35 results about "Diseases onset" patented technology

Provided are methods of training a model to predict a disease onset risk. Aspects of the methods include obtaining a plurality of electronic health records, dividing the subjects into a first set for machine learning model training and a second set for machine learning evaluation, training a machine learning model using the electronic health records of the first set of subjects, evaluating the machine learning model using the electronic health records of the second set of subjects and identifying from the trained and evaluated machine learning model one or more features of the subjects that predict the disease onset risk. In some embodiments, the one or more features are one or more sex-specific features. Also provided are methods of predicting a disease onset risk in a subject, methods of clinically evaluating the presence of a disease in a subject and methods of treating a subject predicted to be at risk of developing a disease. Embodiments of the invention are computer-implemented methods. Systems and non-transitory computer-readable mediums for performing the subject methods are also provided.

The objective of the present invention is to provide a disease onset risk prediction device for predicting the onset risk of liver disease from disease-related information about a subject of prediction.　A disease onset risk prediction device according to the present invention includes an information acquisition unit, a prediction unit, and an output unit, wherein: the information acquisition unit acquires disease-related information about a subject of prediction, the disease being liver disease, and the disease-related information including at least one among information acquired by health diagnosis and information acquired by the subject of prediction; the prediction unit predicts a disease onset risk of the subject of prediction from the disease-related information; and the output unit outputs the disease onset risk.

A computer-implemented method for predicting cardiovascular disease risk, the method including: receiving a first patient history data comprising imaging data and / or non-imaging data for a patient at a first time point; selecting prediction report parameters defining a type of cardiovascular event and a risk prediction time scale; processing the first patient history data using a trained machine learning model configured to predict disease onset, progression, and / or regression over time, wherein the trained machine learning model is trained using patient subsets created based on patient history characteristics and outcomes; generating a risk prediction for the selected type of cardiovascular event over the selected risk prediction time scale based on the processed first patient history data; generating a risk prediction report based on the risk prediction; and outputting the risk prediction report.

The invention relates to a disease early warning method and device, electronic equipment and a medium, and belongs to the technical field of medical decision, and the method comprises the steps: obtaining image data and physiological signal data of a patient; inputting the image data into a fully trained convolutional neural network model to obtain a first feature, and inputting the physiological signal data into a fully trained long-short-term memory network model to obtain a second feature; inputting the first feature and the second feature into a fully trained dynamic weighted graph attention network model to obtain a comprehensive feature; the comprehensive features are input into a decision model which is completely trained, a disease attack risk value is obtained, early warning is carried out based on the disease attack risk value, and the decision model is constructed based on a neural network. The structural features of the image data are extracted through the neural network, the time sequence features of the physiological signal data are extracted through the long-short-term memory network, multi-modal medical data deep fusion is achieved, and the diagnosis precision is effectively improved.

A system (1) includes an electronic processor (16, 20) integrated with or in wireless communication with an associated monitoring device (10). The electronic processor is programmed to receive physiological sensor data (13) of an associated human subject measured using the associated monitoring device (10); determine whether the associated human subject has indicia of onset an illness by applying a pre-trained artificial intelligence (AI) model (24) to the received physiological sensor data; and output a warning (26) in response to a determination that the associated human subject has indicia of onset of the illness. The pre-trained AI model is pre-trained to determine whether the human subject has received a vaccination.

The invention provides a CKD early screening marker based on plasma proteomics. The early screening marker comprises the following five kinds of protein: IGFBP4, GDF15, EDA2R, HAVCR1 and XG. The early screening marker provided by the invention can provide ultra-early warning within 16 years before disease attack, is high in prediction accuracy, and is superior to the conventional model in short-term and long-term prediction aspects; meanwhile, a key proteome pathway is disclosed, and reference can be provided for clinical application and prevention strategies. The invention also provides application of the early screening marker based on the CKD.

The purpose of the present disclosure is to provide a disease onset risk prediction device for predicting the risk of onset of a renal disease on the basis of disease-related information of a prediction subject.　This disease onset risk prediction device includes an information acquisition unit, a prediction unit, and an output unit. The information acquisition unit acquires disease-related information of a prediction subject, the disease being a renal disease. The disease-related information includes at least one of information acquired by a health diagnosis and information acquired by the prediction subject. The prediction unit predicts a disease onset risk of the prediction subject from the disease-related information, and the output unit outputs the disease onset risk.

The purpose of the present disclosure is to provide a disease onset risk prediction device for predicting the onset risk of lung cancer regardless of the smoking status of a prediction subject.　A disease onset risk prediction device according to the present disclosure includes an information acquisition unit, a prediction unit, and an output unit. The information acquisition unit acquires disease-related information of a prediction subject. The disease is lung cancer. The disease-related information includes information about the age, a carcinoembryonic antigen (CEA), and the 1-second rate (FEV10). The prediction unit predicts the onset risk of the disease in the prediction subject from the disease-related information. The output unit outputs the onset risk of the disease.

