59 results about "Disease specific" patented technology

The invention belongs to the field of bioinformatics, and relates to a protein post-translational modification prediction method based on multi-modal deep learning. The method comprises the following steps: firstly, performing multi-modal feature extraction by inputting a protein sequence and three-dimensional structure data to obtain a sequence feature vector and a structure feature vector; secondly, carrying out feature fusion by adopting a cross-modal attention mechanism and a self-adaptive gating network; then, combining the fusion features with the disease type information, and performing fine adjustment on the prediction probability through a disease specific coding network; then, using a multi-task learning framework to predict the locus probabilities of various protein post-translational modification types in parallel; finally, feature importance is calculated through a gradient back propagation technology, and a comprehensive report is output in combination with variation influence analysis. According to the method, high-precision and explainable protein post-translational modification prediction with disease perception capability is realized, and an important calculation and analysis tool is provided for revealing a disease molecular mechanism and finding accurate drug targets.

The invention provides a disease population middle-aged and elderly category structure prediction method based on gray component prediction modeling, which comprises the following steps: collecting multi-component elderly population structure data influenced by a specific disease, and analyzing component data features; building a modeling framework integrating a time elastic mechanism and component structure analysis according to the data features; establishing a novel dynamic nonlinear gray component prediction model of multi-parameter combination optimization adaptive to the irregular time sequence component data based on the framework; and gray component prediction model parameters are solved, and the structural evolution of the old-age disease crowd with each component is estimated, so that the problems of old-age category structure prediction and health management of the disease crowd based on small sample sequence modeling are solved. According to the method provided by the invention, the proportion change rule of the old people in different health states of the disease population can be accurately described, so that the health management personnel can more accurately grasp the change trend of the old people in the disease burden, and thus a more reasonable resource allocation and intervention strategy is formulated.

The present disclosure provides systems and methods that establish in real time at a point of care a patient-specific and evidence-based cost optimized path of care while maintaining expected clinical outcomes for a patient with a disease.

The invention discloses a pathological image classification method and system for assisting pathological diagnosis, and aims to solve the problems that the traditional pathological diagnosis is low in efficiency and the accuracy depends on artificial experience. According to the method, a pathological image is collected, denoising, enhancement and other preprocessing operations are executed, a pre-trained deep learning model is utilized to automatically extract image features, and classification results of benign, malignant or specific disease types are output in combination with a classification algorithm, so that efficient and accurate automatic diagnosis is realized; the system integrates image acquisition, preprocessing, feature classification and result display modules, supports multi-user concurrent access and cloud deployment, is equipped with model updating and user interaction functions, and can continuously optimize model performance based on new data. In addition, remote pathological diagnosis is supported, and balanced distribution of medical resources is promoted through a digital system; the method can significantly reduce the workload of pathologists, reduces the risk of human misdiagnosis, and is suitable for rapid classification and diagnosis of various pathological images such as tissue slices, cell smears and the like.

Described is a system for receiving a disease specific treatment algorithm corresponding to a disease of a patient from a first Electronic Health Record (EHR) system, processing data corresponding to the disease specific treatment algorithm using a Large Language Model (LLM), receiving one or more CQL models from the LLM based on the processing of the data corresponding to the disease specific treatment algorithm, receiving an update to a patient record of the patient from the first EHR system, based on the update to the patient record and the one or more CQL models, triggering a Clinical Decision Support (CDS) hook to generate an alert, and causing transmission of the alert for a medical practitioner associated with the first EHR system.

