94 results about "Disease specific" patented technology

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.

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.

The present invention relates to methods for predicting whether peptides or polypeptides comprising disease specific amino acid modifications, in particular tumor-associated neo-antigens, comprise epitopes, in particular tumor-associated neo-epitopes, which are useful for immunotherapy such as for vaccination. The methods of the invention may be used, in particular, for the provision of vaccines which are specific for a patient's tumor and, thus, in the context of personalized cancer vaccines.

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 discloses a disease full-period management system based on a deep neural network, and the system comprises a collection time sequence planning module, a disease biomarker data collection module, and a compliance standard assessment module. The method comprises the following steps: acquiring previous disease diagnosis elements such as specific disease category proportion and attack frequency of each patient in each time window of a historical period from a target hospital patient platform, analyzing a disease period adaptive value according to the previous disease diagnosis elements, and further determining an acquisition time sequence plan when the patient is discharged from a hospital. And the compliance standard evaluation module is used for acquiring a compliance evaluation value of the discharge collection time sequence plan of each patient in the current period, judging whether the compliance reaches the standard or not, sending out an early warning prompt to the substandard patient, and recording the substandard patient as a to-be-analyzed patient. And the disease outcome state analysis module is used for analyzing the disease outcome states of the to-be-analyzed patients at each acquisition time point, carrying out early warning prompt according to the analysis result, and realizing accurate management and monitoring of the whole period of the diseases of the patients through multi-module cooperation.

The invention discloses a medical image anomaly detection method and system. The method comprises the following steps: acquiring a preprocessed standardized medical image; a medical image anomaly detection model is constructed and trained, and a trained medical image anomaly detection model is obtained and comprises a trained feature deentanglement encoder and a trained condition denoising diffusion probability model; using the trained feature deentanglement encoder to extract anatomical structure codes and disease specific codes which do not involve disease information in the preprocessed standardized medical image; using anatomical structure codes and disease specific codes as conditions, and using the trained condition denoising diffusion probability model to generate a health image; and comparing an abnormal image in the preprocessed standardized medical image with the correspondingly generated health image to obtain an abnormal area. The detection method is high in detection quality.

The invention discloses a whole-body wearable disease symptom simulation system for medical and nursing clinical education. The system comprises a whole-body wearable device; the control unit is used for controlling electrical stimulation parameters of the electrical stimulation unit; the control unit comprises an input module, a disease symptom model library and a stimulation control module; the stimulation control module extracts a corresponding electrical stimulation scheme from the disease symptom model library, adjusts and generates a control instruction, and controls the electrical stimulation unit to perform electrical stimulation according to a set electrical stimulation parameter sequence; the feedback module is used for collecting physiological feedback data of the subject in real time and feeding back the physiological feedback data to the control unit, monitoring the physiological state of the subject and adjusting electrical stimulation parameters; the display interface is used for displaying the simulation process, the control parameters and the physiological feedback data. According to the invention, through integrated control of the wearable device and the computer, accurate electrical stimulation is carried out on the corresponding acupuncture points for comprehensive stimulation, the pain or discomfort experience of specific diseases is completed, and the clinical education effect of medical and nursing is significantly improved.

Provided is a circular RNA encoding CARs and the use thereof to create immune cells that target specific diseases, e. g., lymphoma, multiple myeloma and leukemia and auntoimmune diseases, such as systemic lupus erythematousus, lupus nephritis and myasthenia gravis.

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 invention discloses a disease identification and prediction method based on social media knowledge graph embedding. The method is mainly used for identifying patients with specific diseases in a social media scene. In order to optimize a representation learning process of a social media user self-report knowledge graph triple, a knowledge embedding optimization algorithm based on a hyperbolic space is designed, a ComplEx model is embedded into the hyperbolic space, the representation capability of the model on complex data with a hierarchical structure is enhanced, a Riemannian gradient descent method is used for carrying out gradient updating on a Poincare sphere model, and a knowledge graph triple representation learning process is established. And the embedding point is always in an effective area. Based on the embedded optimization, on the basis of a BERT + LSTM model, a channel attention mechanism and a space attention mechanism are combined to carry out knowledge fusion on social media user self-report text information and knowledge graph information, and external information in a knowledge graph is utilized to enhance the spatial feature expression ability of the model. Therefore, the recognition precision of the specific disease patient user in the social media is improved.

