163 results about "Personalized medicine" patented technology

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

The invention provides a multi-source medical outpatient service data comprehensive management method and platform based on RPA, and the method comprises the steps: S1, designing a script based on robot process automation, and carrying out the data collection and storage of multiple systems; s2, constructing a knowledge graph # imgabs0 # according to the structured data set; s3, introducing a causal inference technology into the knowledge graph # imgabs1 #; s4, according to the abnormal mark knowledge graph # imgabs2, generating personalized medicine use optimization suggestions through joint learning of a graph structure and node attributes; and S5, implementing closed-loop management by executing optimization suggestions and dynamically updating system rules. According to the method, the defects of a traditional method in the aspects of data integration, drug use anomaly detection and optimization suggestion generation can be overcome.

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention discloses a myocardial infarction patient risk assessment model construction method and system, and particularly relates to the field of cardiovascular disease prediction, and the method comprises the following steps: I, collecting modal data of a hospital information system, an image filing system and wearable equipment, and integrating the modal data; according to the method, the accuracy of myocardial infarction risk prediction can be optimized, risk assessment not only depends on statistical characteristics, but also fuses pathophysiological mechanisms, the clinical interpretability of prediction is improved, the limitation that a traditional data-driven method is only based on correlation modeling is avoided, the optimal treatment scheme can be dynamically adjusted, data-driven personalized medical treatment is achieved, and the risk assessment efficiency is improved. The scientificity of intervention decision making is improved; the method can quickly adapt to new patient data, improve the individualized prediction ability, improve the data utilization rate, enhance the generalization ability of the model for different patient groups, avoid training the whole model from zero, greatly reduce the consumption of computing resources, make up for the problem of insufficient data of a single patient, and improve the prediction ability of rare cases.

Systems and methods for providing recommended doses and instructions for pharmaceutical treatments, and more particularly systems and methods for measuring physiological and / or electrophysiological signals from a subject and providing recommended doses and instructions of pharmaceuticals based at least in part on the measured signals are described. Further, the recommended doses and instructions are based at least in part on a subject's disease(s), disorder(s), or injuries and / or symptom(s) of such, and / or side effects of medications or treatments the subject is receiving. The recommended doses and instructions are personally tailored to the subject to precisely and personally address the subject's needs, and may be based in part on correlation with database(s) comprising historical data of the subject and / or other subjects to treat the subject's movement disorders, injuries to the body or brain, cognitive diseases and disorders, sleep diseases and disorders, chronic pain, or other conditions.

The invention provides an individualized management system and method for colorectal cancer risk prediction, and relates to the technical field of medical diagnosis and individualized medical treatment. The system comprises a multi-omics data processing module used for collecting a blood sample of a subject, extracting circulating free DNA, and sequencing cfDNA by adopting a simplified methylation sequencing technology to obtain final original data of DNA methylation characteristics; the risk prediction module is used for carrying out bioinformatics analysis on the original data of the final DNA methylation features by adopting a machine learning algorithm, extracting a methylation feature set, a tail end motif feature set and a breakpoint motif feature set, and carrying out risk prediction on the basis of the extracted feature sets; and predicting the colorectal cancer disease risk of the subject by using a pre-trained prediction model to obtain a prediction result. The problem that the risk prediction accuracy of the colorectal cancer is low is solved.

Systems and methods for computer modeling in medicine. A sort of period table of medical models is described for personalized diagnostics, prognostics and therapeutics, including at least 80 major categories of medical models. Generative artificial intelligence and geometric deep learning techniques, and algorithms including 2D and 3D graph machine learning and GenAI algorithms, are described, tailored and applied to diagnostic disease description, prognostic prediction and therapeutic development and management, including generation of novel synthetic drugs. The AI and machine learning techniques and algorithms are applied to understand each individual's genetic, RNA and protein anomalies that represent the source of many unique patient diseases. AI-enabled software agents assist physicians and researchers in building patient medical models. Several personalized medicine applications of individualized medical modeling include cardiovascular disease, cancer, neurological disorders, immune system disorders and genetic diseases.

