316 results about "Disease status" patented technology

The present disclosure, in some aspects, provides one or more disease state-associated genes for repeat expansion diseases (e.g., a repeat expansion disease associated with spliceopathy, such as DM1). In some embodiments, each of the disease-state associated gene described herein is a disease-state associated gene for a repeat expansion disease (e.g., DM1). Methods of measuring the splicing activity of disease state- associated genes, e.g., for determining disease state and monitoring patient as well as treatment efficacy are also provided.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and / or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and / or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

Presented herein are systems and methods that provide for automated analysis of medical images to determine a predicted disease status (e.g., prostate cancer status) and / or a value corresponding to predicted risk of the disease status for a subject. The approaches described herein leverage artificial intelligence (AI) to analyze intensities of voxels in a functional image, such as a PET image, and determine a risk and / or likelihood that a subject's disease, e.g., cancer, is aggressive. The approaches described herein can provide predictions of whether a subject that presents a localized disease has and / or will develop aggressive disease, such as metastatic cancer. These predictions are generated in a fully automated fashion and can be used alone, or in combination with other cancer diagnostic metrics (e.g., to corroborate predictions and assessments or highlight potential errors). As such, they represent a valuable tool in support of improved cancer diagnosis and treatment.

Described herein are methods such as multi-omic methods for assessing a disease such as cancer. The multi-omic methods may integrate proteomic, transcriptomic, genomic, lipidomic, or metabolomic data. The method screening diseases or disease states. Also described herein are methods for screening for diseases or disease states from biological samples. The methods may include assessing whether a nodule, mass, or cyst is cancerous.

The invention relates to the technical field of auxiliary medical diagnosis, and particularly discloses a three-branch diagnosis and evaluation method and device based on multi-modal remote medical data. Multi-modal remote medical data of a patient is collected and input into three diagnosis and evaluation models for analysis, and if a diagnosis result is clear, whether the patient is ill or not is determined, and a corresponding result is directly output; and if the diagnosis result is not clear, further evaluating the disease risk of the patient and outputting an evaluation result. The method not only can effectively diagnose the illness state and the non-illness state of the patient, but also breaks through the limitation that a traditional intelligent diagnosis model cannot accurately evaluate the illness risk when the illness state of the patient is uncertain or incompletely meets the diagnosis standard.

Provided herein are methods and systems for diagnosing pathological conditions using machine learning and artificial intelligence models. An exemplary method can include loading an abundance matrix that represents mass spectrometry reads of a sample of a plurality of volatile organic compounds (VOCs) extracted from a biological sample. The abundance matrix can represent each of the plurality of VOCs in a mass-to-charge ratio dimension, an abundance dimension, and a retention time dimension. The method can include providing the abundance matrix to a machine-learning model. The machine- learning model can be trained on abundance matrixes of biomarkers of VOCs collected from healthy and diseased subjects. The method can include receiving, from the machine-learning model, a prediction of a state of a disease state.

The invention relates to the technical field of medical systems, in particular to a multi-disease fusion prediction method and system, and the method comprises the steps: obtaining a medical sequence code, and outputting a disease vector; obtaining a gating memory model, obtaining a doctor-seeing time interval, and configuring an additional time gate based on the doctor-seeing time interval and the disease vector; updating the gated memory model based on a time gate to obtain a health memory state and a context disease hiding state; aggregating the context disease hiding states to obtain aggregation states, calculating attention scores corresponding to the aggregation states, and outputting patient disease states based on the attention scores and the aggregation states; demographic statistical characteristics are obtained, and integration is carried out in combination with the disease state of the patient to obtain a combined vector; and mapping the combined vector to disease risk probability distribution to obtain a prediction result. The method and the device have the effect of predicting various diseases through the general data.

