125 results about "Kidney problem" patented technology

The invention relates to a nephrotic patient health management method and system based on a neural network prediction model, and the method comprises the steps: obtaining the multi-dimensional health data of a target nephrotic patient at each stage, constructing a time sequence feature matrix to extract the change trend of the whole course of disease, and dividing the course of disease into a disease alleviation stage and a disease deterioration stage; analyzing the health data of the two stages to obtain an improvement factor and a deterioration factor; outputting a renal function state prediction result by using a neural network prediction model fusing an attention mechanism and a knowledge graph, and generating an initial health management scheme by combining the result, a preset health management rule base and patient preferences; and optimizing the initial scheme based on the improvement factor, the deterioration factor and a reinforcement learning algorithm to obtain an optimized health management scheme. Complete-cycle closed-loop management of data acquisition, trend analysis, factor identification, prediction and early warning, scheme generation and dynamic optimization of nephropathy patients is realized, and the accuracy and individuation level of nephropathy health management are improved.

The invention relates to the technical field of medical artificial intelligence, and discloses an end-stage nephropathy curative effect evaluation and complication risk intelligent prediction system, which comprises a data acquisition module, which is in communication connection with a hospital information system, physiological monitoring equipment and a user terminal to obtain multi-source data of a patient, the multi-source data comprises structured physiological index data, laboratory detection data, patient medical history and other unstructured text data; and the feature fusion module is used for receiving the multi-source data from the data acquisition module, performing standardization processing on the structured physiological index data, converting the unstructured text data into a vector form in an embedded encoding manner, splicing the vector form with the structured features, and constructing uniform time sequence input features. According to the method, by introducing an incremental learning mechanism and a feature attribution method, the comprehensive effects of dynamic model updating, explainable prediction results, accurate feature characterization and controllable stability are achieved.

The invention relates to the technical field of medical care informatics, in particular to a nephropathy medication interaction risk screening system which comprises a data acquisition unit, an SOC chip processing and analysis unit, a risk early warning unit and a man-machine interaction and communication unit. Through multi-source data access, dynamic weight scheduling and Kalman filtering fusion correction, accurate and on-demand acquisition of nephropathy core indexes is realized, and real-time intelligent discrimination of personalized medication risks is realized by relying on knowledge base support, adaptive path adjustment and a safe atomization updating mechanism, so that the risk of the nephropathy is accurately determined. Therefore, the problems that traditional system data acquisition lacks specialized pertinence, risk assessment personalization is insufficient, and model updating is lagged are effectively solved, and the accuracy and timeliness of nephropathy medication interaction risk screening are remarkably improved.

The invention discloses a remote home dialysis data management cloud platform for nephropathy and a data analysis method, and relates to the technical field of home medical treatment, the remote home dialysis data management cloud platform comprises a data acquisition module, a data cloud end and an interaction terminal; the data acquisition module comprises a dialysis process data acquisition unit used for acquiring real-time dialysis data in the household dialysis process of a user; the home behavior data acquisition unit is used for acquiring user home behavior data; the data cloud comprises a cloud data storage module used for storing real-time dialysis data and user home behavior data in the user home dialysis process collected by a data collection module; the cloud data analysis module is used for analyzing whether the home dialysis treatment of the user is abnormal or not; and the virtual diagnosis and treatment module is used for constructing a virtual diagnosis and treatment room. The system has the advantages that an intelligent management mode is adopted, so that the treatment effect and the management efficiency of home dialysis are remarkably improved, and a safer and more convenient medical service solution is provided for patients with chronic kidney diseases.

The invention provides a chronic kidney patient cardiovascular adverse event prediction and clinical treatment system based on deep learning, and the system comprises a multi-source information integration module which obtains multi-modal fusion data; the risk prediction module is used for constructing a risk prediction model and outputting the risk prediction probability, the risk level and the risk report of each patient; the decision support module is used for generating a plurality of interpretable layers, calculating high-risk significant factors and automatically recommending clinical decision suggestions; the personalized diagnosis and treatment module is used for constructing a process knowledge graph, carrying out personalized treatment recommendation and distributing corresponding rehabilitation execution tasks for different roles; and the doctor-patient interaction module is used for establishing a multi-terminal collaborative micro-service architecture and carrying out multi-terminal collaborative management and early warning information real-time pushing. The cardiovascular adverse event prediction accuracy and efficiency of the chronic kidney disease patient can be effectively improved, personalized treatment schemes are pushed to different patients, and early diagnosis and early intervention of chronic kidney disease related complications are achieved.

