83 results about "Complex disease" patented technology

The invention relates to the technical field of medical data processing, in particular to an intelligent decision-making system and method for multi-modal medical data dynamic fusion, and the system comprises a data collection module which is used for collecting multi-modal medical data; the data preprocessing module is used for preprocessing the collected multi-modal medical data; the multi-modal fusion and decision generation module is used for constructing a cascade learning framework comprising a plurality of subtask models; the dynamic decision adjustment module is used for setting cascade priorities among different modes in different diagnosis and treatment scenes; and the closed-loop optimization module is used for constructing a closed-loop process in combination with a national diagnosis and treatment guide and real clinical data. According to the method, in different diagnosis and treatment scenes, the system can automatically adjust the cascade priority of each modal data according to the illness state stage, so that the characterization capability of the decision model on the complex illness state is remarkably improved, the limitation of traditional static fusion is broken through by the dynamic fusion mechanism, and the accuracy of diagnosis and treatment suggestions and the scene adaptability are remarkably enhanced.

The invention relates to the technical field of intelligent medical treatment, and discloses a medical large model driven cross-department collaborative prescription generation method and system. According to the method, electronic medical records of patients and prescription data of multiple departments are acquired, key information is structurally extracted to generate directional vectors, and transfer learning and fine adjustment are performed by using historical conflict cases and a drug knowledge graph based on a medical basic large model and an AI chip, so that a collaborative prescription model is constructed. The model can identify drug incompatibility and dosage risks among departments and generate a prescription suggestion set. A doctor can dynamically correct a prescription based on feedback, a collaborative report containing a medication time sequence, a monitoring index and an emergency scheme is generated after multiple rounds of collaborative optimization, and the safety and effectiveness of multi-department combined medication of complex diseases are remarkably improved.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention relates to the technical field of artificial intelligence and medical diagnosis, in particular to an intelligent diagnosis and dynamic optimization method combining traditional Chinese medicine and western medicine. The intelligent diagnosis and dynamic optimization method based on combination of traditional Chinese medicine and western medicine includes the steps that multi-modal data such as traditional Chinese medicine tongue picture images are collected and preprocessed, traditional Chinese medicine syndrome differentiation and western medicine disease models are input respectively, a supervision model group is distributed or a collaborative diagnosis mechanism is started according to model confidence, and a structured diagnosis report is generated through double verification. And obtaining a compliance diagnosis result through data processing, and dynamically adjusting a knowledge calling priority according to user feedback to obtain a calling result. According to the method, a complete closed loop of data acquisition, intelligent diagnosis, safety supervision and feedback optimization is constructed, the system is endowed with self-learning and self-adaptive capabilities while the precision, the safety and the compliance of combined diagnosis of traditional Chinese medicine and western medicine are improved, and an efficient and reliable technical path is provided for intelligent diagnosis and treatment of complex diseases.

The invention discloses a multi-omics data integration and classification method, system and device based on hierarchical attention, and is applied to the field of precise medical big data analysis. The method comprises the following steps: firstly, generating feature embedding and feature importance scores through a plurality of parallel feature-level attention modules; then, embedding and inputting all the characteristics of the omics into a unified omics-level attention module, and generating omics embedding and omics importance scores; and finally, a classification prediction task is executed based on omics embedding, and a classification result is output for disease classification. The invention completely abandons a traditional dependency graph convolutional network and an integration normal form of variants of the dependency graph convolutional network, and provides a universal hierarchical attention integration architecture. The framework supports classification tasks of any complex diseases, is not limited by omics data types and combination modes, not only is remarkably superior to a traditional integration normal form in classification performance, but also shows a unique negative generalization distance, and proves that the framework has excellent generalization ability. Meanwhile, features and omics importance scores automatically output by the model provide a powerful analysis tool for biomarker discovery and precise diagnosis and treatment of complex diseases.