To enable a patient himself / herself to collect medical information which is related to his / her own symptoms and worries, is made strict and has high reliability without anxiety.SOLUTION: A medical information providing device 30 is mutually connected to a plurality of patient terminals 10 related to a specific disease and a manager terminal 20 operated by a manager of a patient group, and includes a reception part 311 for receiving information related to a request to provide medical information from the patient terminals, and a transmission part 120 for transmitting a request to determine whether the medical information requested to be provided is specific medical information related to the specific disease to the manager terminal. The medical information providing system includes a determination part 312 for acquiring information indicating a determination result from an administrator terminal, a request part 313 for transmitting information related to a medical information provision request to a related external organization 41 related to medical information selected from a plurality of external organizations 40 when the medical information requested to be provided is specific medical information, an acquisition part 314 for acquiring the medical information from the related external organization, and a sharing part 315 for permitting a plurality of patient terminals to access the medical information.SELECTED DRAWING: Figure 1

Disclosed is a method of providing genetic information analysis results performed by at least one hardware processor. The method may include displaying a user interface configured to provide genetic information analysis results for a specimen. The user interface includes: a list of genes associated with a specific disease in a first region within the user interface; a first browser configured to visualize and search for information regarding a variant obtained from analysis of the specimen in a second region within the user interface; and a second browser configured to search for sequence information obtained from analysis of the specimen in a third region within the user interface. The method may further include displaying, in response to user input received through the user interface, interactive response information comprising details of the genetic information analysis results to at least one region within the user interface.

Embodiments of the invention encompass identification and characterization of microbiota having potential or demonstrated therapeutic effects for certain conditions in certain non-human animals. In some embodiments, the identification and characterization of microbiota may include identifying and characterizing the microbiota of potential donors, characterizing the potential donors, and identifying potentially therapeutic microbiota based on analysis of such data from multiple individuals. In some embodiments, the identification and characterization of microbiota may include testing, that is, administering microbiota of potential donors to animals suffering from a disease or condition, documenting any therapeutic response, and optionally identifying and characterizing the microbiota of recipients before and after treatment, and optionally identifying demonstrated therapeutic microbiota. Embodiments of the invention encompass compositions comprising the microbiotia, including solid oral compositions, methods of making these compositions, and methods of treatment of non-human animals suffering from various diseases and conditions with these compositions.

The present disclosure relates to a method of isolating extracellular vesicles directly from human tissues. The invention further relates to a method of identifying disease and tissue specific membrane proteins on extracellular vesicles by membrane isolation and proteomic analysis. The invention further relates to methods of diagnosing diseases by capturing extracellular vesicles by the use of disease specific membrane proteins from body fluids, and detecting or analyzing molecular signatures (proteome, DNA, and RNA) on captured extracellular vesicles. Moreover, the present invention relates to kits, apparatus and software required for implementing aforementioned methods.

This method for predicting the presence or absence of a specific disease by using microbiome data, which is performed by a disease presence / absence prediction device, may comprise: a data acquisition step of acquiring multiple pieces of human microbiome data; a preprocessing step of converting the multiple pieces of human microbiome data into language-format data; a pre-training step of training a language model including at least one transformer encoder block by using the language-format data; a fine-tuning step of configuring a disease prediction model in which a prediction layer for predicting the presence or absence of a specific disease is combined with the language model, and fine-tuning the disease prediction model by using microbiome data related to the specific disease; and a prediction step of predicting the presence or absence of the specific disease by using the disease prediction model for which the fine-tuning has been completed.

The invention discloses an electronic medical record generation system and method based on a preset rule and a modular robot, and belongs to the technical field of computer-aided medical treatment. The invention aims to solve the technical problems of high data redundancy, difficulty in maintenance and difficulty in automatic processing of data logic conflicts of a computer system caused by exponential growth of an electronic medical record template due to disease combination in the prior art. The technical scheme of the invention is to provide a computer implementation system and method. The system includes a memory and a processor. The memory stores: 1, a normal person basic template library as a calculation reference; 2, a plurality of disease robot knowledge bases which respectively correspond to specific diseases and can be independently updated are included, and an instruction set for modifying a basic template is defined in each robot; and 3, a conflict processing rule base used for solving instruction conflicts. The processor is configured to activate one or more corresponding disease robots according to input patient information, execute instruction sets of the disease robots, automatically solve conflicts according to preset rules in a conflict processing rule base if conflicts are detected in the instruction application process, and finally generate a personalized electronic medical record with consistent data.