Certain aspects of the present disclosure provide a monitoring system comprising a continuous analyte sensor configured to penetrate a skin of a patient and generate a sensor current indicative of analyte levels of the patient, and a sensor electronics module coupled to the continuous analyte sensor. The sensor electronics module comprises an analog to digital converter configured to receive the sensor current and convert the sensor current generated by the continuous analyte sensor into digital signals, a processor configured to convert the digital signals to a set of analyte measurements indicative of the analyte levels of the patient, and a Bluetooth antenna configured to transmit the set of analyte measurements wirelessly to a wireless communications device using Bluetooth or BLE communications protocols.

Organ transplantation is currently recognized as the treatment of choice for patients with end-stage renal disease (ESRD). To date, no risk-stratification system exists that adequately predicts transplant patients' individual risk of allograft loss. Described herein the development and validation of an integrative risk prediction score to predict kidney allograft survival of individual patients. A methodology of predicting whether a kidney transplant recipient is at risk of having allograft loss is accomplished by implementing the iBox risk prediction score. The methodology can be achieved using a computer-readable storage medium with program instructions which, when executed by a data-processing unit, cause execution of at least the step of implementing an algorithm of said method of predicting whether a kidney transplant recipient is at risk of having allograft loss.

The invention discloses a potential nephropathy prediction electronic nose system and method based on machine learning, and belongs to the technical field of medical detection. According to the system, ammonia concentration and temperature and humidity parameters in exhaled air of a human body are collected through a multi-mode sensor module, baseline calibration, flow velocity compensation and noise filtering are carried out in combination with a data preprocessing module, and a standardized characteristic matrix is generated. The machine learning prediction model adopts a Bayesian deep learning or integrated learning algorithm, inputs an ammonia concentration peak value, an integral area and temperature and humidity parameters, outputs a nephropathy risk probability and stage prediction, and quantifies key influence factors through an SHAP value (for example, a high-risk alarm is triggered when an ammonia concentration contribution degree is greater than or equal to 60%). The system is equipped with a real-time GUI interaction interface, dynamically displays a risk thermodynamic diagram and multi-dimensional data overlay analysis, and supports clinicians to feed back and correct. And the data iteration module realizes continuous optimization of the model through an encrypted cloud storage and incremental learning framework (Online SGD). A heterogeneous sensor array (such as a Fe2Mo3O8 / MoO2 (at) MoS2 composite material) and a dynamic temperature and humidity compensation technology are adopted, so that the detection stability is remarkably improved, and the clinical misdiagnosis rate is reduced. The system can be integrated in a portable device or a hospital system, is suitable for community screening, emergency triage and remote medical scenes, and has the advantages of noninvasive, high-precision and cross-mechanism collaborative diagnosis.

The invention relates to proteins for use in the prevention, treatment or amelioration of kidney diseases or kidney damage, more particularly where the protein is a follistatin-fusion protein.

Systems, methods and computer-readable media are provided for identifying patients having an elevated near-term risk of chronic kidney disease (CKD) progression, including predicting an individual's risk of progression to Stage 3 CKD within a future time interval, which may be up to 36 months. Based on the prediction, appropriate care providers may be notified so that the risk of CKD progression may be mitigated. In an embodiment, measurements of physiological variables are obtained, including serial measurements for uric acid levels from a longitudinal time series of serum or plasma samples spanning the previous two to five years. An annualized uric acid velocity of the patient is determined and used to generate a multivariable mathematical model for determining a likelihood of risk for developing Stage 3 CKD within 36 months.

The present disclosure provides a method and an apparatus for predicting a progression trajectory from acute kidney injury (AKI) to a kidney disease. The method includes the following steps: receiving a first set of features of a particular acute kidney injury (AKI) patient; selecting a second set of features from the first set of features using a preset algorithm; and predicting, using a first machine-learning model, a progression trajectory of a kidney disease of the particular AKI patient based on the second set of features.

Exemplary systems and methods for estimating progression of chronic kidney disease in a patient and applying clinical interventions may include determining historic values of at least one patient parameter that varies as a function of the progression of the chronic kidney disease over time, and computationally estimating a trend corresponding to the historic values. Based on the trend, at least one marker may be automatically provided that identifies a clinical intervention and a time in the future when the clinical intervention is expected to be needed. Based on the at least one marker, clinical preparations may be executed at a time prior to the administration of the intervention in order to improve at least one of a) timeliness of the execution of the intervention and b) effectiveness of the intervention.

The invention discloses a fundus lesion evaluation method and device for chronic nephropathy, and the method comprises the steps: obtaining an ultra-wide-angle fundus image of a to-be-detected target object with chronic nephropathy, and extracting fundus features from the ultra-wide-angle fundus image; based on the extracted fundus features, utilizing a multivariable regression model to predict a fundus lesion state; performing multi-modal fusion processing on the fundus lesion state, the patient information of the to-be-detected target object and the clinical information; and performing lesion probability prediction processing of a dynamic confidence interval on the multi-modal fusion features to generate a chronic kidney disease fundus lesion risk level. Therefore, the fundus lesion risk grade of the chronic kidney disease can be accurately predicted, and the reliability of evaluation of the fundus lesion risk grade of the chronic kidney disease is improved; in addition, a set of evaluation standards for the risk level of fundus lesions complicated by early chronic nephropathy are also constructed, so that the purposes of early discovery and early treatment of patients are achieved, and irreversible fundus lesions caused by CKD are reduced.