In some embodiments provided herein is a generative foundation model trained over millions of health system-scale electronic health records along with web-scale medical text corpora to acquire knowledge of both medical practices and theories, and use of the generative model for rare disease diagnosis (including rare ophthalmic, diseases and rare systemic diseases), emergency condition identification (including ophthalmic emergencies and systemic emergencies), complex disease solving ("diagnostic puzzles"), or generating multimodal medical imaging reports (including ophthalmic images and radiology images such as X-rays and CT scans). In some embodiments, the generative model involves the use of language data, for pre-training, language data for supervised finetuning using a instruction tuning approach (e.g., QA pairs), and a human-machine hybrid evaluation strategy. In some embodiments, both the pre-training and supervised finetuning phases involve the use of a particular method of scaling to extend the context window. In some embodiments, the human-machine hybrid evaluation strategy involves language data for automated evaluations, as well as evaluations by generalists and by different specialists (e.g., ophthalmologists and radiologists) of varying levels of experience. In some embodiments, the generative foundation model, MetaGP, is used for unmet clinical needs through integration of medical and multimodal imaging data.

The invention discloses a method and system for evaluating the treatment effect of traditional Chinese medicine based on single cell and spatial transcriptome data, and the method comprises the following steps: respectively obtaining single cell data and spatial transcriptome data of a tissue sample before and after administration, and carrying out the preprocessing; classifying the cells and identifying cell types; the expressed ligand and receptor genes are paired to obtain ligand-receptor pairs, and the ligand-receptor pairs which are differentially expressed before and after administration are screened out; acquiring space coordinate information of a single cell, and constructing a cell interaction network and a differential gene network according to the cell type, the ligand-receptor pair and the space coordinate information; weighting processing is conducted on the cell interaction network and the differential gene network, and comprehensive indexes for evaluating the effect of the traditional Chinese medicine are obtained.The brand-new method for evaluating the disease treatment effect of the traditional Chinese medicine is provided, the method is scientific and reliable, and the treatment effect of the traditional Chinese medicine on complex diseases can be accurately reflected.

The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

The present invention discloses a group of multifunctional active peptides whose amino acid sequences are GLLHLLHHLLHH and GLLKLLHHLLHH, respectively. The present invention also provides applications for anti-infection, anti-tumor, calcification promotion, and hydroxyapatite adsorption. The present invention has the advantage over the prior art in that the multifunctional active peptides provided by the present invention can simultaneously address the different characteristics of complex diseases. Based on their anti-infection, anti-tumor, calcification promotion, and hydroxyapatite adsorption properties, they can be used to treat a variety of diseases.

The invention relates to a biological preparation prepared from astragalus polysaccharide and application of the biological preparation. The biological preparation comprises the following components: traditional Chinese medicine components: 15-20g of an astragalus-codonopsis pilosula-lucid ganoderma co-extract, 10-15g of an astragalus-atractylodes macrocephala-Chinese yam co-extract, 8-12g of an astragalus-medlar-dendrobe co-extract, 6-10g of an astragalus-ginseng-polygala tenuifolia co-extract and 5-8g of an astragalus-coptis-kudzu vine root co-extract; the biological preparation comprises the following components: recombinant human interferon alpha-2b150-200IU, 0.03-0.05 mg of an anti-PD-1 antibody fragment, 0.08-0.1 [mu] g of a brain-derived neurotrophic factor (BDNF) and 0.05-0.1 mg of a glucagon-like peptide-1 (GLP-1) analogue, and by means of multi-target linkage, traditional Chinese medicine polysaccharide synergistic protection of the biological preparation, dynamic dosage regulation and the like, the treatment effect on complex diseases is improved, and the dosage and side effects of the biological preparation are reduced.

The invention discloses an AI service development system based on an agent technology, which relates to the technical field of medical health and comprises a medical professional module, a digital duplication module, a continuous optimization module, an interactive service module, a safety management module, an ethical and compliance management module, a basic service module and an intelligent diagnosis module. According to the AI service development system based on the agent technology, a multi-dimensional medical knowledge graph which comprises a large number of medical entities and relation edges and a dynamically updated clinical path library is constructed by utilizing a semantic network, accurate and reliable medical knowledge support can be provided, and medical data is deeply analyzed in cooperation with an intelligent diagnosis module; the system provides auxiliary diagnosis of complex diseases, improves the accuracy and efficiency of diagnosis, is high in intelligent level, and can provide accurate and efficient diagnosis service for patients especially in the aspect of diagnosis and treatment decision support of the complex diseases.