The purpose of the present invention is to provide a disease onset risk prediction device for predicting the risk of onset of a skeletal disease or joint disease from disease-related information of a prediction target individual.　The disease onset risk prediction device according to the present disclosure comprises an information acquisition unit, a prediction unit, and an output unit, wherein: the information acquisition unit acquires disease-related information of a prediction target individual; the disease is a skeletal disease or a joint disease; the disease-related information includes at least one of information acquired through a medical checkup and information acquired by the prediction target individual; the prediction unit predicts the risk of onset of the disease in the prediction target individual from the disease-related information; and the output unit outputs the disease onset risk.

The invention provides methods for prognosing an increased risk of early onset preeclampsia (EOPE) in a pregnant subject before the disease onset. The methods comprises quantifying small extracellular vesicles that express pairs of biomarkers selected from CD10, CD63 and placental alkaline phosphatase (PLAP) in a sample obtained from the subject.

The embodiment of the invention discloses a construction method and system for a disease onset risk prediction model, and relates to the technical field of data processing, and the method comprises the steps: determining a disease node and at least one gene node according to the information of a to-be-trained gene sample; according to the disease node and the at least one gene node, constructing a directed acyclic graph for representing a directional dependency relationship between the gene and the disease; based on a directed acyclic graph, determining a target gene node having a directional dependency relationship with the disease node; performing regression modeling according to the target gene node and the disease node to obtain an initial disease onset risk prediction model; according to the method, the initial disease onset risk prediction model is trained according to the to-be-trained gene sample information, the target disease onset risk prediction model is obtained, subjective deviation existing in manual work can be reduced through the target disease onset risk prediction model, and then the accuracy and reliability of disease prediction are improved.

Methods, systems, and computer-readable media are disclosed herein for generating a disease-agnostic data model that can be used to predict the onset of multiple chronic diseases in individual patients. In an aspect, the data model is made by autonomously selecting features from longitudinal medical records of patients having chronic diseases that will become predictors for the onset of a chronic disease. The features are vectorized around a disease onset date and processed through a recurrent neural network to produce the data model. Then, the data model may leveraged to predict, for new longitudinal medical records that are input, a future time period when another patient is predicted to experience the onset of the chronic disease. The same data model may utilized to make predictions for multiple chronic diseases.

This device (1) is provided with: an exercise sensor (31) for continuously detecting the motion of an exerciser involving intermittent exercise; and a heartbeat meter (32) for continuously measuring the exerciser's heartbeat. The device is further provided with an activity detection unit (12), a heartbeat monitoring unit (13), an activity monitoring unit (14), and a notification processing unit (15). The activity detection unit (12) detects, from the motion of the exerciser, an activity intensity, a high activity-intensity period, and a low activity-intensity period. The heartbeat monitoring unit (13) detects a declining trend in the exerciser's heart rate in every low activity-intensity period and monitors whether or not the present declining trend is lower as compared to a predetermined standard. In the case when the monitoring by the heartbeat monitoring unit is affirmed, the activity monitoring unit (14) monitors whether or not the activity intensity measured in the next high activity-intensity period is lower as compared with the activity intensities detected in the high activity-intensity periods up to the previous one. In the case when the monitoring by the activity monitoring unit is affirmed, the notification processing unit (15) forecasts an outbreak of disease. With this configuration, outbreak of disease in an exerciser during exercise is predicted with high accuracy.

A computer-implemented method for predicting cardiovascular disease risk, the method including: receiving a first patient history data comprising imaging data and / or non-imaging data for a patient at a first time point; selecting prediction report parameters defining a type of cardiovascular event and a risk prediction time scale; processing the first patient history data using a trained machine learning model configured to predict disease onset, progression, and / or regression over time, wherein the trained machine learning model is trained using patient subsets created based on patient history characteristics and outcomes; generating a risk prediction for the selected type of cardiovascular event over the selected risk prediction time scale based on the processed first patient history data; generating a risk prediction report based on the risk prediction; and outputting the risk prediction report.

The application relates to the technical field of cardiovascular internal medicine disease automation, in particular to a cardiovascular internal medicine disease onset risk prediction method and system. The application can effectively evaluate and predict the cardiovascular internal medicine disease, especially the coronary heart disease, by jointly using multiple groups of clinical indexes and multiple image group feature data, the score data is more intuitive to determine the onset degree of the thyroid papillary carcinoma, so that the coronary heart disease onset risk can be accurately judged. Compared with the blood vessel CT angiography technology in the prior art, the application embodiment combines the image group feature data in the ultrasonic image with the clinical indexes, so that the discrimination result is more accurate.