The present invention discloses a method and system for intelligent analysis, prediction and early warning of the health status of chronic kidney disease, and relates to the technical field of medical data analysis and prediction. The method for intelligent analysis, prediction and early warning of the health status of chronic kidney disease obtains several historical health measurement data of the patient, and the historical health measurement data includes historical blood data, historical urine data, and historical physiological data; the historical health measurement data of each time of the patient is evaluated and analyzed respectively. The present invention integrates and dynamically analyzes the historical blood, urine and physiological data to construct a health assessment index for each time, and combines these indexes to form a comprehensive view of the health status of the kidney. Compared with traditional methods, this method can more accurately predict changes in renal function and potential lesions by considering multiple health dimensions and using trend analysis of historical data, and solves the problem of misjudgment that may be caused by a single data point in the prior art.

The present invention relates to the technical field of neural network models, and specifically to a chronic kidney disease risk screening modeling method and system, the method comprising: obtaining the target age of a patient to be screened and the target detection index under each preset detection factor; obtaining the feature weight of the target community to which the target age belongs under each preset detection factor; screening through a chronic kidney disease risk screening network model according to the target feature weight and the target detection index, and obtaining the risk screening factor corresponding to the patient to be screened, wherein during the screening process, the target feature weight under the preset detection factor is used as the initialization weight of the chronic kidney disease risk screening network model for the preset detection factor. The present invention analyzes age and target detection indexes, and adaptively sets the initialization weight of each preset detection factor in the chronic kidney disease risk screening network model, thereby improving the rationality of the initialization weight setting of the neural network model and the efficiency of risk screening.

Provided herein are compounds that are useful in treating cancer, metabolic disease, cardiovascular disease, and kidney disease.

The invention relates to the field of immune assessment, in particular to an end-stage nephropathy dialysis patient immune assessment method, which comprises the following steps: preparing a database for storing assessment scores of various parameters; preparing a convolutional neural network, and collecting immune intensity change data before and after dialysis of a patient population as a training library; immune intensity change data before and after dialysis under experimental conditions are obtained under laboratory conditions, the data under the experimental conditions are input into a convolutional neural network, and the convolutional neural network is used for training according to a preset mixing proportion rule and outputting a dialysis cause compensation function; data such as demographic characteristics, basic diseases, representative immune activation state data, representative immune senescence state data and dialysis frequency of a patient are collected, a total score is calculated based on a database and a dialysis cause compensation function, and the immunocompetence of the patient is evaluated. By adopting the method, the immunocompetence fluctuation caused by imperfect biocompatibility of the dialysis membrane can be compensated in immune assessment.

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.

Disclosed are compounds according to Formula (I) or (II), and pharmaceutical compositions comprising them. Also disclosed are therapeutic methods, e.g., of treating kidney diseases, using the compounds of Formula (I) or (II).

In one aspect, the invention provides compounds of Formula I, and salts, hydrates and isomers thereof. In another aspect, the invention provides a method of promoting bone formation in a subject in need thereof by administering to the subject a therapeutically effective amount of a compound of Formula I. The present invention also provides orthopedic and periodontal devices, as well as methods for the treatment of renal disease, diabetes, bone loss, and cancer, using a compound of Formula I.

The disclosure relates to methods and compositions for the treatment of chronic kidney disease.

The disclosure features methods and compositions for the treatment of perivascular fibrosis and other hypertensive diseases and conditions, cardiovascular diseases, and chronic kidney disease.

It refers to a dietary product for hypoproteic diet suitable for supplementing the diet in patients suffering from chronic kidney disease, hepatic insufficiency and urea cycle disorders. It also refers to the use of the dietary product as supplement of the diet of said patients. The dietary product comprises amino acids, amino acid analogues, and pharmaceutically acceptable excipients.

Provided herein are anti-PAPP-A antibodies. Also provided are methods of using such antibodies to treat PAPP-A related disorders, such as kidney disease.

The present disclosure relates to methods related to the treatment of B-cell responsive autoimmune glomerulopathies.

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 disclosure is directed to a method for predicting whether a patient is at risk for developing Fast Progressing Renal Disease (FPRD) and treating patients to avoid such risk.

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.

Methods and kits for assessing the propensity of developing urolithiasis, kidney disease, or bladder disease in pets; and compositions to treat such conditions are described herein.

1. The name of the product under this design: Nursing pants. 2. Purpose of this design product: used for nursing pants for kidney disease, with pockets on both sides for holding urine bags. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.