The invention discloses a tumor personalized drug recommendation method and system based on fusion of multiple clinical guidelines, and belongs to the technical field of bioinformatics and precision medicine. The method comprises the following steps: acquiring gene variation data and clinical feature information of a patient; generating a preliminary drug candidate list based on drug recommendation rules of a plurality of clinical guidelines; calculating the weight of each guide by adopting a dynamic weight distribution algorithm; obtaining drug-gene-disease associated information through multi-hop reasoning of the knowledge graph; calculating a drug evidence score by adopting a multi-dimensional scoring algorithm; carrying out personalized score adjustment in combination with individual features of the patient; and outputting a personalized drug recommendation result. According to the method, through multi-guide dynamic fusion, multi-dimensional evidence scoring, knowledge graph reasoning and personalized adjustment, the problems of incomplete guide coverage, lack of personalization, low response speed and the like in the prior art are solved, the method has the advantages of high accuracy, high clinical applicability, quick response and the like, and the clinical real-time decision-making requirement can be met.

The invention provides a single-cell drug sensitivity prediction algorithm based on multi-modal reinforcement learning, and aims to solve the problem that the accuracy of a traditional drug sensitivity prediction method is limited. According to the system, multi-modal bioinformatics data including cell line gene expression data, proteome data, metabolome data, drug reaction data and single-cell RNA sequencing data are integrated, and feature extraction, fusion and selection are performed by using a deep reinforcement learning technology, so that high-precision prediction of single-cell drug sensitivity is realized. The algorithm mainly comprises five core modules: a multi-modal data integration and preprocessing module, a deep reinforcement learning feature extraction and fusion module, a dynamic feature selection and importance evaluation module, a cross-domain migration and adaptive learning module and a deep reinforcement learning prediction model and performance optimization module. The modules work cooperatively, potential features can be learned automatically from multi-modal data, a feature fusion strategy can be optimized, a key feature subset can be selected dynamically, and a model structure and parameters can be adjusted adaptively according to features of target domain data, so that prediction accuracy and stability are improved remarkably. Experimental results show that the algorithm shows significant advantages in multiple evaluation indexes, and compared with a traditional prediction method, the algorithm has higher prediction accuracy and wider applicability. Therefore, the invention provides a novel and efficient drug sensitivity prediction tool for the fields of personalized medical treatment and drug research and development, and has important practical application value.

The present disclosure provides methods, compositions and systems for identifying variant targets of interest, for example, in clinically actionable genes. Disclosed herein are representative assays for identifying gene variants, for example, that are implicated in one or more drug metabolism pathways. The methods, composition and systems disclosed herein enable a highly streamlined and cost-effective workflow for moving forward the emerging field of personalized medicine and related fields of study such as pharmacogenomics.

The invention discloses an intelligent storage management method for western medicine pharmaceutical ingredient data, and relates to the technical field of medical information, the method comprises the following steps: collecting medicine ingredient data through an API interface, carrying out semantic analysis and encrypted storage by using a natural language processing technology, and constructing a medicine self-recommendation platform; personalized medicine recommendation is provided for patients through electronic medical record analysis and retrieval of medicine component label attributes. Through calculation of a drug recommendation validity index ytx, drug recommendation validity can be preliminarily evaluated, gene compatibility analysis is further performed in combination with gene drug efficacy data of a patient, and after a gene compatibility analysis instruction is received, a gene compatibility index jsp is calculated. And finally, through calculation of a comprehensive gene adaptation index zht, evaluating the matching between the drug and the gene of the patient, and optimizing a drug recommendation scheme. According to the method, the accuracy and individuation level of drug recommendation can be effectively improved, side effects and treatment risks of drugs are reduced, and technical support is provided for individualized medical treatment.