The purpose of the present disclosure is to provide a disease onset risk prediction device for predicting the onset risk of an endocrine disease or a metabolic disease from disease-related information regarding a prediction subject.　A disease onset risk prediction device according to the present disclosure includes an information acquisition unit, a prediction unit and an output unit. The information acquisition unit acquires disease-related information regarding a prediction subject. The disease is an endocrine disease or a metabolic disease. The disease-related information contains at least one of information acquired by a health diagnosis and information acquired by the prediction subject. The prediction unit predicts a disease onset risk of the prediction subject from the disease-related information, and the output unit outputs the disease onset risk.

Provided is a biomarker for predicting disease onset risk. This biomarker for predicting disease onset risk comprises at least one selected from the group consisting of: the single nucleotide polymorphism rs200137244 in the SIGLEC1 gene, the single nucleotide polymorphism rs143664589 in the SIGLEC1 gene, the single nucleotide polymorphism rs111336308 in the SIGLEC1 gene, the single nucleotide polymorphism rs3746636 in the SIGLEC1 gene, the single nucleotide polymorphism rs76943436 in the SIGLEC4 gene, the single nucleotide polymorphism rs11084810 in the SIGLEC4 gene, the single nucleotide polymorphism rs1807124 in the SIGLEC5 gene, the single nucleotide polymorphism rs2305773 in the SIGLEC6 gene, the single nucleotide polymorphism rs76001696 in the SIGLEC6 gene, the single nucleotide polymorphism rs2305770 in the SIGLEC8 gene, the single nucleotide polymorphism rs7258951 in the SIGLEC10 gene, the single nucleotide polymorphism rs145769059 in the SIGLEC10 gene, the single nucleotide polymorphism rs200296439 in the SIGLEC11 gene, the single nucleotide polymorphism rs74354979 in the SIGLEC12 gene, the single nucleotide polymorphism rs111981406 in the SIGLEC14 gene, the single nucleotide polymorphism rs71353353 in the SIGLEC16 gene, and the single nucleotide polymorphism rs544574549 in the SIGLEC16 gene.

Disclosed herein are mass spectrometry methods for determining glycoproteoform-based biomarkers. The methods comprise identifying, with a processor from mass spectrometry data of the glycoprotein, a set of glycoproteoforms where each of the glycoproteoforms have a measurable intact mass; generating, with the processor from the identified set of glycoproteoforms, a glycoproteoform network separated by saccharide features, determining, with the processor from the glycoproteoform network, a site-independent prediction of N-glycans mapped to biosynthesis pathways; and generating, with the processor from the determined N-glycans mapped to biosynthesis pathways, a glycan structure. The methods may be used to analyze a glycoprotein in a subject, analyze disease progression, or identify disease onset or a recovery.

The purpose of the present disclosure is to provide a disease onset risk prediction device for predicting an onset risk of a heart disease or a vascular system disease from disease-related information of a target to be predicted.　A disease onset risk prediction device according to the present disclosure includes an information acquisition unit, a prediction unit, and an output unit. The information acquisition unit acquires disease-related information of a target to be predicted. The disease is a heart disease or a vascular system disease. The disease-related information includes at least one of information acquired from a medical examination and information acquired from the target to be predicted. The prediction unit predicts a disease onset risk of the target to be predicted from the disease-related information. The output unit outputs the disease onset risk.

The application discloses application of a glycoprotein in preparation of a product for treating psoriasis, and belongs to the technical field of medicines. The application proves for the first time that the glycoprotein with a specific amino acid sequence can target macrophages and effectively inhibit inflammatory reactions triggered by various psoriasis inducers, and directly targets a key immune link of disease onset to play a role; in-vivo experiments not only prove significant therapeutic effect, but also make clear that the therapeutic effect depends on macrophages through mechanism research, so that verified solutions are provided for the shortage of current macrophage-targeted therapeutic drugs, and a solid core foundation is laid for development of a new type of therapeutic product which can fundamentally intervene in the disease process and reduce the recurrence rate.

Various embodiments provide methods, apparatus, systems, computing entities, and / or the like, providing a temporal disease risk profile describing a likelihood of disease onset over time for an individual in a dynamically interpretable manner. Interpretability of the temporal disease risk profile is enabled by providing additional and contextual information, such as weight distributions of various health indicators, factors, and features. In an embodiment, an example method comprises generating a temporal disease risk profile comprising risk score nodes based at least in part on providing a plurality of record data objects to a risk scoring machine learning model configured to generate a risk score; providing the temporal disease risk profile for display via a first user interface comprising a plurality of interactable node mechanisms each corresponding to a risk score node; and providing a node-specific weight distribution comprising one or more sub-nodal weight values for display via a second user interface.

