47 results about "Patient stratification" patented technology

The invention discloses a multi-omics cancer subtype recognition method, system and device based on density sensing cluster structure guide contrast learning and a medium, and belongs to the technical field of bioinformatics and artificial intelligence crossing. The method comprises the following steps: acquiring and preprocessing multi-omics data; constructing an omics specific auto-encoder and learning potential representation; constructing a density sensing cluster block in the potential space; constructing a cross-omics positive and negative sample pair based on cluster block sample overlapping; difficult negative sample mining; constructing a cluster block level cross-omics contrast learning target, and training and updating; a self-supervised soft refinement mechanism is introduced to dynamically enhance a cluster structure; and carrying out multi-loss joint optimization and model iteration training. According to the method, the robustness and the stability of a cancer subtype recognition result can be improved, high-dimensional, multi-source and multi-noise multi-omics data can be efficiently modeled and analyzed, good generalization ability and application potential are achieved, and reliable technical support can be provided for cancer subtype research, patient stratified analysis and precise medical aid decision making.

The invention provides a big data resource service-based patient hierarchical intervention method and system, which are applied to the technical field of medical information, and are used for acquiring full-cycle health data of a patient, generating and updating a patient disease course trajectory sequence, analyzing health index change and trend under short, medium and long time scales, and calculating health scores and steady-state coefficients, so as to realize hierarchical intervention of the patient. A layered intervention scheme is generated, detection and personalized intervention of the health state of the patient are achieved, the timeliness and pertinence of medical intervention can be improved, and the disease management effect is optimized.

Methods and systems for patient stratification include learning (404) interdependent biomarkers as integrated time-series machine learning models. A disease stage is identified (424) for a patient based on collected biomarker data. A treatment for the patient is performed (430) based on the identified disease stage and a predicted future response of the patient.

Disclosed are methods and agents for predicting response to therapy, immune status and / or disease progression. More particularly, disclosed are methods, agents and kits for analyzing cellular distribution of PD-L2, including its nuclear localization, for stratifying a patient as a likely responder or non-responder to a therapy, for managing treatment of a patient with a therapy, for monitoring a disease in a patient following treatment with a therapy, for determining the status of a disease and / or for determining the immune status of a patient.

The invention discloses a disease community discovery and patient layering method based on gene information guidance, and belongs to the technical field of biological information.According to the method, an initial disease set is created through full phenotype correlation research, and the diseases are expressed in a high-dimensional embedding space of electronic health record data; vector representation of diseases is optimized and extracted by learning individual features of the diseases and a topological structure of a co-disease network of the diseases through a graph auto-encoder, the diseases are divided into different communities with consistent interiors by embedding vectors into the optimized diseases and applying a clustering algorithm, and the method is provided based on the individual health history, and is suitable for the individual health history and the co-disease network. According to the method for classifying new patients into a certain community, accurate patient layering is achieved, a complex multi-disease network is disclosed, a brand-new systematic perspective is provided for disease understanding, a traceable and data-driven bridge from genes to patient subgroups is established, and a novel framework is provided for medical conversion.

This invention discloses a multi-terminal interconnected nursing system and method based on the Internet of Things (IoT). The multi-terminal interconnected nursing method includes: collecting patients' physiological and behavioral data through IoT devices deployed at home; generating exclusive early warning thresholds and analysis models by a personalized data module, with rules pushed to the hospital, community nursing, home monitoring, and family members via a multi-terminal synchronization protocol; automatically generating nursing tasks based on the patient's data anomaly level and the urgency of nursing needs; allowing nursing staff to access complete patient information integrated across multiple terminals with a single click through an integrated operation portal; periodically iterating and optimizing the personalized model library based on multi-terminal feedback data; and achieving dynamic synchronization of multi-terminal thresholds and analysis models by constructing a patient-stratified personalized data model library, creating a multi-terminal integrated collaborative platform, integrating multi-source data, and reconstructing the information display and operation logic at the nursing staff's end, thereby improving the accuracy of nursing decisions and the efficiency of nursing service execution.

A respiration monitoring device comprises an electronic controller configured to: receive an audio signal that is acoustically coupled with an airway of a patient receiving mechanical ventilation therapy from a mechanical ventilator; map the audio signal to one or more lung disease or injury condition categories; and at least one of: display the mapped one or more lung disease or injury condition categories on a display device; and determine a recommended adjustment to one or more parameters of the mechanical ventilation therapy delivered to the patient based at least on the mapped lung disease or injury condition categories and displaying the recommended adjustment on the display device.