Provided herein are methods and systems of molecular profiling of diseases, such as cancer. In some embodiments, the molecular profiling can be used to identify treatments for a disease, such as treatments that were not initially identified as a treatment for the disease or not expected to be a treatment for a particular disease.

The present invention is directed to olfactory diagnostic testing compositions and kits for detecting, screening and / or monitoring the progression of specific disease states or other conditions in a subject. In one aspect, the invention is directed to a method and testing kit using a pathology-specific panel of fragrances to detect, screen and / or monitor a specific disease state or condition. In another aspect, the invention is directed to a method of making diagnostic compositions having a specific complex fragrance such as an essential oil fragrance and diagnostic testing kits including such diagnostic compositions.

A method is provided for predicting a disease outcome using a machine learning model that generates training data for training the machine learning model useful for implementing a cellular disease model.SOLUTION: A method for predicting a disease outcome using a machine learning model includes implementing an ML-enabled cellular disease model to validate an intervention, identifying a patient population likely to be a responder to the intervention, and developing a therapeutic structure activity relationship screen. To generate a cellular disease model, data from human genetic cohorts, the literature, and generic cellular or tissue-level genomic data are combined to elucidate a set of factors (e.g., genetic, environmental, cellular factors) that cause a particular disease. A series of factors are used to manipulate invitro cells to generate training data for training a machine learning model useful for implementing a cellular disease model.SELECTED DRAWING: FIG. 1B

The invention discloses a Wilson's disease low-abundance urine protein biomarker detection method, and belongs to the technical field of Wilson's disease, and the method comprises the following steps: S1, collecting a urine sample of a Wilson's disease to-be-detected person, and pre-treating the urine sample; s2, analyzing the pretreated urine sample by adopting a DIA mass spectrometry method, and identifying and quantifying global protein components in the urine sample in an unbiased manner; s3, screening candidate marker proteins significantly related to the Wilson's disease in the global protein components by means of statistics and bioinformatics; and S4, verifying the candidate marker protein obtained by screening through targeted mass spectrometry to obtain biomarker protein data. A DIA mass spectrometry technology is combined with liquid chromatography-ion mobility separation, unbiased deep analysis is carried out on urine proteomes of Wilson disease patients and healthy controls, detection parameters are optimized to improve the number and reliability of protein identification, and potential Wilson disease specific low-abundance protein markers are screened out.

A computer-implemented method for calculating an individual's tailored Polygenic Risk Score is based on known genetic information. A dataset is provided related to a reference panel including genetically characterized individuals with known disease status and diversified global ancestry. An individual-specific genetic reference group of individuals is selected as a subset from the reference panel. Genetic distances of the individual from each of the reference panel individuals are computed; each being the individual's genetic distance from a respective reference panel individual. Individuals of the individual-specific genetic reference group based on the individual's computed genetic distances are selected. The individual's basic Polygenic Risk Score for a disease is calculated to provide the individual's disease risk prediction. An ancestry-based background PRS contribution is determined. The ancestry contribution is removed from the individual's calculated basic Polygenic Risk Score to obtain the individual's tailored Polygenic Risk Score and provide a disease risk prediction.

The invention belongs to the technical field of chronic disease management, and discloses an intelligent chronic disease management method, system and device based on pre-analysis and map retrieval enhancement and a medium, and the method comprises the steps: firstly, achieving the dynamic evaluation and early warning of the health state of a user in combination with the physiological data of the user; secondly, designing a lightweight user demand pre-analysis mechanism, performing semantic analysis and structured reconstruction on user input, and combining health tags and physiological data to improve the understanding and adaptation ability of a large language model to individualized demands; and finally, by means of a chronic disease knowledge base and a map retrieval enhancement technology, deploying a domain model oriented to a specific disease scene so as to improve the vertical adaptation capability of the model. According to the technical scheme, a chronic disease knowledge service framework enhanced through pre-analysis and map retrieval is fused, the accuracy and normalization of knowledge services can be effectively improved, and an extensible technical path is provided for intelligent chronic disease management.