Systems and methods for accurate, reliable, and comprehensive multi-disease retinal screening is disclosed. Leveraging a hierarchical deep learning architecture, a system can optimize feature extraction through a convolutional neural network (CNN) by first classifying images as normal or abnormal, and then discern the presence and severity of specific diseases using disease-specific CNN branches. The hierarchical deep learning architecture can promote feature disentanglement by associating each branch with a potentially unique set of parameters, enhancing disease-specific feature sensitivity. The system can further refine disease detection accuracy by implementing a probability classifier to exploit patterns of disease co-occurrence. A Monte Carlo Expectation-Maximization algorithm can optimize the parameters of the probability classifier, and a subsequent prediction calibration step can correct potential biases in the final predictions. An end-to-end learning approach using backpropagation to optimize all parameters concurrently can be employed to ensure robust disease prediction outcomes.

Described herein are systems for disease detection from a biological sample using a machine learning-based (ML-based) disease-detection model trained on a dataset of detection events. Also described are methods for detecting a disease category from a biological sample received from a subject, and further detecting the specific disease type within the disease category using the systems and ML-based disease detection models. Also described are methods for monitoring progression of a disease in a subject by analyzing a biological sample using the systems and ML-based disease-detection model disclosed herein.

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.

The present invention provides a process and method for the early detection and diagnosis of disease by reading and decoding volatile organic compounds (VOCs) for signatures associated with a specific disease. Urine is available as a diagnostic sample providing its off gas with VOCs for rapid screening from its outset, each disease begins producing its own unique set of volatile organic compounds. For many diseases, this early-stage detection may be many months or years before noticeable symptoms. The VOC emissions when analyzed, result in a “signature” that identifies and distinguishes the developing, or at later stages, the developed disease. The device assays non-invasively obtained biosamples in real-time to output a VOC based signature that, when correlated with data in a disease signature library, identifies one or more diseases associated with the sample.

The present invention relates to methods for predicting whether peptides or polypeptides comprising disease specific amino acid modifications, in particular tumor-associated neo-antigens, comprise epitopes, in particular tumor-associated neo-epitopes, which are useful for immunotherapy such as for vaccination. The methods of the invention may be used, in particular, for the provision of vaccines which are specific for a patient's tumor and, thus, in the context of personalized cancer vaccines.

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.

A method of operating an apparatus operated by at least one processor, the method including constructing a medical data library that generates a table for each data item by applying disease-specific fabric conditions including data items extractable from a clinical data warehouse and medical conditions including disease-specific patient groups for disease-specific research; obtaining a database generation request for research on a specific disease; determining specific data items related to research on the specific disease from the medical data library; applying the fabric conditions for the specific disease to generate a table for each of the specific data items using the limited patient group data for the specific disease; and generating a database for research on the specific disease using the tables of the specific data items.

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

The invention discloses a key acupuncture point node mining method based on an acupuncture point-disease network, and belongs to the crossing field of information systems and empirical medicine. Comprising the steps of establishing a weighted undirected acupoint-disease network based on a clinical acupuncture prescription, providing a high-specificity key node mining method based on the acupoint-disease network, and balancing contribution of a local area to the network on the basis of meridian distribution characteristics in combination with a traditional Chinese medicine acupoint selection principle. And key acupoints with high influence on global topology are selected according to the evaluation indexes of the key nodes. According to the method, key acupuncture points of meridians and collaterals in each region of a human body are positioned and mined by using a complex network theory in the field of an information system, and the key acupuncture points which have obvious influence on various common diseases of the human body are positioned from a new perspective, so that other key acupuncture points in local meridians and collaterals of the human body are determined; and an empirical medical researcher is assisted to find the optimal prescription acupoint for a specific disease under the resource constraint.

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.

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.

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.