The invention relates to a double-attention-enhanced drug prediction model MTDADP under a multi-task learning framework, and belongs to the technical field of medical artificial intelligence. According to the method, disease diagnosis detail information can be fully utilized, association between the disease diagnosis detail information and medical history can be deeply mined, and safer and more accurate personalized drug prescription recommendation can be realized. The method comprises the following steps: firstly, performing embedded representation on historical treatment records, current symptoms and test details of a patient by the MTDADP; the state representation of the patient is enhanced through double mechanisms of access level attention and feature dimension attention; then constructing a patient memory bank and carrying out two-channel retrieval, and comprehensively utilizing drug distribution information of similar and dissimilar patients; then, the safety of drug representation is enhanced through a graph convolutional network by using an EHR graph and a DDI graph; and finally, integrating multi-source information to carry out joint prediction, introducing disease diagnosis prediction as an auxiliary task, and carrying out optimization through a multi-objective loss function. Experimental results on a disclosed PIC data set show that the MTDADP is remarkably improved in Jaccard, F1 and PR-AUC indexes compared with an existing advanced baseline model, and the effectiveness of the method in the aspect of improving the drug prediction accuracy is verified.

The invention provides an application of a biomarker in platelet contamination degree evaluation. The biomarker comprises IDH2, LIMS1, MLEC, DIAPH1, GNAI2, ATP5F1B, RAB14, TBXAS1, JAM3, VDAC1, ATP2A3, ARHGAP6, VDAC3, ESAM, SLC25A5, ITGA6, PDLIM7, PF4V1, HSP90B1, HSPD1, RDH11, ATP2A2, VDAC2, RAB35, TREML1, GP5, PRDX3, ITGB1, MDH2 and GNB1. Based on the marker combination, platelet contamination samples can be accurately recognized and eliminated, and large-scale application of plasma proteomics to clinical scenes such as cancer early screening and personalized medical treatment is facilitated.

A genetic prime editing screening platform to identify functional variants related to human health and disease, is configured substantially to annotate genome with nucleotide resolution with actionable disease prediction and treatment for personalized medicine.

Described herein are computer-implemented systems, methods, and devices for pharmacogenomic determination. The system includes a data processor configured to receive pharmacogenomic data representing at least one pharmacogenomic annotation in association with at least one gene; a database configuration engine configured to receive at least one genomic variation of the at least one gene and to search the pharmacogenomic data for at least one association with each genomic variation to return the associated data, the associated data being a haplotype or diplotype and a phenotype; a report generator configured to generate at least one report comprising the associated data with the genomic variation associated.

The invention discloses a drug recommendation system based on causal co-occurrence enhancement, which fuses a co-occurrence relationship and a causal relationship between medical information to realize high-precision and high-safety personalized drug recommendation. Comprises: a medical information representation generation module based on a medical information co-occurrence relationship, used for constructing a medical entity co-occurrence graph and obtaining embedded representation through graph attention neural network learning; the patient treatment characterization generation module based on the medical information causal relationship constructs a treatment-level medical causal graph by using a causal discovery algorithm, and performs weighted fusion according to the causal roles (such as cause nodes and intermediate nodes) of entities to form patient treatment characterization; according to the drug recommendation module based on the co-occurrence causal relationship, co-occurrence enhancement factors and a causal effect matrix calibration mechanism are introduced on the basis of preliminary prediction, and it is ensured that a recommendation result has statistical rationality and causal interpretation at the same time. The accuracy, interpretability and clinical applicability of drug recommendation are remarkably improved.

The invention discloses a personalized drug metabolism management system based on body temperature data, and relates to the field of medical health management, and the system comprises a body temperature data collection module which is used for continuously monitoring and collecting body temperature time sequence data before and after a patient takes medicine; the body temperature response fingerprint construction module is used for generating body temperature response fingerprints corresponding to specific medicines and doses according to the body temperature time sequence data; the medicine state recognition module is used for recognizing the medicine metabolism state of the patient according to the body temperature response fingerprint; and the dosage optimization module is used for determining personalized optimal medicine dosage and medicine taking time according to the medicine metabolism state. The problem that a traditional medicine management method cannot meet individual difference requirements is solved.