The present disclosure provides systems and methods for machine learning classification and assessment of COVID-19 disease based on gene expression data, including prediction of disease severity. In an aspect, a method for determining a COVID-19 disease state of a subject may comprise: (a) assaying a biological sample obtained or derived from the subject to produce a data set comprising gene expression measurements of the biological sample of each of a plurality of COVID-19 disease-associated genes; (b) computer processing the data set to determine the COVID-19 disease state of the subject; and (c) electronically outputting a report indicative of the COVID-19 disease state of the subject.

Presented herein are systems and methods that provide for automated analysis of three-dimensional (3D) medical images of a subject in order to automatically identify specific 3D volumes within the 3D images that correspond to specific anatomical regions (e.g., organs and / or tissue). Notably, the image analysis approaches described herein are not limited to a single particular organ or portion of the body. Instead, they are robust and widely applicable, providing for consistent, efficient, and accurate detection of anatomical regions, including soft tissue organs, in the entire body. In certain embodiments, the accurate identification of one or more such volumes is used to automatically determine quantitative metrics that represent uptake of radiopharmaceuticals in particular organs and / or tissue regions. These uptake metrics can be used to assess disease state in a subject, determine a prognosis for a subject, and / or determine efficacy of a treatment modality.

Methods for assessing disease, such as multi-omics methods, are described herein. A multi-omics method can integrate proteomic data, transcriptomic data, genomic data, lipidomic data, or metabonomic data. The method screens for a disease or a disease state. Also described herein are methods of screening a disease or a disease state from a biological sample. Multi-omics databases and methods of using them are also described herein.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for a learning platform that employs machine learning to identify patterns that describe meaningful subgroups of patients associated with a particular disease, distinct characteristics of each subgroup, and implications of belonging to a specific subgroup in terms of developing into a more severe disease state for patient journey mapping. The system may include a mapping server that includes a medical-event embedding engine that embeds electronic health records into vectors, a clustering engine that identifies clusters of patients with similar patient journeys by operating on the output of the medical-event embedding engine, and a cluster profiling engine that identifies distinguishing medical events for each identified cluster by operating on the output of the clustering engine.

The invention relates to the technical field of image detection, in particular to a pest and disease identification method and system based on unmanned aerial vehicle remote sensing. The method comprises the following steps: acquiring health indexes of each plant; marking suspicious points; spraying preset hormones to the suspicious points; after a first preset time interval, obtaining the health index of the hormone-induced plant again, and distinguishing whether the plant is pathological abnormality or physiological abnormality according to the change of the health index; on the basis of the pathology and physiology distinguishing result, a definite diagnosis threshold value in a preset range of the suspicious point is adjusted, so that when the plant at the suspicious point is pathologically abnormal and the health indexes of other plants in the preset range adjacent to the suspicious point are abnormal, the condition that the other plants are pathologically abnormal can be detected more sensitively; and using the adjusted pest and disease damage early warning threshold value to identify pest and disease damage conditions of plants of which suspicious points are adjacent to a preset range. According to the invention, the pest and disease states of crops can be detected more accurately.

Provided are techniques including receiving patient data; generating, based on the patient data, a patient risk stratification including: generating stratification scoring based on the patient data; and determining, based on the stratification scoring, a binary classification; generating, based on the patient data, a patient risk level assignment including: generating risk level scoring based on the stratification scoring and the binary classification; and determining, based on the risk level scoring, a risk category; generating a set of patient next best actions including: determining, based on the patient data, a patient outcome prediction; and generating, based on the predictions of patient outcomes, the set of patient next best actions; generating a patient disease state transition prediction including: determining, based on the patient data, a set of transition probabilities; generating, a patient unknown identification prediction including: determining, based on the patient data, a disease propensity score; and generating a corresponding patient diagnosis report.