The method of detecting SNP combinations in GWAS data based on the crowned porcupine algorithm of the genetic frequency control pool belongs to the field of intersection of computer data processing and genetics. Four different individual generation strategies are designed, namely the global guidance strategy, the collaborative perturbation strategy, the local exploration strategy and the convergence acceleration strategy; a gene sampling module with a frequency suppression mechanism, namely the genetic frequency control pool, is introduced; a method of automatically inferring the maximum detectable interaction order based on the sample size is introduced; a significance detection mechanism combining the K2 score and the G test is adopted; the method of the present invention significantly improves the computational efficiency and detection accuracy, and effectively identifies high-order SNP combinations related to complex diseases.

The invention discloses a circRNA (Ribonucleic Acid) and miRNA (Micro Ribonucleic Acid) interaction prediction system and a circRNA and miRNA interaction prediction method of a graph Fourier pulse neural network. The method comprises the following steps: on the basis of high-throughput sequencing omics data of complex diseases, constructing a heterogeneous biological information network containing drugs, diseases, proteins, circRNA, miRNA and lncRNA; converting the topological features of the entities into a unified feature space by using a graph convolutional network; designing a pulse graph neural network in combination with Fourier coding and a pulse neural network, and extracting a topological structure and high-order semantic features in the network; fusing sequences, topologies and semantic features of circRNA and miRNA through a gate multilayer perceptron to obtain embedding features of circRNA and miRNA; and finally, the interaction of circRNA and miRNA is predicted by adopting a Bayesian classifier. According to the method, heterogeneous biological information is modeled from the perspective of network science, Fourier coding, spiking neurons and graph embedding learning are utilized, the action mechanism of circRNA and miRNA in complex diseases can be disclosed, and the method has good practicability and application prospects in the fields of artificial intelligence, life science, clinical medicine and the like.

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.

The invention provides a method and system for gene expression quantitative trait locus analysis, and relates to the technical field of genomics, the method comprises the following steps: carrying out sample and locus level quality control on input genotype data, and generating a standardized genotype matrix; performing standardization processing and low expression gene filtering on the input gene expression data to generate a standardized expression quantity matrix; performing principal component analysis according to the standardized genotype matrix to obtain a group structure covariable; carrying out implicit factor analysis on the basis of the standardized expression quantity matrix to obtain a technical batch effect covariable; quantitative character site correlation analysis is carried out through a standardized genotype matrix, a standardized expression quantity matrix, a population structure covariable and a technical batch effect covariable. The method is suitable for functional genomics research, complex disease genetic mechanism analysis and precision medical related functional genetic variation mining scenes.

The invention discloses a high-stability reversible deformation carbon nanotube fiber sensor, and a device and a method thereof, and relates to the technical field of electrochemical biosensors. According to the sensor, fusiform carbon nanotube fibers are adopted as a sensing base body, sensitive materials are embedded into the fibers in the expansion state of the fusiform carbon nanotube fibers to form a stable sensitive layer, then the stable sensitive layer is recovered into a slender structure through screwing, and closed protection and minimally invasive implantation of the sensitive layer are achieved. The invention also provides a multi-parameter electrochemical detection device, which can realize in-vivo synchronous detection of various physiological indexes such as hydrogen peroxide, superoxide anions and glucose, and is suitable for continuous monitoring and early warning of a complex disease process. The device is novel in structure and high in function integration degree, has good mechanical protection performance and biological compatibility, is particularly suitable for multi-index in-vivo continuous monitoring, and has wide application prospects in the scenes of chronic disease early warning, postoperative monitoring, metabolic disorder evaluation and the like.

The application belongs to the technical field of medicine manufacturing, and discloses a through-the-du-to-soothe-the-nerves paste and a preparation method thereof. The raw material composition of the through-the-du-to-soothe-the-nerves paste comprises cornu cervi 10 g, zizyphus jujuba mill 30 g, notopterygium 10 g, panax 10 g, pueraria 30 g, astragalus 30 g, cassia bark 10 g, radix paeoniae alba 20 g, citrus grandis 10 g, poria cocos 20 g, licorice 6 g, radix scutellariae 6 g, and malt 20 g. The optimal preparation process parameters are as follows: water addition amount 1200 ml, soaking time 70 min, and decocting time 180 min. The application can relieve the low mood of patients, is used for treating insomnia, pain, and fatigue caused by depression, and blocks the development of depression. The application has the advantages of flexible prescription, individualized prescription, and temporary adjustment according to individual conditions, time, and place. The paste also has the characteristics of wide application range, main and secondary considerations, combination of treatment and prevention, and suitability for various complex diseases.