Methods and systems are described for a high-level approach to organ generation by self-assembly using bioprinting, overcoming longstanding challenges in replicating the intricate architecture and function of complex organs. In some embodiments, these methods are used to produce lung tissues and microtissues, demonstrating how mini-lung constructs can be produced on a small scale through specialized processes and media. In such embodiments, methods and systems for generating bioprinted lung tissues and microtissues are provided, as are tissues made by such methods. The tissues find use in implantation, drug discovery, personalized medicine, and other applications. Methods for the large-scale manufacturing of lung epithelial stem cells are also provided.

Disclosed examples relate to a system and method for creating and integrating self-supervised learning (SSL) digital movement biomarkers into electronic health records (EHRs) for enhanced diagnostic support and treatment monitoring, particularly for rare diseases and movement disorders. Example systems employ SSL techniques to analyze video data, tracking body key points and extracting kinematic data without the need for human-labeled annotations. Disclosed examples generate digital movement biomarker profiles that quantify symptoms and track disease progression, facilitating the objective measurement of drug impacts on symptoms. Example systems include components for video processing, compliance checking, pose estimation, context analysis, kinematic measurement, and classification. The digital movement biomarker profiles are stored securely and made accessible for clinical use, offering a novel approach to personalized medicine by enabling the identification of disorder-specific traits and symptom similarities across different disorders.

This application relates to the production of solid dosage forms. The invention utilizes 3D printing technology, particularly fused filament manufacturing (FFF) 3D printing, to produce solid dosage forms, such as pharmaceutical tablets. The production process utilizes novel printing filaments containing active ingredients, typically on a spool. Such active ingredient-containing filaments have proven to be very robust, and the principles outlined in this disclosure provide a variety of viable formulations directly from the 3D printer. This provides, for the first time, a feasible means for on-site (e.g., in-pharmacy) 3D printing of personalized medicines tailored to patient needs. The invention also relates to specialized software for operating printing devices and for monitoring the real-time operation of multiple printing devices to enable easy detection of malfunctions, thereby facilitating regulatory approval and establishing a local, national, and global system conducive to regulatory compliance.

The invention relates to the technical field of biology, and discloses a construction method of a gastric cancer organoid culture system, which comprises the following steps: preparation of a conditioned medium: culturing L-WRN cells and collecting the conditioned medium; preparing a gastric cancer organoid culture medium, and mixing the conditioned culture medium, the basic culture medium, a plurality of growth factors and additives; and extraction and culture of organoid: separating cells from gastric cancer tissues, mixing the cells with matrigel, and culturing by using the prepared culture medium. According to the method, the repeatability and the stability of the organoid model are improved, the response consistency of the organoid in drug screening is improved, and the reliability of the model in disease mechanism research and personalized medical application is guaranteed.

A method for the diagnosis of diseases, in particular hematological malignancies or solid tumors, in a subject comprises the steps of isolating (21,61) an extracellular vesicle pellet (3a,3b) obtained from a sample (2) of a biological fluid previously taken from the subject; measuring (31,41,51-52), from said pellet, parameters representative of the six biomarkers (I) size, (II) total amount of the extracellular vesicles, (III) amount of the extra- cellular vesicles expressing on their own surface (III) a first antigen and (IV) a first and a second antigen, (V) mean fluorescence intensity for each of said antigens and (VI) nucleic acid content of the extracellular vesicles; performing a first comparison of the biomarker-representative parameters with respective cut-off values; assigning a partial score to each parameter or to the six biomarkers on the basis of the above first comparison step; calculating a final score, i.e. a subject score, as the sum of the partial scores; performing a second comparison of the final score with at least one predetermined diagnostic threshold value, preferably with two diagnostic upper and lower values; estimating the probability diagnosis of said pathology on the basis of the above second comparison step. The combination of the six biomarkers, in the context of personalized medicine, overcomes the prior art method limitations due to the high clinical heterogeneity between subjects, in terms of sensitivity, with reference to a given pathology, for one rather than the other biomarkers and their relative parameters selected from the six.