The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

The invention belongs to the technical field of medical informatization and artificial intelligence, and particularly relates to a lupus erythematosus patient layering and personalized treatment scheme recommendation system which comprises a data access module, a data preprocessing module, a graph neural network modeling module, a layering label updating module, a subtype division module and a treatment scheme recommendation module. Accessing multi-source data through a data interface, constructing a data association graph by adopting a graph neural network, and analyzing a potential association relationship among data of different dimensions; a deep learning algorithm is combined to dynamically update layering labels of patients, the patients are subdivided into hormone sensitive subtypes, immunosuppressor dependent subtypes, refractory subtypes and other subtypes, and personalized treatment schemes are matched. According to the method, the layering accuracy of the patient and the scientificity of a treatment scheme can be improved, the dependence on artificial experience is reduced, and technical support is provided for precise medical treatment of the lupus erythematosus patient.

Objective discrimination of a disease label of a depressive symptom with respect to an active state of a brain is achieved. One means for solving the problems of the present invention is to provide a discriminating device for assisting in determination of whether a subject has a depressive symptom. The discriminating device includes a storage device for storing information for identifying a classifier generated by classifier generation processing based on a signal obtained by using a brain activity detecting apparatus to measure, in advance and time-sequentially, a signal indicating a brain activity of a plurality of predetermined regions of each brain of a plurality of participants in a resting state, the plurality of participants including healthy individuals and patients with depression. The classifier is generated so as to discriminate a disease label of a depressive symptom based on a weighted sum of a plurality of functional connectivities selected by feature selection as being relevant to the disease label of the depressive symptom through machine learning from among functional connectivities of the plurality of predetermined regions. The discriminating device further includes a processor configured to execute discriminating processing of generating a classification result for the depressive symptom of the subject by using the classifier.

The present application relates to the technical field of protein genomics analysis, and particularly relates to a protein gene multiomics analysis method, which comprises: S1. sample collection and pretreatment: tumor tissue and paired normal adjacent tissue are selected as samples, and the samples are frozen and crushed; S2. sample multiomics detection: the frozen and crushed samples are subjected to genomics detection and proteomics detection; S3. sample multiomics data processing and analysis: the genomics detection result and the proteomics detection result of the samples are obtained, and tumor molecular characteristics are identified through multiomics data integration analysis.The present application carries out systematic protein genomics analysis on LCNEC tumor tissue and NATs. The research not only reveals the genomic abnormality characteristics of LCNE and disease-related molecular phenotypes, but also deepens the scientific cognition of patient stratification logic in targeted therapy strategies. Further research finds that IL33, as a new key therapeutic biomarker, is related to T cell infiltration and has anti-tumor activity.

The invention discloses application of TGM2 to regulation and control of ubiquitination degradation of KRAS through a non-classical enzyme activity pathway and prevention and treatment of related cancers, and belongs to the technical field of biological medicines. It is verified that TGM2 inhibits KRAS ubiquitination through a non-classical enzyme activity pathway, key ubiquitination sites on KRAS are determined, and the structural basis of interaction of TGM2 and KRAS is clarified. The inhibiting effect of the TGM2 inhibitor on proliferation and metastasis of KRAS related cancer cells is screened and verified, the synergistic effect of the TGM2 inhibitor and chemotherapeutic drugs is evaluated, and a new strategy is provided for clinical treatment. The correlation between the TGM2 expression level and the prognosis of the KRAS-related cancer patient is confirmed through related experiments, and a convenient and efficient TGM2 detection method is developed and used for patient stratification and treatment scheme optimization. The technical problems that existing KRAS targeted therapy is narrow in application range and inaccurate in prognosis evaluation are solved, and a brand new scheme is provided for accurate diagnosis and treatment of KRAS related cancers.

The present invention relates to methods for identifying cancer, responders, predicting response and stratifying patients with respect to a combination therapy comprising the administration of a P2X4 receptor inhibitor and a cell death inducing chemotherapy. The methods according to the present invention are based on the detection of P2X4 receptor protein expression in cells of a patient-derived cancer sample, e.g. cells or organoids, to identify cancer, responders, predict responders and stratify patients, wherein an increase in protein expression identifies cancer, responders and predicts responders when applying the combination therapy and also allows to stratify the patients accordingly.

This invention belongs to the field of biomedical technology, specifically relating to the application of a pharmaceutical composition consisting of denosumab combined with an immune checkpoint inhibitor in the preparation of a drug for treating KRAS-mutant advanced non-small cell lung cancer (NSCLC). Addressing the problems of high toxicity, limited efficacy, and lack of effective prognostic biomarkers in existing maintenance therapy for KRAS-mutant advanced NSCLC, this invention experimentally demonstrates that maintenance therapy with denosumab combined with a PD-1 / PD-L1 inhibitor can significantly prolong overall survival in patients with KRAS-mutant advanced NSCLC who have not progressed after 4-6 cycles of first-line immunochemotherapy, with a lower incidence of treatment-related adverse events and better safety, especially in patients with KRAS-G12C mutations. Furthermore, this invention discovers that plasma CCER2 can serve as a prognostic biomarker. This invention provides a new, low-toxicity, high-benefit strategy for KRAS-mutant advanced NSCLC and also provides a liquid biopsy biomarker that can be used for patient stratification, possessing significant clinical application value.