The embodiment of the invention relates to the technical field of time sequence knowledge graph reasoning and medical treatment, and provides a chronic gastritis evolution trend prediction method and device, and the method comprises the steps: obtaining the current illness state data of a chronic gastritis disease prediction target patient; inputting the data into a pre-trained chronic gastritis evolution trend prediction model; deducing the data through a historical interaction mode to obtain first probability distribution of the predicted entity in a time sequence knowledge graph historical vocabulary; inference is carried out through the generation mode, and second probability distribution of the prediction entity in the clinical diagnosis and treatment related entity set is obtained; and fusing the first probability distribution and the second probability distribution to obtain a disease entity with the highest combination probability, and taking the disease entity as a future illness state prediction result of the target patient. Therefore, dynamic evolution trend prediction is carried out on the development of the current illness state of the target patient, the disease state of the target patient can be dynamically evaluated, and a doctor is assisted to make a medical decision.

Disclosed are computer-implemented methods, systems, and media for analyzing patient-reported outcome (PRO) data. A method for determining a method of evaluation and treatment for patients may include receiving PRO data; validating the PRO data; inputting the PRO data to a first machine learning model; generating, using the first machine learning model, based on the PRO data. i) a score indicative of at least one of an activity of the patient's disease, an effectiveness of current treatment, or severity of the patient's reactions, and / or ii) an inference indicative of the disease state of the patient; and generating, recommending, and / or selecting based on the score and / or the inference, an action item, a second method of evaluation, and / or a second method of treatment for the patient.

A method for classifying neurological disease status is described. The method includes acquiring, by a data preprocessor logic, patient image data. The method further includes generating, by a trained artificial neural network (ANN), a classification output based, at least in part, on the patient image data. The classification output corresponds to a neurological disease status of the patient. The trained ANN is trained based, at least in part, on longitudinal source data.

Systems and methods are disclosed herein for classifying one or more disease conditions. In some embodiments, an application stores a common extraction model, the common extraction model trained using training examples for a plurality of diseases. The application stores a plurality of disease classifiers, each disease classifier configured to output whether or not its respective disease is present, each disease classifier trained using training examples for its respective disease. The application receives a selection of a disease and selects a disease classifier from the plurality of disease classifiers corresponding to the disease. The application inputs an image into the common extraction model and receives, as output from the common extraction model, a set of biomarkers extracted from the image. The application inputs the set of biomarkers into the selected disease classifier, the selected disease classifier configured to output whether or not the disease is present in the image.

In one aspect, the present disclosure provides a method of detecting a presence or absence of a biomarker for a disease in the sample, wherein the biomarker comprises: a) a complex of physiologically active target macromolecules or a fragment or portion thereof and target macromolecules that are not physiologically active; b) a conformation of the physiologically active macromolecules or fragment thereof when the physiologically active target macromolecules or the fragment or portion thereof is a complex with a non- physiologically active target macromolecule; c) the conformation of physiologically active target macromolecules or a portion or fragment thereof in a PAT-Tau complex; d) the conformation of non-physiologically active target macromolecules or a portion or fragment thereof in a PAT-Tau complex; or e) a combination of a), b), c), d) and / or e).

The invention discloses a miRNA and disease relation prediction system and method based on causal feature selection. According to the system, the accuracy of miRNA and disease relation prediction is improved through a plurality of modules such as data acquisition, negative sample selection, feature selection and causal analysis, model training and evaluation and the like. The data acquisition module is used for acquiring miRNA expression data and disease characteristics from a biomedical database; the negative sample selection module is used for grouping unknown miRNA-disease relationships through k-means clustering and randomly selecting negative samples; the feature selection module adopts a causal feature selection algorithm to construct a Markov blanket so as to screen out miRNA features directly related to disease states, and redundancy is reduced; the model training module uses a single machine learning algorithm for training, and optimizes the model performance through cross validation; and finally, the evaluation module evaluates the model effect through indexes such as AUROC and AUPR. According to the method, the accuracy of miRNA and disease relation prediction is remarkably improved, and important support is provided for medical research.

