40 results about "Disease specific" patented technology

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.

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.

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

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.

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.

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

The present invention relates to a comprehensive disease risk prediction device and method capable of predicting a disease risk associated with a specific disease by comprehensively using healthcare bigdata, including information on a user's genes, information on lifestyle habits, and information on medical examination records. The comprehensive disease risk prediction device comprises: a data collection module for receiving genetic information about the user's genes, lifestyle information about lifestyle habits, and examination information about medical examination records; and a prediction module for predicting a comprehensive disease risk, which is a risk level of the user related to a predefined disease, on the basis of the genetic information, the lifestyle information, and the examination information.

The invention discloses a design method of a specific methylation marker Panel, which comprises the following steps: obtaining a first candidate interval based on a methylation related gene of a selected disease, screening a first target interval in the first candidate interval, and extracting a first CpG site set in the first target interval; performing methylation sequencing data on lesion tissues of the selected disease crowd to obtain a second candidate interval, screening a second target interval in the second candidate interval, and extracting a second CpG site set in the second target interval; and combining the first CpG site set and the second CpG site set to obtain a Panel interval covering the CpG sites therein. According to the design method, researchers or medical personnel can be helped to obtain the methylation Panel of the required disease, the methylation level resolution based on high-depth sequencing data is improved, and the efficiency of screening methylation markers related to the specific disease is improved.

The invention discloses a real-time disease deterioration prediction system based on multi-modal data, and belongs to the technical field of auxiliary medical treatment based on deep learning. Based on a bidirectional encoder characterization model, a Transform model and a text generation model, the ICU model can be accessed to a monitor, an electronic medical record system and the like to obtain multi-modal data, including real-time high-frequency physiological signals, clinical text records, image reports and the like, and the condition deterioration probability of ICU patients in multiple different time periods in the future is predicted; and an interpretable report is generated to prompt a doctor of a basis for model evaluation. According to the invention, complex multi-modal medical data can be processed, disease change of the ICU patient can be detected in real time, early discovery, early intervention and early treatment can be realized, and the method has great potential in optimizing medical resource distribution, improving working efficiency of medical staff and reducing death rate of the ICU patient. In addition, the method is high in generalization ability, and a special system can be developed for specific departments and specific diseases, so that the prediction accuracy and the result credibility are improved.

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 COL 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.

The invention relates to the technical field of medical statistics and clinical information processing, in particular to a data-driven model for determining an optimal association boundary value of a continuous medical index and a dichotomy clinical outcome and application of the data-driven model. The method is driven by data, the bias of subjective experience judgment is avoided, and the most significant statistical critical value associated with the clinical outcome is automatically and objectively found. A large number of candidate critical values are traversed, non-linear fitting is carried out in combination with a restrictive cubic spline, the lowest point of the p value on a fitting curve is accurately positioned, the accumulation histogram is assisted to visually present the result distribution difference corresponding to different threshold values, the optimal correlation boundary value is finally determined, and the precision of the optimal correlation boundary value is remarkably higher than that of a traditional grouping method. The method can be applied to correlation analysis of other continuous medical indexes (such as CD4 counting, virus load, biochemical indexes and the like) and various binary classification clinical outcomes (such as survival states, treatment reactions, complications and the like), and does not depend on specific diseases or indexes. According to the method, visual chart output can be integrated, so that the result is more visual, and clinicians can understand and apply the result conveniently.

In the various aspects and embodiments, the present disclosure provides cell populations or cell “banks” thereof (e.g., cell collections) to provide immune compatible, allogeneic cell therapies covering global, ethnic, and disease-specific populations. In the various aspects and embodiments, the cell banks and progeny thereof maintain sufficient HLA Class I and HLA Class II functionalities, while facilitating patient matching to prevent or reduce graft versus host disease (GVHD) or graft rejection. The disclosure further provides methods for creating the cell banks by gene editing, and methods for cell therapy involving cells or tissues derived from the cell banks (including but not limited to hematopoietic stem cells, or “HSCs”, progenitors, or progenies thereof).

Provided is an artificial intelligence (AI) model device of estimating the survival rates of a critically ill patient, adapted to estimate short-, medium- and long-term survival rates of an intensive care unit (ICU) patient, including a monitoring module, a data processing module, an AI evaluation module, and a display module. Therefore, AI algorithmic computation is performed on the ICU patient (but not targeted at any specific disease) with an XGBoost-based AI algorithmic computation model according to the ICU patient's daily personal features, test report data, physiology data, and evaluation data to evaluate the ICU patient's 30-day, 60-day, and 90-day survival rates. The AI algorithmic computation model is effective in estimating the longer-term prognosis of a critically ill patient precisely and enabling an ICU team to allocate medical resources appropriately and communicate with the ICU patient's family members better.

The invention discloses a specific disease intelligent follow-up visit management system based on multi-model fusion and a patient grading method, and belongs to the technical field of medical informatization. The system comprises a data integration layer, an intelligent patient classification and grading module, a model fusion layer, a personalized follow-up visit content generation module, a follow-up visit state management module, a data synchronization and integration module and a statistical analysis layer, high / medium / low risk grading is achieved, and the model fusion layer comprises recurrence, re-admission, bleeding and compliance prediction models. The intelligent patient classifying and grading module is used for classifying and judging based on rules of diagnosis sets, operation markers, bedridden duration, tumor markers and heart and cerebral vessel markers, and calculating a total score by adopting a category adaptive weight and a multi-dimensional score; patient grading accuracy is improved by more than or equal to 90%, VTE recurrence prediction accuracy is improved by more than or equal to 85%, recurrence rate is remarkably reduced by 27%, and follow-up visit efficiency is improved by more than 20%.

The invention discloses a health insurance specific disease intelligent underwriting method and system based on a general large model and cue word engineering, and the method comprises the steps: firstly obtaining a specific disease inspection material uploaded by a user, enabling the system to receive the specific disease inspection material uploaded by the user through a terminal device, and enabling the specific disease inspection material to be multi-modal data, based on multi-modal identification processing of the general large model, inputting the data into the general large model, and obtaining structured disease information data through the general large model; the method comprises the steps that an underwriting prompt word template is constructed based on a specific disease underwriting rule of a health insurance company, the underwriting prompt word template comprises a fixed prompt part and a dynamic filling part, and prompt word optimization is carried out; and inputting the complete underwriting cue word into the general large model, guiding the general large model to perform reasoning according to an underwriting rule through a cue word project to obtain a preliminary underwriting conclusion, and finally outputting according to an agreed format in the cue word in a reasoning process.