960results about "Drug references" patented technology

The invention belongs to the technical field of antibacterial peptide activity identification and evaluation, and relates to an antibacterial peptide activity and MIC value combined prediction framework based on cross-modal deep learning, and the framework uses a protein language model ESM2 to respectively carry out token-level semantic embedding coding on an antibacterial peptide sequence and a pathogen protein sequence; performing cross-modal feature extraction and fusion through a multi-branch structure comprising a Mama module, a multi-head self-attention mechanism and DASM 1D convolution; a multi-task decoding structure is adopted to realize antibacterial peptide activity classification and MIC value regression prediction at the same time; according to the method, functional characteristics in the sequence can be effectively mined, the accuracy and generalization ability of antibacterial peptide activity and MIC value prediction are remarkably improved, and a reliable calculation tool is provided for high-throughput screening and rational design of the antibacterial peptide.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention discloses a bone infection and drug resistance prediction method and system fusing a knowledge graph and a graph convolutional network. The method comprises the following steps: preprocessing bone infection multi-source heterogeneous data to obtain a standardized data feature matrix; constructing a bone infection knowledge graph based on the matrix and obtaining a knowledge embedding matrix, and fusing the two to generate a medical semantic constraint fusion feature matrix; key medical variables are screened, the maximum information coefficient (MIC) of the key medical variables is calculated, and an adjacent matrix is constructed in combination with medical association strength factors; inputting the fusion feature matrix and the adjacent matrix into a GCN spatial feature extraction module and a BiGRU time sequence dependence capture module to obtain spatial and time sequence features, and fusing the spatial and time sequence features into a space-time fusion feature matrix; and inputting the result into a double-task prediction module, and outputting a bone infection and drug resistance grading probability prediction value. According to the invention, accurate and rapid prediction of bone infection prediction and drug resistance grading can be realized.

Systems, devices and methods are provided for determining a medication dose for a patient or user. The dose determination can account for recent and / or historical analyte levels of the patient or user. The dose determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity, and / or behavior. Many different dose determination embodiments are set forth, pertaining to a wide array of different aspects of the system or environment in which the embodiments can be implemented.

The invention relates to the technical field of medicine data analysis, in particular to a method, a system and equipment for generating a medicine relocation research report and a medium. According to the method for generating the drug relocation research report, a drug relocation request submitted by a pharmaceutical enterprise is decomposed into three sub-tasks of indication extension analysis, target matching analysis and drug effect simulation verification, and an indication extension analysis agent, a target matching analysis agent and a drug effect simulation verification agent are called respectively; the contribution value of each link is calculated through multi-agent cooperation, and finally a relocation research report containing potential rating and confidence evaluation is generated based on the drug-disease knowledge graph. By implementing the technical scheme provided by the invention, the efficiency and reliability of drug relocation analysis are improved, and scientific and credible decision support is provided for pharmaceutical enterprises.

The invention discloses a drug combination patent intelligent identification method and device based on a large language model, and the method comprises the steps: obtaining a standard data set built based on drug combination labeling information of patent data, carrying out the data expansion of the standard data set, and generating an initial training set; a parameter efficient fine tuning technology is adopted, the initial training set is utilized to train the base large language model, and an initial recognition model is generated; screening and identifying difficult samples in the unlabeled patent data by using the initial identification model, and adding the difficult samples into the initial training set to form an enhanced training set; performing iterative training on the initial recognition model by using the enhanced training set and adopting a multi-task learning framework in combination with a comparative learning mechanism to obtain a drug combination patent recognition model; the to-be-recognized patent data is input to the drug combination patent recognition model, and a drug combination information recognition result is obtained.The accuracy, data efficiency and model interpretability of drug combination patent recognition can be remarkably improved, and the computing resource requirement and the manual review cost are reduced.

The invention belongs to the technical field of traditional Chinese medicine information processing, and provides a traditional Chinese medicine disease prescription data base construction method and system.The method comprises the steps that five types of data including ancient literatures, modern medical records, prescription data, traditional Chinese medicine resources and medical record manuscript images are accessed through an extensible adapter frame; a traditional Chinese medicine term service platform is constructed by using multi-source term resources, and standardized data is generated from the analyzed data; the method comprises the following steps: extracting diseases, syndromes, prescriptions and traditional Chinese medicine entities by adopting a rule engine and a domain self-adaptive entity recognition model, on the basis of a public traditional Chinese medicine standard knowledge graph, aggregating neighbor node information through GAT, and introducing a contrast learning optimization entity relationship classifier to obtain an enhanced entity vector and classify a semantic relationship; constructing a knowledge graph based on the extracted semantic relationship; and carrying out data quality and algorithm performance monitoring and tracing on data access analysis, standardization processing, knowledge extraction and graph construction. Uniform access and standardized integration of multi-source heterogeneous traditional Chinese medicine data are realized.