Based on experimental data, observed i n the Personalized Medicine Laboratory of the requesting institute, which reveal an increase in the inorganic polyphosphate (iPolyp) in fecal samples from subjects affected by colorectal cancer compared to samples from individuals not affected by the same disease, inorganic polyphosphate is suggested as a novel non-invasive biomarker to be detected in fecal samples for the early diagnosis of colorectal cancer, to be associated with other screening methods such as fecal occult blood, in order to provide greater predictivity, i.e., the probability that a subject with a positive diagnostic test is actually affected by colorectal cancer.

The field of the invention is in vitro assays for the production of in vitro organ culture systems and the use of such in vitro organ culture systems for screening assays in drug screening, patient selection and personalized medical context. More specifically, the present invention relates to an in vitro method of establishing a tumor organoid from a cancer tissue sample obtained from a subject, an in vitro method of providing immune cells activated by an autologous tumor organoid, a method of preparing an in vitro organ culture system for mimicking interaction of a tumor with an immune system of a subject, and a method of preparing an in vitro organ culture system for mimicking interaction of a tumor with an immune system of a subject. The invention relates to an in vitro organ culture system, and to an in vitro organ culture system produced thereby, to a method for determining the reactivity of a tumor to at least one immunooncology drug therapy using said in vitro organ culture system, and to a kit for preparing an in vitro organ culture system that mimics the interaction of a tumor with the immune system of a subject, as disclosed herein.

The present invention provides a novel user authentication system based on biometric authentication which involves user confirmation and user identification. The user authentication system is based on human exhaled breath, and executed using principles of machine learning. The user authentication system of the present invention can also be used as a diagnostic tool by the correlation of the turbulence information to the occlusion in the extrathoracic passage, which is a major source of deposition of aerosolized therapeutics. The exhaled breath time series velocity signals based diagnosis can also be used for personalized medication and treatment.

An exemplary system comprises a memory that stores instructions and a processor communicatively coupled to the memory and configured to execute the instructions to perform a process. The process may comprise accessing an individual three-dimensional (“3D”) cochlea model of a cochlea of a recipient of a cochlear implant system that includes a stimulating lead inserted into the cochlea during a lead insertion procedure, generating, based on the individual 3D cochlea model and procedure data associated with one or more lead insertion procedures performed with respect to one or more other recipients, an individualized drug delivery profile for the recipient, and transmitting a command to the cochlear implant system to cause release of a drug from the stimulating lead in accordance with the individualized drug delivery profile.

An anaerobic chamber system to evaluate human enteric disease is described herein that can be used to test therapeutic components. In specific embodiments, the anaerobic chamber is used to determine the effect of one or more bacterial communities on ex vivo enteroid cultures. In one application, the anaerobic chamber system is used to determine the efficacy of therapeutic components in ameliorating human enteric disease using personalized medicine.

The invention relates to the technical field of data analysis, in particular to a patient medical information intelligent analysis method based on a big data artificial intelligence model, which comprises the following steps: processing large-scale and high-dimensional data according to clinical symptoms and disease degrees in multiple sources such as electronic health records, genome data and the like, extracting key features by utilizing a dimension reduction technology, and analyzing the key features; and information loss is reduced. The non-linear relation in the data can be identified and modeled, the complex mode is disclosed, and the medical data information of the patient can be analyzed more deeply. The refined analysis helps doctors to identify specific requirements of different patient groups, the effectiveness of clinical decisions is improved, and personalized medical treatment and resource optimization are realized.

The present disclosure provides methods and systems for genomic data representation and estimation of various biomarkers of interest in personalized medicine and particularly in cancer genomics such as molecular residual disease (MRD), tumor fraction (TF), blood tumor mutational burden (bTMB) and clonal hematopoiesis of indeterminate potential (ChIP).

Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays. Embodiments have multiple uses in genomics, proteomics, metabolomics, multi-omics data integration, drug discovery, personalized medicine and predictive modeling, demonstrating the versatility and importance of tensor regression models to understand the associations between omics data and complex diseases.