30 results about "Clinical variables" patented technology

A method and system for predictive modeling of patient outcomes. The predictive method includes the steps of applying an algorithm to patient data and displaying predicted patient data. The predictive method may further include the step of adjusting one or more clinical variables. The system includes a database of patient data, a rules engine operably connected to the database wherein the rules engine is capable of applying algorithms to the patient data to generate predicted patient data, and a user interface operably connected to the database.

Embodiments include systems and methods for determining cardiovascular information for a patient. A method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of the patient's vasculature based on the patient-specific data; and creating a computational model of a blood flow characteristic based on the anatomic model. The method also includes identifying one or more of an uncertain parameter, an uncertain clinical variable, and an uncertain geometry; modifying a probability model based on one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry; determining a blood flow characteristic within the patient's vasculature based on the anatomic model and the computational model of the blood flow characteristic of the patient's vasculature; and calculating, based on the probability model and the determined blood flow characteristic, a sensitivity of the determined fractional flow reserve to one or more of the identified uncertain parameter, uncertain clinical variable, or uncertain geometry.

An informatics platform provides an architecture to integrate information from relevant patient information systems. The informatics platform may include: a workflow tool that can be used to prepare and review information at multi-disciplinary board meetings; a visual timeline of patient events; a search engine to search for patients with specific attributes; a graphing tool that can display disparate clinical variables in a single chart; a virtual Pin Board for users to identify relevant patient information for board meetings; an image viewing application that can provide for comparison of images from different information systems; structured reporting functionality that incorporates system aggregated patient information and board recommendations; an application interface that integratesclinically relevant tools to provide patient specific references; a collaboration interface that facilitates communication of patient specific information and documents the discussion threads as independent reference points; and a default display of relevant patient information customized for each clinical specialty.

The invention is a method of predicting the evolution of a patient suffering of a neurovascular disease, preferably stroke, comprising obtaining a biological sample of said patient, assessing in said biological sample the level of endostatin and FasL, determining whether said levels of endostatin and FasL together are above or below predetermined cut-off levels and predicting the functional outcome of said neurovascular disease on said patient evaluating the result of the previous step. The combination of endostatin and FasL is a more powerful predictor that any clinical prognostic tool and adds significant prognostic value to all other clinical variables of the art.

The invention discloses a diagnosis prediction method of a bidirectional recurrent neural network based on attention, and relates to the technical field of prediction diagnosis. The diagnosis prediction method firstly embeds high-dimensional medical codes (ie, a clinical variable) into a low code layer space, and then inputs code representation into a bidirectional recurrent neural network based on attention to generate hidden state representation. The diagnosis prediction method inputs the hidden representation through a softmax layer so as to predict medical codes accessed in future. The experimental data shows that, by using the diagnosis prediction method of a bidirectional recurrent neural network based on attention, when predicting future access information, the attention mechanism can assign different weights to previous accesses, thus effectively completing the diagnosis and prediction task, and reasonably explaining the prediction result.

A system and method are provided that employ the variability of physiological waveforms to estimate the time to death after WLST, or time to inadequate organ perfusion. From the variability data one can derive an index subsequently used to determine the probability of death (or inadequate organ perfusion) within a given time frame in an automated fashion from bedside monitors in the intensive or post-anesthesia care unit. The resulting variability index can also be combined with the clinical variables used in other death prediction tools to enhance the performance and outcome when compared to existing models. In at least one implementation, variability monitoring at the bedside could be used to provide estimates of the probability that a patient will die within a certain time frame after WLST. These estimates could be used to reduce the distress of the patients' families, as well as optimize the use of resources surrounding donation.

