214 results about "Risk stratification" patented technology

A method and system for generating a patient specific anatomical heart model is disclosed. A sequence of volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. A multi-component patient specific 4D geometric model of the heart and aorta estimated from the sequence of volumetric cardiac imaging data. A patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model is generated. Patient specific material properties of the aortic wall are estimated using the 4D geometrical model and the 4D computational model. Fluid Structure Interaction (FSI) simulations are performed using the 4D computational model and estimated material properties of the aortic wall, and patient specific clinical parameters are extracted based on the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.

A system and method that remotely accesses and diagnoses the medical condition of an individual patient and of each patient in a group of patients and provides treatment based upon the diagnoses of the individual patient and the risk stratification the individual patient assumes in the group of patients.

A data processing system is provided for determining clinical outcomes of medical data gathered by the system. The system can allow a person to define a medical study and can then administer the medical study and can collect and analyze data from potentially geographically diverse doctors, patients and other people associated with a study. Users enter sets of medical information. The system can analyze the medical data according to any number of clinical algorithms that may be custom defined and edited before and during the study. The system conditionally outputs the clinical outcome to the user. The clinical outcome can be used for treatment of patients participating in the study immediately after the data is input and analyzed. The medical outcomes can indicate such things as performance comparisons, composite outcomes, and risk stratification and assessments for such things as treatments, drugs, illnesses, doctors, patients and physician groups.

The invention discloses a tumor malignant risk stratification auxiliary diagnosis system of an artificial intelligence medical image. The system comprises a data acquisition module, a data preprocessing module, a model establishment module, a model verification and optimization module, a stratification diagnosis module and a database platform. The tumor malignant risk stratification auxiliary diagnosis system of the invention is based on artificial intelligence technology, successive stratification of the malignant risk of the tumor can be achieved, the clinical diagnosis thinking is simulated, based on the high-precision ability of detecting benign lesions and malignant tumors of artificial intelligence model, and the space-occupying lesions with definite imaging features are diagnosed automatically. As a result, the system can substantially assist the clinical management decision of space-occupying lesions, improve the existing work flow of clinical diagnosis, increase the confidenceof doctors in diagnosis, reduce the work pressure, reduce the anxiety of patients with low-risk malignant lesions, greatly improve the diagnostic rate of benign lesions and malignant tumors, and hopefully realize the landing implementation of artificial intelligence clinical auxiliary diagnosis.

The present invention relates to methods and compositions for symptom-based differential diagnosis, prognosis, and determination of treatment regimens in subjects. In particular, the invention relates to the use of biomarkers, either individually or in combinations with one another to rule in or out diseases of the aorta and its branches, most particularly aortic aneurysm and/or aortic dissection, and for risk stratification in such conditions. Preferred markers include one or more of creatine kinase-BB (CK-BB), creatine kinase-MB (CK-MB), acidic calponin, basic calponin, B-type natriuretic peptide (BNP), NT-proBNP, proBNP, BNP_79-108, BNP_3-108, caldesmon, caspase-3, D-dimer, soluble elastin fragments, endothelial cell-selective adhesion molecule (ESAM), fibrillin-1, heart-type fatty acid binding protein, MMP-9, myeloperoxidase, myoglobin, smooth muscle myosin, smooth muscle myosin heavy chain, TIMP-1, free cardiac troponin I, complexed cardiac troponin I, free and complexed cardiac troponin I, free cardiac troponin T, complexed cardiac troponin T, and free and complexed cardiac troponin T, and preferred assays are configured to detect these markers.

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

Improved risk categorization is provided for clinical decision support and forecasting future health care spend. A risk index is provided that improves on other risk stratification models by synthesizing electronic medical records and health questionnaires with an individual patient's claim histories. Machine learning algorithms catalogue patients into distinct group clusters, based on risk which may be associated with annual health care spending, thereby enabling administrators to forecast future health care spending on the individual and population level.

The present invention provides an approach for the simultaneous determination of the activation states of a plurality of proteins in single cells. This approach permits the rapid detection of heterogeneity in a complex cell population based on activation states, and the identification of cellular subsets that exhibit correlated changes in activation within the cell population. Moreover, this approach allows the correlation of cellular activities or properties. In addition, the use of potentiators of cellular activation allows for characterization of such pathways and cell populations.

The invention provides a cardiopulmonary exercise assessment and exercise training integrated system. The system comprises a case big data intelligent management system, a cardiopulmonary exercise function evaluation and personalized exercise prescription formulation system which exchanges data with the case big data intelligent management system, an exercise equipment-based rehabilitation training and prescription optimization system, a guidance and monitoring system for walking and other free exercises, and clients. According to the cardiopulmonary exercise assessment and exercise training integrated system of the invention, with the case big data intelligent management system adopted as the core, a digital cardiopulmonary walking test is adopted to perform cardiopulmonary exercise function assessment and risk stratification, and exercise prescriptions are prescribed; the exercise training of a patient on exercise equipment is controlled and monitored according to the case data and exercise prescription of the patient; a rehabilitation assistant is adopted to remind a user about the implementation of the five major prescriptions of the patient and monitor the implementation of the five major prescriptions of the patient; the doctor client, family client and professional service client of the system provide personalized and professional rehabilitation services at any time, andprovide intelligent, standardized and personalized medical and rehabilitation services.

