prediction model, and obtaining dialysis device control parameters based on the blood pressure prediction model; and controlling the dialysate temperature, dialysate flow rate, and dialysis duration of the dialysis device based on the dialysis device control parameters. This application has the advantage of being able to calculate and adjust the parameters of the dialysis device according to the user's real-time condition.