The invention belongs to the technical field of artificial intelligence, and discloses a chronic disease co-disease new risk assessment method, system, device and medium, and the method comprises the steps: employing a time sequence ternary group to model a disease new path, introducing a multi-disease co-existence coefficient to quantify the new risk, and carrying out the grading, learning disease network representation by adopting a relational graph convolutional network (R-GCN), and predicting a new risk; secondly, designing a cross-time window joint training strategy, performing hot start training on the model by using embedded representation of historical data, expanding representation information of an encoder for incremental data, fusing the representation information into a disease network, and integrating data to iteratively optimize the model; according to the method, through systematic modeling of the patient medical history data and the disease association network, the potential law of the new occurrence of the common disease is disclosed, and an analysis tool and direction guidance are provided for subsequently exploring the common disease mechanism of a specific disease and identifying a high-risk evolution mode.

Novel compositions and methods are provided that are useful for treating, preventing and potentially curing genetic diseases, such as familial Alzheimer's disease, by disrupting a genomic sequence comprising one or more SNPs that are highly epidemic in a population but independent of a particular disease, in some embodiments, their genomic positions are within the same gene exon as the disease-related alleles, and upstream of such disease-related alleles present in the genome of a treatment recipient.

The invention discloses an intelligent health early warning system based on cloud processing, and relates to the field of intelligent early warning, and the system comprises the steps: comprehensively collecting the physiological, environment and behavior data of a user through a multi-mode collection module, and dynamically adjusting the collection frequency; after preprocessing and end-to-end encryption are completed on the user side edge equipment, the data are uploaded to the cloud for mixed storage and life cycle management; performing fusion analysis on the data by the system, mining internal association and extracting key feature vectors, and constructing a personalized dynamic health assessment model according to the internal association and the key feature vectors; the system calculates a comprehensive health risk score and a specific disease incidence probability based on a model, realizes graded early warning, and bidirectionally interacts and feeds back to a user and medical staff through an intelligent terminal. The method has the advantages that safety and high efficiency of data are guaranteed through edge-cloud cooperative processing and end-to-end encryption, accurate risk assessment and graded early warning are realized based on a personalized health model, and the method has early warning accuracy, use convenience and medical specialty.

A personalized multi-factor genetic risk system for disease prediction and method of use is described herein. The personalized multi-factor genetic risk system is generated by generating a disease specific polygenic score model. Responsive to receiving one or more inputs, wherein the one or more inputs include genomes or partial genomes of an individual, the disease specific polygenic score model generates a lifetime disease likelihood for the individual.

Provided herein are methods and systems of molecular profiling of diseases, such as cancer. In some embodiments, the molecular profiling can be used to identify treatments for a disease, such as treatments that were not initially identified as a treatment for the disease or not expected to be a treatment for a particular disease.

The present invention relates to a method for identifying a nucleic acid segment that interacts with a target nucleic acid segment or a plurality of target nucleic acid segments and a kit for performing the method. The present invention also relates to a method for identifying one or more interacting nucleic acid segments indicative of a particular disease.

A system processes clinical guidance to enhance patient care management. The system receives clinical guidance in digital text form from authoritative medical sources. A large language model analyzes the received clinical guidance to extract key information and relationships. The system generates a structured pathway based on the analyzed clinical guidance. The generated pathway represents a comprehensive summary for a specific disease state, organized into logical steps. These steps encompass treatment goals, management strategies, and measures for preventing complications. The system integrates the generated pathway information into an electronic health record (EHR) system. Within the EHR, the system produces a patient chart incorporating the derived pathway information.

The application discloses a disease recognition prediction method based on social media knowledge graph embedding, mainly aiming at specific disease patient recognition in a social media scene. In order to optimize the representation learning process of the social media user self-report knowledge graph triple, a knowledge embedding optimization algorithm based on hyperbolic space is designed, the ComplEx model is embedded into the hyperbolic space, the representation ability of the model for complex data with hierarchical structure is enhanced, and the Riemannian gradient descent method is used for gradient updating of the Poincaré ball model, so that the embedded points are always in the effective area. Based on the embedding optimization, the social media user self-report text information and the knowledge graph information are knowledge fused by combining the channel attention mechanism and the spatial attention mechanism on the basis of the BERT+LSTM model, the spatial feature expression ability of the model is enhanced by using the external information in the knowledge graph, so that the recognition accuracy of the specific disease patient user in the social media is improved.