The present invention relates to methods for providing a diagnosis, prognosis and/or risk stratification of a subject with heart failure, comprising determining the concentration of osteopontin (OPN) in the biological sample, preferably in a plasma sample. An OPN cut-off value is discloses as a valuable reference value. The present invention furthermore relates to the use of osteopontin as marker for diagnosis, prognosis and/or risk stratification of a subject with heart failure, the use of the determination of the osteopontin plasma concentration in a biological sample of a subject for diagnosis, prognosis and/or risk stratification of heart failure as well as kits for performing the methods and uses of the invention. The present invention allows particularly for risk stratification of patients with heart failure, such as mortality prediction and prognosis of heart failure severity.

A method for real-time threat monitoring includes identifying two or more real time vulnerabilities, each associated with one or more objects of an enterprise, correlating the two or more real-time vulnerabilities to each other, applying a risk tiering model to the correlated real-time vulnerability, thereby classifying the correlated real-time vulnerability into risk tiers, and initiating an alert based on the correlated real-time vulnerability and the risk tiers into which the correlated real-time vulnerability is classified. According to other embodiments a method includes applying a risk methodology to log data contained in one or more object logs thereby identifying one or more security events, applying a risk tiering model to the one or more security events, thereby classifying the security events into risk tiers, and initiating an alert based on the security events and the risk tiers into which the security events are classified.

The present invention relates to a method and means for diagnosing or risk stratification of co-morbidities, particularly cardiovascular complications in diabetes patients. The invention particularly relates to a method for diagnosing whether a diabetes patient is suffering from a cardiovascular complication or is at risk of suffering from a cardiovascular complication, said method comprising the steps of (a) measuring, preferably in vitro, the level(s) of at least one cardiac hormone or a variant thereof in a sample from the patient, and (b) diagnosing the cardiovascular complication or the risk of suffering from cardiovascular complication by comparing the measured level(s) to known level(s) associated with the cardiovascular complication or risk. The present invention also relates to combining the measurement of markers comprising cardiac hormones, natriuretic peptides, inflammation-associated markers, angiogenesis markers, and markers for platelet activation. Preferred markers are brain natriuretic peptides (particularly NT-proBNP), PIGF, and sCD40L.

The present invention refers to a use of an ex vivo method comprising the determination of PlGF and sFlt-1 in a sample for diagnosis, risk stratification and/or monitoring of a vascular disease with atherosclerotic etiology, in particular a coronary heart disease such a unstable angina pectoris or myocardial infarction, and/or for estimation of the probability of developing such a disease, as well as for identification of a patient supposed to benefit from a therapy by agents reducing the risk for a cardiovascular disease. In the method (i) a ratio of [PlGF=high:sFlt-1=low], and/or (ii) a PlGF concentration in the upper two tertiles of a reference collective, and an sFlt-1 concentration in the lower tertile of the reference collective, and/or (iii) a PlGF result above a PlGF reference value, and an sFlt-1 result below an sFlt-1-reference value indicate an elevated probability for an adverse event. The present invention also refers to the used method. The present invention further refers to a diagnostic kit and its use as well as to an assay element and its use.

The present invention is directed to methods for atherosclerosis risk reduction including initial risk stratification, goal setting, and goal attainment for patients with, or at risk for, atherosclerosis. The present invention may be embodied in a computer implemented software product, the modules and sub-routines resident on a computer or hand held device, allowing a physician to determine the best strategy for coronary artery disease prevention based on such risk assessment values as Framingham score, genetic predisposition, biomarker levels and atherosclerosis imaging scores. The software product is supported by a backend database containing risk assessment value scores for a patient population of known clinical outcome. The database may reside in a memory unit, such as a hard drive, of the computer or hand held device, or may be accessed remotely in a distributed computer environment.

A method of detecting and diagnosing cancers characterized by the presence of at least one nodule/neoplasm from an imaging scan is presented. To detect nodules in an imaging scan, a 3D CNN using a single feed forward pass of a single network is used. After detection, risk stratification is performed using a supervised or an unsupervised deep learning method to assist in characterizing the detected nodule/neoplasm as benign or malignant. The supervised learning method relies on a 3D CNN used with transfer learning and a graph regularized sparse MTL to determine malignancy. The unsupervised learning method uses clustering to generate labels after which label proportions are used with a novel algorithm to classify malignancy. The method assists radiologists in improving detection rates of lung nodules to facilitate early detection and minimizing errors in diagnosis.

Devices and methods for detecting heart failure (HF) events or identifying patient at elevated risk of developing future HF events are described. A medical device can detect contextual condition associated with a patient, such as an environmental context or a physiologic context, sense a heart sound signal, and perform multiple measurements of heart sound features in response to the detected patient contextual condition meeting specified criterion. The contextual condition includes information correlating to or indicative of a change in metabolic demand of a patient. The medical device can use the physiologic signals to calculate one or more signal metrics indicative of diastolic function of the heart such as a trend of the heart sound features. The medical device can use the signal metrics to detect an HF event or to predict the likelihood of the patient later developing an HF event.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.