A method comprising obtaining a bodily fluid from a subject; contacting the bodily fluid with an adsorbent material comprising a synthetic carbon particle (SCP) to produce a first filtrate having a level of disease mediators (y); contacting the first filtrate with an adsorbent material comprising the SCP and an anion exchange resin where the ratio of SCP to anion exchange resin is in a range from about 0.1:100 to 100:0.1 to produce a second filtrate; contacting the second filtrate with an adsorbent material comprising the SCP and a cation exchange resin where the ratio of SCP to cation exchange resin is in a range from about 0.1:100 to 100:0.1 to produce a third filtrate.

Devices, systems and methods are provided to assist with the monitoring or management of a patient's medical condition, which have one or more sensors sensing individual patient data on or near the patient. This individual patient data corresponds to at least one physiological parameter of the patient and includes a sensor that does not require the patient to apply it or activate it. The data is then transmitted to a processor for computing a risk or status signal that is based on comparison from a baseline related to a patient or related population and an alert or alarm can be generated based on the result of the signal.

Described herein are methods for screening for a disease state. The method may include obtaining multiple data sets and identifying the disease state based on a combination of the data sets. The data sets may include biomolecule measurements obtained by multiple methods, such as through the use of particles and reference biomolecules.

Provided are techniques including receiving patient data; generating, based on the patient data, a patient risk stratification including: generating stratification scoring based on the patient data; and determining, based on the stratification scoring, a binary classification; generating, based on the patient data, a patient risk level assignment including: generating risk level scoring based on the stratification scoring and the binary classification; and determining, based on the risk level scoring, a risk category; generating a set of patient next best actions including: determining, based on the patient data, a patient outcome prediction; and generating, based on the predictions of patient outcomes, the set of patient next best actions; generating a patient disease state transition prediction including: determining, based on the patient data, a set of transition probabilities; generating, a patient unknown identification prediction including: determining, based on the patient data, a disease propensity score; and generating a corresponding patient diagnosis report.

An example method for quantitative systems pharmacology (QSP) includes analyzing, using a computing device, RNA sequencing (RNA-seq) data for a plurality of patients having a disease. The analysis identifies a plurality of differentially expressed genes (DEGs) and a plurality of differentially enriched biological pathways. Additionally, the method includes deriving, using the computing device, a plurality of gene expression signatures associated with each of a plurality of disease states of the disease using the DEGs and the differentially enriched biological pathways. The method also includes identifying, using the computing device, a plurality of drugs predicted to reverse a particular gene expression signature associated with a particular disease state of the disease. The method further includes prioritizing, using the computing device, the drugs predicted to normalize the particular gene expression signature associated with the particular disease state of the disease for further experimental testing.

The invention provides an experimental dyeing result image processing and calculating method. The method comprises the following steps: identifying a dyeing positive region in a preprocessed tissue sample digital image by using a first identification model, and carrying out area quantification on the identified dyeing positive region to obtain positive region area data; using a second recognition model to recognize and extract the intracellular cavitation region in the preprocessed tissue sample digital image, and performing area quantification on the extracted intracellular cavitation region to obtain cavitation region area data; calculating a dyeing positive region area proportion based on the positive region area data, and calculating a cavitation region area proportion based on the cavitation region area data; and inputting the area proportion of the dyeing positive region and the area proportion of the cavitation region into a preset disease evaluation model, and outputting a disease state evaluation result. According to the method, the accuracy and comprehensiveness of experimental dyeing image analysis are improved, and the automation degree and result credibility of pathological analysis can be remarkably improved.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

The development of a methylation capture panel that targets methylation sites that vary between tissues at high sequencing depth means that the projected cost for a sample run on the proposed capture technology is 10-fold smaller than with whole genome bisulfite sequencing (WGBS). This unexpected combination of methylation sequencing, cell type identification, machine learning, and capture technology enables a previously unattainable ability to predict disease status. These methods can be used to diagnose, prognose, and stage disease conditions including amyotrophic lateral sclerosis (ALS) and other degenerative conditions. In addition, it offers great advantages for other cfDNA-based detection applications, such as cancer and pregnancy-related conditions.