The present invention relates to the treatment of a ALS patient with oral fausdil at a dose of 180-240 mg / day, wherein the patient is treated beginning at least 24 months following disease onset. This results in an anticipated 25-50% reduction in the average decline over at least three months as measured using the revised ALS Functional Rating Scale.

The present invention relates to the treatment of an ALS patient having disease onset of at least 24 months prior to initiation of treatment with fasudil. Fasudil is administered at a dose of 60-240 mg / day according to specific treatment regimens. This results in an anticipated 25-50% reduction in the average decline over at least three months as measured using the revised ALS Functional Rating Scale.

This invention relates to the fields of biotechnology and medicine, specifically to methods and systems for screening gene mutation sites associated with the risk of complex diseases, constructing multi-gene genetic risk rating models for complex diseases, and predicting the risk of disease onset.

A wellness learning platform may collect and analyze heterogenous data streams representative of multiple individualized behavioral and physiological data / parameters or characteristics of users or subjects, such as vehicle drivers. Such parameters can be observed from the users' own actions or physiology / physiological response(s), as well as from “user-adjacent” behaviors or conditions observed, e.g., from the way users operate a vehicle or interact with the users' environment(s). The parameters can then be used to train personalized models (generated using, for example, a digital twin system or machine-learning (ML) / artificial intelligence (AI) mechanisms with which the collection / analytical platform is operatively connected) to predict the onset of disease conditions. Notifications suggesting remediating actions or instructions in response to identifying some disease onset may be provided to the user.

Described herein are novel methods for the prevention and / or treatment of autoimmune diseases after disease onset associated with CD11b / CD18 activity, such as systemic lupus erythematosus (SLE), comprising administering a therapeutically effective amount of CD11b agonist LA1 ((Z)-4-(5-((3-benzyl-4-oxo-2-thioxothiazolidin-5-ylidene)methyl)furan-2-yl)benzoic acid) or it's choline salt.

The present invention relates to a method for diagnosing gastric cancer or predicting the onset thereof. The present invention also relates to a novel gene variant; a method for preparing a gastric cancer model comprising a genetic mutation; a gastric cancer model comprising the genetic mutation; a composition for diagnosing gastric cancer or predicting the onset thereof; a kit for diagnosing gastric cancer or predicting the onset thereof; and a method for screening a drug for treating or preventing gastric cancer. The method for diagnosing gastric cancer and predicting the onset thereof enables early diagnosis of gastric cancer or prediction and prevention of the risk of gastric cancer occurrence prior to disease onset, thereby significantly reducing the incidence and mortality of gastric cancer. In addition, by observing the presence or absence of genetic mutations or changes in frequency of genetic mutation in response to drug treatment, the method can be utilized for molecular mechanism studies for gastric cancer treatment or for the development of drug screening platforms.

A method for inducing a rat autism model by using a neurotoxin, relating to the technical field of animal model construction. Provided is a method for inducing a juvenile rat autism model by using a neurotoxin. A female rat is injected with β-N-methylamino-L-alanine (BMAA) and then caged with a male rat, and the offspring produced is a juvenile rat autism model; a normally born juvenile rat is injected with BMAA, and a juvenile rat autism model is obtained. The use of a neurotoxin BMAA to establish an autism model can reproduce a mother-to-child transmission process of BMAA, and can also simulate the process by which a newborn is exposed postnatally to the toxin BMAA in an environment, leading to disease onset. The method for constructing an animal model has a relatively simple preparation process, causes few abnormalities, and is beneficial to subsequent research on diagnosis and treatment of autism.

Various embodiments provide methods, apparatus, systems, computing entities, and / or the like, providing a temporal disease risk profile describing a likelihood of disease onset over time for an individual in a dynamically interpretable manner. Interpretability of the temporal disease risk profile is enabled by providing additional and contextual information, such as weight distributions of various health indicators, factors, and features. In an embodiment, an example method comprises generating a temporal disease risk profile comprising risk score nodes based at least in part on providing a plurality of record data objects to a risk scoring machine learning model configured to generate a risk score; providing the temporal disease risk profile for display via a first user interface comprising a plurality of interactable node mechanisms each corresponding to a risk score node; and providing a node-specific weight distribution comprising one or more sub-nodal weight values for display via a second user interface.

Provided is a disease onset and fatality risk prediction model for increasing the accuracy of predicting the onset risk of a cardiovascular disease and the risk of fatality due to onset of the cardiovascular disease.　A disease onset and fatality risk prediction model according to the present disclosure predicts a disease onset and fatality risk by using: disease onset and fatality risk prediction markers that include BNP and / or N-terminal pro-BNP, and ANTR2, MACOI, SP-B, and PLOD3, and that serve as indices for predicting at least one of the onset risk of a cardiovascular disease and the risk of fatality due to onset of the cardiovascular disease; and, as input data, values obtained by quantifying the disease onset and fatality risk prediction markers by using aptamers.