The invention relates to a medication recommendation method and system. The method comprises the following steps: preprocessing original medical data of a current patient into a standardized time sequence diagnosis and treatment sequence; inputting the time sequence diagnosis and treatment sequence and the medical professional knowledge base into a code-doctor-seeing double-level attention model for processing to obtain code-level aggregation features of each doctor seeing and corresponding doctor-seeing-level attention weight, and performing weighted aggregation on the code-level aggregation features based on the doctor-seeing-level attention weight to obtain a code-level aggregation feature; generating a global health representation of the current patient; based on at least one of the multiple medical feature information of the current patient, similar historical cases are retrieved from a case information base to serve as reference cases; and generating and outputting a medication recommendation list for the current patient at least based on the global health representation of the current patient and the medication scheme of the reference case. By adopting the method and the device, the accuracy and the safety of drug recommendation for the current patient can be improved, so that a doctor can be better assisted to make a drug decision.

The invention discloses a computer-aided drug screening method, system or device based on FBXO2 and PKM2. The invention provides a brand-new method, system or equipment for screening oral squamous cell carcinoma treatment drugs based on FBXO2 and PKM2, provides a tool for new drug development and clinical application for treatment of oral squamous cell carcinoma, and has a wide application prospect.

The invention relates to the technical field of intelligent agriculture, and discloses a laying hen antibiotic-free breeding system and method.The technical defect that a general model cannot adapt to the influences of biological individual heterogeneity and environment hysteresis is overcome by constructing a dynamic metabolism reference containing environment correction logic, dynamic and accurate adaptation of the breeding reference is achieved, and meanwhile, the breeding efficiency of laying hens is improved. By means of parallel time sequence feature extraction and metabolic damping kinetic analysis, medicine forced intervention signals and natural physiological recovery signals are effectively separated and recognized on the physical level, the problem that hidden illegal medicine use is difficult to monitor is solved, in addition, in combination with an antagonism verification mechanism of the law of conservation of biological energy, the accuracy of monitoring is improved. A non-violating physical anti-counterfeiting barrier is constructed, the data modification behavior is effectively identified, and the supervision transparency and compliance credibility of the whole process of antibiotic-free cultivation are remarkably improved.

The invention relates to the technical field of image analysis, in particular to a targeted drug curative effect prediction method based on image recognition, which comprises the following steps: acquiring tissue images and nuclear morphological parameters by a microscope, establishing a database in combination with transcripts, extracting an injury area, recognizing image features through a convolutional neural network, and constructing a prediction model; and inputting candidate drug molecular structures for molecular docking, calculating a repair progress by combining animal verification to establish a curative effect model, predicting drug scores and response time based on the curative effect model to generate a ranking list, screening high-score drug cells, verifying monitored survival, comparing, predicting and outputting a result. The method comprises the following steps: extracting a cell nucleus form, revealing a relation between damage and molecular abnormality in combination with a transcriptome, identifying a target spot corresponding to an abnormal mode and pathological change through deep learning, performing affinity prediction and animal verification on a drug structure, quantifying the repair progress by adopting image difference, and evaluating the curative effect with two dimensions of structure and function. And curative effect scores and response prediction are output to realize system sequencing, so that drug screening is more accurate and practical.

The invention belongs to the technical field of artificial intelligence algorithm and bioinformatics crossing, and relates to a drug interaction prediction method based on multi-modal molecular characterization. According to the method, through the edge perception GCNII architecture and the Hop2Token multi-hop coding mechanism, effective modeling of atomic-level and bond-level local environments and a cross-substructure high-order dependency relationship in drug molecules is realized, and the accuracy and robustness of drug interaction prediction are improved. According to the method, Mol2Vec and MolT5 cross-modal molecular characterization is integrated, fusion of molecular overall semantics and substructure grammar semantic association is achieved, and the generalization ability of the model to complex molecules and unknown medicine combinations is remarkably improved. According to the method, the dynamic feature screening algorithm driven by the SHAP value of the artificial intelligence technology is adopted for biological verification, the feature redundancy problem is effectively solved, the molecular biological information analysis processing calculation efficiency and the model transparency are improved, and the traceability of the prediction process is guaranteed.

