20 results about "Diseases onset" patented technology

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 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 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.

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.

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.

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.

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.

To predict when subjects with mild cognitive impairment will develop Alzheimer's disease. The Alzheimer's disease onset prediction device (1) calculates the hippocampal volume [mm ] of a subject with mild cognitive impairment relative to the Alzheimer's Disease Assessment Scale-Cognition score of the subject. 3 ]; a memory unit 20 that stores a cumulative distribution function determined by a predetermined n and a predetermined λ; a predicted onset time information calculation unit 30 that calculates predicted onset time information indicating the time when the subject is predicted to develop Alzheimer's disease based on the volume score value acquired by the acquisition unit 10 and the probability distribution function stored in the memory unit 20; and an output unit 40 that outputs the predicted onset time information calculated by the predicted onset time information calculation unit 30 to the outside.

The present invention provides a radiomics-based method and system for predicting the onset of human diseases using medical imaging and advanced machine learning techniques. This non-invasive approach combines Convolutional Neural Networks (CNNs) with pseudo-color transformation in the CIELAB color space to enhance early disease detection. The method begins by acquiring grayscale medical images from diagnostic techniques such as CT, MRI, or X-ray, followed by CNN-based feature extraction to identify clinically relevant regions of interest. These regions are then converted into pseudo-color representations using the CIELAB color space, improving tissue contrast and visualization of subtle abnormalities. A machine learning classifier is applied to the pseudo-colored images to predict the likelihood of disease onset, generating an output report that includes a heatmap, probability score, and diagnostic recommendations. The invention offers a fully automated process that facilitates early detection, improved visualization, and personalized diagnostics, providing a versatile solution for various medical conditions.

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.