Methods for target, biomarker, and patient selection discovery in central nervous system disorders utilizing patient-derived cellular models, spatial proteomics, and machine learning. The method generates neural cells from forebrain regions from induced pluripotent stem cells, performs cell-type specific proteome profiling using antibody-enzyme conjugates and spatial proteome profiling, and applies statistical data augmentation to sparse biological datasets. Machine learning classifiers with SHAP-based feature importance identify ranked biomarkers from mass spectrometry data. The platform enables patient stratification by linking molecular signatures to symptom severity, drug screening through biomarker modulation, and diagnostic applications. Kits comprising antibodies for biomarkers including antibodies for biomarkers identified by the method facilitate implementation. Applications include autism spectrum disorder, rare neurodevelopmental disorders, schizophrenia, epilepsy, Alzheimer's disease, and Parkinson's disease.

The invention relates to the field of cerebral apoplexy rehabilitation assistance and auxiliary tool precise adaptation, and provides a patient layering and auxiliary tool adaptation method and system. The method comprises the steps that a patient initiates a conversation, an intention chain is generated, data is collected, layered labels are classified and mapped, and a personalized auxiliary tool list is generated. The system comprises three modules, and can improve the adaptation efficiency and precision, optimize interaction, enhance the marketing effect and save computing resources. The invention relates to the field of cerebral apoplexy rehabilitation assistance and auxiliary tool precise adaptation, and provides a patient layering and auxiliary tool adaptation method and system. The method comprises the steps that a patient initiates a conversation, an intention chain is generated, data is collected, layered labels are classified and mapped, and a personalized auxiliary tool list is generated. The system comprises three modules, and can improve the adaptation efficiency and precision, optimize interaction, enhance the marketing effect and save computing resources.

The application discloses an incomplete multi-omics cancer subtype identification method, system, device and medium, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: incomplete multi-omics data acquisition and preprocessing; constructing an entry-level observation mask matrix and a view availability mask matrix; constructing a feature module in the omics based on a granule division; extracting a module-level skeleton representation; recovering an entry-level missing value based on the skeleton structure; constructing a multi-expert skeleton recovery integrated result; constructing a central feature matrix of the feature module; constructing a cross-omics consensus structure space; performing a mask-aware skeleton consensus alignment; performing adaptive view weighting based on structure reliability; performing sample-level mask-aware fusion; and outputting a cancer subtype clustering result. The application provides reliable technical support for cancer typing research, patient stratification analysis, prognosis evaluation and precision medicine auxiliary decision-making.

Disclosed herein is a method for treating selected patients with **Heart Failure with Preserved Ejection Fraction (HFpEF)** using computer-assisted patient stratification. Also disclosed is a computer-implemented method for classifying patients who are likely to respond to and demonstrate clinical benefit from a Rho kinase inhibitor.

Described herein are methods of calculating a pancreatic cancer risk score and methods of stratifying patients; for example, into two groups - patients who can be further monitored by also repeating the test in intervals of several months, and patients who need further testing by confirmatory methods. Also described are methods of further confirming pancreatic cancer, and methods of treating pancreatic cancer.

The present disclosure relates to [18F]-labeled imidazopyridine derivatives or salts thereof as positron emission tomography (PET) radiotracers suitable for imaging the stress-signaling non-receptor tyrosine kinase c-abl, and their use in in vivo diagnosis, preclinical and clinical imaging, patient stratification on the basis of mutational status of c-abl and assessing response to therapeutic treatments. The present disclosure further relates to the use of [18F]-labeled imidazopyridine derivatives as PET radiotracers. The disclosure also provides a process for the radiosynthesis of [18F]-labeled imidazopyridinederivatives.

This invention provides methods, computer programs, computer-readable storage media, and systems that offer improved solutions for processing genetic data. [Solution] The processing method comprises the steps of: S10 obtaining a dataset containing microbial gene expression data of multiple patients; S20 clustering multiple patients into a set of clusters based on the microbial gene expression data; and S50 determining genes exhibiting expression that explains the clustering by selecting genes included in a set of metabolic pathways and / or identifying genes that show significantly different expression between clusters, thereby forming an improved solution for patient stratification based on metagenomic data.