Provided is a medical assistance device including an acquisition unit configured to acquire personal data including examination data and medical interview data of a user, and an output unit configured to determine a disease state of the user for an individual disease forming a complex disease, based on the personal data, and configured to output an improvement target to be achieved by the user to improve a complex disease state, when a combination of the disease states of the individual diseases or a combination of the disease state of the individual disease and the personal data is the complex disease state.

The invention discloses a method for analyzing the action mechanism of a decoction of trichosanthes kirilowii maxim, allium macrostemon and pinellia ternate for treating heart failure (heart failure), and the multi-component, multi-target and multi-channel synergistic action mechanism of the prescription is systematically analyzed by integrating network pharmacology and metabonomics technologies. The method provides a systematic analysis strategy for research on a mechanism of treating complex diseases by a traditional Chinese medicine compound.

This invention provides an automated loading and fixation detection system for small model animals. Utilizing the siphon principle, the system constructs a siphon pathway using both soft and rigid capillaries, achieving pump-free loading and fixation of animals. Combined with an imaging analysis unit, it performs imaging analysis of the animals. By controlling the siphon flow rate, it enables rapid and automated fixation of animals without gel embedding or prolonged anesthesia, effectively improving fixation efficiency and shortening fixation time. Furthermore, the detection system of this invention is simple in structure, easy to operate, economical, and readily applicable. It achieves high-throughput, automated, pump-free fixation, and non-microfluidic chip-based automated loading and fixation detection for small model animals, providing highly promising technical support for the further development and screening of drugs for treating complex diseases, as well as other related research on small model animals.

A plurality of data pairs are created by matching an element from a first modality with an element from a second modality. Each element from the first modality and each element from the second modality are tokenized to obtain first modality tokens and second modality tokens. A model is trained based on the plurality of data pairs, the training comprising learning a first embedding from the first modality tokens via a first attention-based encoder for the first modality and a second embedding from the second modality tokens via a second attention-based encoder for the second modality, calculating a cosine similarity between the first embedding and the second embedding for each data pair and computing a loss between predicted items and ground truth based on the cosine similarity. The predicted items with a minimal loss are validated to obtain at least one candidate therapeutic.

This invention relates to a method for disease therapeutic target discovery and drug prediction based on multi-omics networks and deep learning models, belonging to the interdisciplinary field of bioinformatics and artificial intelligence drug discovery. The method includes: integrating genomic expression profiles and common molecular interaction data from disease and control groups to construct a candidate whole-genome network; refining the network based on expression profile data through systematic modeling and the AIC criterion to obtain the real molecular interaction network; extracting the core network using the master network projection method and identifying key targets through pathway analysis; predicting candidate drugs interacting with the targets using a pre-trained deep neural network model; and finally screening potential therapeutic drugs based on multi-dimensional criteria such as regulatory ability, sensitivity, and toxicity. This invention achieves a complete integration from disease mechanism analysis to drug prediction, and is particularly suitable for complex diseases such as atopic dermatitis. It can systematically discover precise targets and efficiently predict repositionable drugs, significantly improving R&D efficiency.

A method, computer system, and a computer program product for biomarker identification is provided. The present invention may include generating a plurality of higher-order joint cumulants based on an input data matrix. The present invention may include identifying one or more significant higher-order joint cumulant groups from the plurality of higher-order joint cumulants. The present invention may include embedding the one or more significant higher-order joint cumulant groups into a lower dimensional network. The present invention may include identifying one or more biomarkers.