The invention relates to a mechanical ventilation system (10) for respiration of a patient (5), the system being adapted for providing decision support for mechanical ventilation. Control means (12) is adapted for using both first data (D1) and second data (D2), indicative of the respiratory feedback in the blood, in physiological models (MOD) descriptive of, at least, lung mechanics, and/or gas exchange in the lungs of the patient, the physiological models comprising a number of model parameters (MOD_P). The control means is further arranged for simulating the effect on one, or more, model parameters (MOD_P) of the physiological models for a suggested value of the positive end expiratory pressure (PEEP) setting for the ventilation means, and thereby provide decision support in relation to said suggested PEEP value. The invention is advantageous for providing mathematical based models of changes in physiology in response to changes in ventilator settings of the PEEP thereby allowing mathematical physiological models to predict changes in clinical variables for a given value of PEEP.

A method for prognosing osteoporosis phenotypes or estimating osteoporosis quantitative traits comprising determining outcomes for selected SNP variables and clinical variables. Products and methods for genotyping multiple osteoporosis associated genetic variations.

A method for prognosing recurrence of prostate cancer (PCa) in a subject following prostatectomy using the outcomes of selected single nucleotide polymorphisms (SNPs) and clinical variables. A method for genotyping PCa associated genetic variations comprising use of a DNA microarray. A microarray for use in the described methods.

The invention provides a DME prognostic information prediction system based on integrated machine learning and an application method thereof. The DME prognostic information prediction system is characterized in that a preprocessing module for preprocessing data; a feature extraction module performs image feature extraction on the preprocessed image by using three deep learning models; a network construction module is used for constructing a deep learning network; a feature fusion module fuses the obtained image features; a data processing module processes the image fusion features and text data generated by the deep learning network to generate a probability distribution map; and a prediction module generates prediction values of CFT and BCVA according to the probability distribution map.The multi-mode human activity recognition system and the application method thereof can construct a deep learning network through the network construction module to process the OCT image, can obtain fused image features and text features of the clinical variables by adding the clinical variables, and finally, performs CFT and BCVA prediction by the prediction module, so that objective prediction values are accurately provided, and the prediction precision is effectively improved, and the defects of a traditional prediction method are overcome.

A method for prognosing a rheumatoid arthritis phenotype using the outcomes of selected single nucleotide polymorphisms (SNPs) and clinical variables. A method for genotyping multiple rheumatoid arthritis associated genetic variations comprising use of a DNA microrarray. A microarray for use in the described methods.

The invention discloses an acute coronary syndrome symptom diagnosis model construction and application method. The construction method comprises the following steps: collecting a blood sample and clinical data of a patient suffering from chest pain in hospital admission, centrifuging the blood sample by using an EDTA (Ethylene Diamine Tetraacetic Acid) anticoagulation tube, and separately storing a plasma sample obtained after centrifugation in a refrigerator at the temperature of-80 DEG C; the method comprises the following steps: carrying out protein precipitation on a plasma sample, oscillating, centrifuging, taking a supernatant for sample introduction, separating 12 ceramides in the plasma sample by using an ultra-high performance liquid chromatography system, establishing a calibration curve by using a mass spectrometer through a mass spectrum isotope internal standard quantification method, and detecting 12 ceramide molecule concentration values of all plasma samples; fitting a linear model of a logistic regression method based on traditional clinical variables of a patient with chest pain and troponin and ceramide data, and calculating optimal model parameters to obtain an acute coronary syndrome symptom diagnosis model; based on the acute coronary syndrome symptom diagnosis model, whether the patient is an ACS patient can be rapidly judged, and the accuracy is high.

The invention relates to a decision analysis method based on clinical variable attributes, and the method comprises the steps: employing a brand-new logic design method; based on sample objects of thesame data source, obtaining a primary effect value corresponding to the data for the data of each variable attribute corresponding to the sample object based on the discrimination of a numerical typevariable and a classification variable; then, based on the primary effect value, obtaining an L2 norm value of each variable attribute, and further obtaining a weight value of each variable attribute; and finally, combining the primary effect value to obtain an influence effect quantized value of the variable attribute on the sample object. Therefore, the quantitative reaction of the influence ofthe variable attribute on the sample object is realized, an objective and accurate data reference basis is provided for subsequent analysis and application of the variable attribute data, and the accuracy of final data analysis and application is effectively improved.