The embodiment of the invention discloses a drug molecule discovery method and device, a medium and equipment, and the method comprises the steps: extracting an entity of an input text, recognizing the intention of the input text, and scheduling one or more processes in drug molecule discovery processes related to the entity according to an intention recognition result, so that a user only needs to give the input text, and the user experience is improved. The subsequent operation of the drug molecule discovery process can automatically schedule one or more processes in the drug molecule discovery processes related to the entity in the input text according to the intention result of the input text, so that a user is prevented from manually importing and exporting data between different processes, and the processing efficiency of drug molecule discovery is improved.

The invention discloses a drug relocation method and system based on heterogeneous knowledge and structure fusion, and belongs to the technical field of medical care informatics. The method comprises the following steps: constructing a biomedical domain knowledge heterogeneous graph; generating disease knowledge embedding and drug knowledge embedding corresponding to a target drug-disease pair based on the biomedical domain knowledge heterogeneous graph; generating disease structure embedding and drug structure embedding corresponding to the target drug-disease pair by constructing a drug-drug similarity network, a disease-disease similarity network and a drug-disease association network; and based on disease knowledge embedding, drug knowledge embedding, disease structure embedding and drug structure embedding, obtaining a drug relocation result. According to the method, complex biological network characteristics are accurately captured and complex entity information is finely modeled through an innovative drug relocation model, so that accurate drug relocation is realized.

The invention provides an intelligent symptom machine distinguishing and treating system based on four diagnostic methods of traditional Chinese medicine. The system comprises a four diagnostic method information acquisition module used for acquiring multi-modal data; the multi-modal data processing module is used for preprocessing the multi-modal data; the feature fusion module is used for carrying out feature fusion on the preprocessed data, and the domain-specific reasoning core module is used for carrying out traditional Chinese medicine pathogenesis dialectical reasoning on fused features based on a thinking chain technology and outputting structured results including dialectical analysis, syndrome types, treatment rules and prescriptions; the traditional Chinese medicine knowledge graph enhancement module is used for performing verification, constraint and knowledge enhancement on the structured result and outputting a second structured result; the interpretability output module is used for outputting a diagnosis and treatment report according to the second structured result, and displaying the diagnosis and treatment report through a graphical user interface. The application utilizes a domain-specific large language model to deeply understand complex association among symptoms and infer pathogenesis behind, and generates a complete syndrome differentiation and treatment scheme conforming to traditional Chinese medicine logic; the intelligent degree is high, and reliability guarantee is provided for clinical application through integration of the traditional Chinese medicine knowledge graph.

A device may include a data acquisition layer comprising a patient information collector and a drug information collector, each operable to ingest patient-specific clinical data and drug-specific data, respectively. A device may include an analysis engine comprising a multi-criteria ranking engine configured to compute composite suitability score for candidate medications by weighting one or more of medication efficacy, medication safety, medication resistance, medication cost, or patient-specific coverage factors. A device may include a presentation layer comprising a summary dashboard configured to display a ranked medication recommendation set produced by the analysis engine.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating answers to medical questions using neural networks and other components. In one aspect, a method includes: obtaining question data representing a medical question; obtaining a plurality of document snippets from a medical database that stores medical documents; for each document snippet in the plurality of document snippets, determining a relevance score for the document snippet by using a ranking neural network based on the document snippet and the medical question; selecting, based at least in part on the relevance scores, a subset of the plurality of document snippets; generating a prompt that includes (i) the medical question and (ii) the subset of the plurality of document snippets; and generating an answer to the medical question based on processing the prompt using a generative neural network.

An ideation platform device and method using a diagram are disclosed. An ideation platform device using a diagram, according to one embodiment of the present invention, can comprise: a C-K canvas module for providing a C-K canvas divided into a concept space and a knowledge space and connecting a concept and knowledge to each other on the C-K canvas through a chaining process so as to help a solution search for resolving a problem; and an instance management module for storing and managing, as one instance, the C-K canvas, for which a solution search is completed, including the concept, the knowledge, and information about an interconnection relationship.