The present invention relates to antibodies that bind to human and monkey mucin 17 (MUC17). Moreover, the invention relates to a detection system comprising such antibodies. The antibodies or the detection system may be used for detecting or quantifying MUC17, for diagnosing a disease associated with MUC17, for patient stratification, monitoring disease progression, and evaluating the therapeutic response.

The invention discloses a lung adenocarcinoma prognosis prediction method based on machine learning. RNA transcription data and clinical information of a lung adenocarcinoma patient are collected from a database, and a data set is constructed; performing gene expression data preprocessing and difference analysis to obtain differential expression genes; sequencing the risk coefficients of the differential genes by using single-factor COX analysis; carrying out survival related feature recognition on the screened genes by virtue of eight machine learning algorithms related to survival analysis to obtain related genes with prognosis values; a machine learning combinatorial algorithm is utilized to train multiple prognosis models, C-index average values of the prognosis models are calculated and compared, and an optimal StepCox-RSF algorithm is selected to carry out patient layering and prognosis prediction model construction according to feature genes; according to the risk score of the patient, predicting the total survival rate and the risk grouping condition of the patient; according to the method, the reflecting capacity of the model to the immune microenvironment is remarkably improved, the prediction precision is optimized, and a scientific basis is provided for personalized treatment of lung adenocarcinoma patients.

The application belongs to the field of proteomics research, and discloses application of a reagent in preparation of a product for detecting or treating IgG4-RD. Six PD-L1 related DEPs (RPS6KB1, NFATC1, CD3E, PTEN, PTPN6 and JAK2 proteins) can be used as biomarkers for patient stratification and identification of individuals with high risk of severe disease progression or fibrosis sequelae, secondly, activation of the PD-1 / PD-L1 pathway suggests that immune checkpoint regulation may be a new treatment method for IgG4-RD, thirdly, significant enrichment of metabolic pathways indicates that IgG4-RD is a metabolic disorder disease, which provides a way for targeted immunometabolism. Finally, drug repositioning research highlights resveratrol and sorafenib as candidate therapeutic drugs, and the potential combination of these drugs provides a new method for rebalancing immune activation and inhibition in IgG4-RD.

Methods for target, biomarker, and patient selection discovery in central nervous system disorders utilizing patient-derived cellular models, spatial proteomics, and machine learning. The method generates neural cells from forebrain regions from induced pluripotent stem cells, performs cell-type specific proteome profiling using antibody-enzyme conjugates and spatial proteome profiling, and applies statistical data augmentation to sparse biological datasets. Machine learning classifiers with SHAP-based feature importance identify ranked biomarkers from mass spectrometry data. The platform enables patient stratification by linking molecular signatures to symptom severity, drug screening through biomarker modulation, and diagnostic applications. Kits comprising antibodies for biomarkers including antibodies for biomarkers identified by the method facilitate implementation. Applications include autism spectrum disorder, rare neurodevelopmental disorders, schizophrenia, epilepsy, Alzheimer's disease, and Parkinson's disease.

The invention provides Salivary Biomarker Panel for Therapeutic Monitoring and Relapse Prediction in Head and Neck Cancers, comprising synergistic combination of miR-1307-5p, CD44v6, KRT4, and PD-L1 quantified from salivary extracellular vesicles. Biomarker expression values are processed in which fold-change thresholds are computed, individual ChemoScore, RelapseScore, and ImmunoScore are derived, and patients are stratified into therapeutic response, relapse risk, immunotherapy suitability, and prognostic categories. Individual scores are further integrated into Overall Risk Score to classify patients into low, moderate, high, or very high risk. Limitations of tissue biopsies and delayed imaging-based assessments are overcome, enabling repeatable, real-time molecular monitoring to support personalized treatment strategies. The panel embodies into diagnostic kit comprising reagents, primers, reference controls, integrated scoring system, facilitating standardized detection and interpretation for dynamic clinical decision-making in head and neck oncology.

The present disclosure particularly relates to a computer-implemented method for genetic data processing. The processing method comprises obtaining (S10) a dataset comprising microbial gene expression data of a plurality of patients. The method comprises clustering (S20) a plurality of patients into a set of clusters based on microbial gene expression data. The method comprises determining (S50) genes whose expression interprets a cluster by selecting genes included in a group of metabolic pathways and / or by identifying genes in the cluster that exhibit significantly different expressions. The method forms an improved solution for patient stratification based on metagenomic data.

This invention provides a system and method for discovering relevant biomarkers using data collected as part of standard of care (SoC). [Solution] A method for predicting the activity of a patient's molecular analytes includes training a first module of a machine learning model based on multiple medical images from a first cohort, training a second module of the machine learning model based on one or more molecular analyte datasets obtained from a second cohort, receiving medical images from the patient, and using the trained first and second modules of the machine learning model to predict the activity of a molecular analyte from the patient's medical images.