This information processing device comprises a model acquisition unit, a data of interest acquisition unit, and a determination execution unit. The model acquisition unit acquires a trained model in which medical record data before or during intervention of a predetermined treatment for patients with a specific disease is set as an explanatory variable and the necessity of palliative care for the patients is set as an objective variable. The data of interest acquisition unit acquires medical record data for a patient of interest. The determination execution unit: determines the necessity of palliative care for the patient of interest by inputting the medical record data for the patient of interest to the trained model; and causes an output device to output the result of the determination.

Disclosed is a method of providing genetic information analysis results performed by at least one hardware processor. The method may include displaying a user interface configured to provide genetic information analysis results for a specimen. The user interface includes: a list of genes associated with a specific disease in a first region within the user interface; a first browser configured to visualize and search for information regarding a variant obtained from analysis of the specimen in a second region within the user interface; and a second browser configured to search for sequence information obtained from analysis of the specimen in a third region within the user interface. The method may further include displaying, in response to user input received through the user interface, interactive response information comprising details of the genetic information analysis results to at least one region within the user interface.

The present invention discloses a method for predicting the recurrence probability of a specific disease based on a clustering method. The method comprises collecting patient disease data from different time periods, preprocessing the patient disease data, dividing the patient disease data according to EBV DNA levels and tumor response to obtain subgroup data, characterizing the subgroup data according to relative risk to obtain time-series disease features, permuting and combining the time-series disease features to obtain disease manifestation data, risk stratifying the disease manifestation data using supervised clustering to obtain risk groups, assessing the prognostic performance of the risk groups using the Concordance Index, comparing the prognostic performance with the TNM staging to obtain recurrence data, constructing a recurrence probability prediction model based on the recurrence data, inputting the data to be predicted into the recurrence probability prediction model, and outputting a prediction result. This method can not only improve the accuracy of predicting the recurrence probability of a specific disease, but also has good interpretability.

The present invention may be an invention that can search for / analyze a disease of interest and / or disease history of a user on the basis of questionnaire information and health records, and recommends customized health functional foods according to the search / analysis results. In addition, the present invention may be an invention that provides support to make it easy to receive posts, SNS posts, etc. registered by other members and share the received posts, SNS posts, etc. with acquaintances. In addition, the present invention may be an invention that can search for various information such as definitions / causes / related functionality / related foods / TIP / TALK, etc. about the disease of interest and supports a service in which location information about searched medical departments is displayed on an electronic map in order of proximity to a user through a location-based service and a search of related medical departments using keywords about age, cardinal symptoms, and accessory symptoms. In addition, the present invention may be an invention that searches and receives recommendations for disease names related to disease factors (nervous system, digestive / metabolic system, reproductive system, muscular system, sensory system, cardiovascular system, endocrine system, immune system, etc.), medicinal ingredients, medicines, types of health functional foods, sales information, etc., and supports purchase information (image, product name, consumer price, discount rate, selling price, quantity) about the searched / recommended health functional foods, and functions related to writing product reviews.

To provide techniques for reducing malassezia counts.SOLUTION: An agent for reducing malassezia counts contains at least one selected from the following components (a)-(d) as an active ingredient: (a) erythritol, (b) xylitol, (c) reduced syrup with a sugar composition where monosaccharides account for less than 30 mass% and pentoses or greater make up less than 50 mass%, and (d) reduced syrup derived from the reduction of syrup with a dextrose equivalent of 30 or more and 50 or less. The present invention can Inhibit the proliferation of Malassezia fungi with no concerns about skin irritation or safety.SELECTED DRAWING: Figure 1