The invention relates to a drug interaction prediction method and system based on multi-view comparative learning, and belongs to the technical field of natural language processing. According to the method, two channels of a drug molecular map and a biomedical knowledge map are constructed in parallel, structural and semantic features are extracted by using a pre-trained heterogeneous map neural network, and multi-view comparative learning guided by information gain is introduced for joint optimization, so that the generalization ability and robustness of the model to unknown drug pairs are enhanced. According to the method, system evaluation is carried out on the performance of the system in two types of prediction tasks (multi-type and multi-label) and three prediction scenes. Experimental results show that the method has excellent performance in all tasks and scenes. Further case analysis also verifies the effectiveness of the system in predicting the interaction type of the unseen drug pair.

The invention belongs to the technical field of traditional Chinese medicine tablet manufacturing, and particularly relates to an online quality prediction method for stomach invigorating and digestion promoting tablets based on multi-modal fusion. Comprising the following steps: synchronously acquiring hyperspectral imaging data and near infrared spectrum data of a stomach invigorating and digestion promoting tablet sample; processing the near infrared spectrum data through a one-dimensional convolutional neural network to extract a first spectrum feature; parallel extraction of spatial features and spectral features is carried out on the hyperspectral imaging data through a double-branch feature extraction network; performing intra-modal fusion on the extracted spatial features and the second spectral features through a cross attention mechanism to obtain hyperspectral fusion features, and performing cross-modal fusion on the hyperspectral fusion features and the first spectrum through cross-modal multi-head attention to generate fusion features; and outputting a quality index prediction value of the stomach invigorating and digestion promoting tablets through a regression prediction module based on the fusion features.

The invention discloses a method for adjusting administration dosage according to adverse reaction risk based on a joint model, and is applied to the field of intelligent medical treatment. In the prior art, the relationship between the sirolimus steady-state valley concentration and the specific adverse reaction cannot be quantified, so that the defect of lack of accurate guidance for clinical medication is caused. Through systematic research, the method determines the individual target valley concentration range through a BKMR model for the first time, obtains individual pharmacokinetic parameters of a patient through a group pharmacokinetic model, and finally determines the medication time and the medication dosage of the patient through Bayesian negative feedback simulation, and the medication time and the medication dosage are used for adjusting a medication scheme. According to the invention, prospective risk prediction of adverse reactions of sirolimus is realized, the treatment safety of children with vascular diseases is remarkably improved, and long-term prognosis of patients is improved.

The present disclosure provides a method of creating a label for labeling an intravenous infusion line. Further, the method may include receiving an information associated with one or more intravenous infusion lines from input devices, analyzing the information, determining label content of labels associated with the intravenous infusion lines, determining label content formats of the label content, generating a label information for the labels based on the label content and the label content formats, and printing the label information on label sheets based on the generating of the label information. Further, the printing of the label information creates the labels for the intravenous infusion lines.

The invention discloses a medical drug knowledge RAG optimization method based on a Trie tree, and relates to the field of artificial intelligence and medical drug information processing. The method comprises the following steps: constructing a dual Trie tree knowledge base in the field of medical drugs (a term Trie tree: storing drug / disease / medication attribute terms and ATC / ICD-11 codes; the content Trie tree is used for storing medicine specification hierarchical content and efficacy weight), carrying out preprocessing segmentation on a medical medicine ultra-long text and associating Trie node attributes, and adopting multi-stage retrieval of Trie prefix matching and BioBERT semantic retrieval to recall knowledge blocks; the context (father node-chapter overview, child node-refining rule and brother node-security association information) is expanded based on the hierarchical relationship of the nodes of the Trie tree, finally a response with accurate reference (drug name + ATC code + specification chapter) is generated, and drug conflict is avoided through consistency check. The medical drug knowledge retrieval accuracy and response safety are improved, and the method is suitable for scenes such as intelligent medication consultation and clinical medication aid decision making.

The invention discloses a medical insurance intelligent auditing method and system based on a multi-modal large model, and relates to the technical field of big data processing, and the method comprises the steps: recognizing a material name and a personal name, and auditing the compliance of a material needed for business handling according to the integrity and correctness of the material; identifying first expense detail data according to the expense detail list material and the expense identification cue word; matching the first expense detail data with a medical insurance catalog vector database to obtain a closest catalog entity, and combining the closest catalog entity with the first expense detail data to form second expense detail data; obtaining medicine knowledge from the medicine specification, matching the medicine knowledge with the medical insurance directory vector database, and associating the directory code of the optimal directory entity into the corresponding medicine knowledge; and for each record of the second expense detail data, performing expense rationality auditing according to the corresponding intelligent auditing rule and medicine knowledge. Content acquisition, medical project matching and intelligent auditing of the electronic material are realized, and the auditing speed and accuracy are improved.