The present disclosure provides novel self-amplifying RNA (saRNA) constructs that demonstrate enhanced protein expression, prolonged durability, reduced immunogenicity, and the ability to express multiple therapeutic proteins homogeneously. The saRNA constructs comprise a 5' untranslated region (5'UTR), non-structural protein genes derived from alphaviruses, at least one gene of interest encoding a therapeutic protein, a 3' untranslated region (3'UTR), and one or more modified nucleosides. Also disclosed are dual construct systems comprising a first construct encoding non-structural proteins and a second construct encoding one or more genes of interest. Methods of producing and using the saRNA constructs for engineering cells, particularly immune cells, for treatment of various conditions including cancer, inflammatory conditions, and infectious diseases are provided. The saRNA constructs enable the generation of "armored" immune cells expressing multiple therapeutic proteins, thereby providing a multi-pronged approach to complex diseases.

The application discloses a kind of volume calculation and geometric reconstruction methods, devices, equipment and medium inside road disease, which comprises: standardization processing to the three-dimensional simulation point cloud data of inside road disease, to obtain standardization simulation point cloud data;Pretreatment data and identify disease connected sub-region, determine the internal starting point set to construct starting point set, based on starting point set Construction space step direction and step search boundary point, generate boundary point set, fusion boundary point set obtains disease space profile, based on disease space profile completes disease volume calculation and three-dimensional geometric shape reconstruction;Since the application accurately locates the disease boundary by internal starting point constraint combined with multi-direction step search, restores complete disease geometry by fusing boundary points, accurately realizes the topological reconstruction and volume calculation of complex disease network, effectively solves the problem of incomplete disease point cloud boundary and complex topology of internal complex disease of road, and improves the integrity of disease reconstruction and the accuracy of volume calculation.

A method for capturing distinct higher order interactions of a dataset relevant to biological inferences includes deriving Hamiltonian parameters for the dataset, wherein the dataset includes data responsive to a phenotype of interest. The method further includes generating a partition function responsive to the Hamiltonian parameters; calculating cumulant moments of the partition function and deriving higher order cumulants using the Hamiltonian parameters, wherein the higher order cumulants are responsive to the phenotype of interest.

Many biologic processes taking place inside a living organism are unpredictable in time and space, and cannot be known exactly. These mechanisms and interactions among them are better modeled as physiological random processes, the statistics of which are fully described by joint characteristic functionals. The present invention provides methods for the estimation of joint characteristic functionals through imaging of multiple physiological random processes. This technology can be used to study complex diseases, such as tumors and viral infections, by imaging the biological processes involved with disease progression and response to treatment.

The invention discloses a protein degradation agent based on an endosome-phagosome connection assembly and application of the protein degradation agent, and belongs to the technical field of protein degradation and nano biological medicine. According to the endosome-phagosome connecting assembly, by inserting an endosome membrane and marking endosome degradation protein, a special degradation technology strategy enables the endosome-phagosome connecting assembly to expand an autophagy degradation target spectrum in traditional cytoplasm to various target proteins outside a cell membrane / cell, and the current development situation of a current targeted protein degradation tool can be thoroughly changed possibly. The preparation method is simple, raw materials are easy to obtain, the method is suitable for research and application transformation, the traditional degradation thought is broken through by utilizing an endosome-phagosome connecting assembly, most target proteins can be degraded through an endosome-autophagy-lysosome approach, the design is novel, the application range is wide, the cost is low, the efficiency is high, a very good degradation effect can be achieved, and the method is suitable for popularization and application. The method can be applied to the field of various complex diseases and has huge potential development and transformation prospects.

The invention discloses an underground pipeline structure health intelligent assessment method and system, and relates to the technical field of underground pipeline structure health assessment, and the method comprises the steps: constructing a disease recognition model employing an improved CNN-Transform hybrid architecture, and inputting a multi-modal feature vector into the disease recognition model; the feature vectors are fused and generated in real time in combination with edge computing equipment, the problem that traditional single data evaluation is one-sided is solved, and multi-source data are rapidly integrated; according to an improved CNN-Transform hybrid architecture, local features of cracks, corrosion and deformation are extracted through CNN, global disease association is captured through Transform, disease types and three-dimensional coordinate positions of the inner wall of a pipeline can be accurately output, and the complex disease misjudgment rate is reduced; the health recognition model outputs quantitative scores and health area positioning, and the defect that only diseases are recognized and overall health assessment is not available traditionally is overcome.