The invention relates to the technical field of microfluidic drug delivery, and discloses an oral drug precise delivery and curative effect evaluation system based on microfluidics. The system comprises a signal acquisition and processing module, an instance identification and feature extraction module, and a path generation and curative effect evaluation module. The signal acquisition and processing module continuously acquires multi-dimensional signals of oral drug delivery through the microfluidic sensing array, and the multi-dimensional signals are integrated into a unified data stream after real-time correction and structure standardization. An instance identification module identifies a delivery instance therefrom, extracts a temporal identity, a spatial location descriptor, and a drug classification identifier. The path generation module divides intervals in combination with the time identifiers, aligns the spatial information to the standard oral cavity model, and generates a drug delivery path time sequence record. And the curative effect evaluation module calculates the drug distribution density of the target area according to drug classification aggregation examples, generates a curative effect evaluation data set, and realizes the precision of drug delivery monitoring and curative effect evaluation.

The invention provides a proton dose distribution determination method and device based on a graphics processor, which can be applied to the technical field of medical radiation dose calculation. The method comprises the following steps: calculating pencil beam tracks of a plurality of pencil beams; obtaining respective dose nuclear parameters of the pencil beam at a plurality of intersection points; according to the radiation field dose truncation radius, voxels of interest are determined from tissue voxels in the tissue voxel grid, and the radiation field dose truncation radius represents the upper limit value of the acting distance of all pencil beams in one radiation field to generate remarkable contribution to dose distribution in the transverse direction; calling a plurality of threads to execute dose calculation tasks of the plurality of voxels of interest in parallel based on the respective dose kernel parameters of the plurality of intersection points to obtain voxel deposition doses of the voxels of interest; and determining a dose distribution result related to the specified tissue according to the voxel deposition doses of the plurality of voxels of interest.

The invention provides an anticancer drug collaborative prediction method MultiFusion Syn based on multi-scale feature fusion, and relates to the technical field of anticancer drug collaborative prediction. According to the method, synergistic effect prediction is converted into a multi-scale feature fusion classification task: a pre-trained graph neural network DGCL is utilized to extract drug fine-grained structure features, ChemBERTa is combined to obtain semantic features, and adaptive fusion is carried out through an attention mechanism; meanwhile, a residual network is adopted to extract biological characteristics of the cell line, drug-cell bidirectional interaction is constructed by means of double-end cross attention, and fine fusion is performed through a gating network; the model introduces a pre-training encoder and realizes knowledge migration in combination with parameter freezing. On a reference data set, MultiFusionSyn is evaluated by indexes such as AUC, PRAUC, ACC and BACC, and the performance of MultiFusionSyn is superior to that of an existing advanced method through verification of an independent test set.

The invention relates to the technical field of disease auxiliary diagnosis, in particular to a digital mammary gland artificial intelligence auxiliary diagnosis system based on multi-modal fusion, and the system comprises a multi-modal data collection module which is used for obtaining digital mammary gland image data and rehabilitation scheme data of a patient; the image quality correction module is used for carrying out acquisition quality evaluation and correction on the digital mammary gland image data; the feature modeling and evaluation module is used for performing feature extraction on the corrected digital mammary gland image data and rehabilitation scheme data, and comprises an image-drug action mechanism cooperation unit and an image-rehabilitation bimodal co-learning unit; and the rehabilitation effect prediction module is used for constructing a rehabilitation effect prediction model and outputting a rehabilitation prediction result under the influence of the scheme specificity of the current rehabilitation scheme of the patient. According to the method, the digital mammary gland image and the patient rehabilitation scheme are deeply fused, so that the accuracy and reliability of mammary gland tumor rehabilitation prediction are effectively improved.

A method for computational drug design includes defining a population of a plurality of compounds. Each compound includes one or more molecular properties. The method includes defining a training set of compounds from the population for which one or more biological properties are known. The method includes selecting, from the population, a subset of one or more compounds that are not in the training set. The method includes determining a subset score of the selected subset based on molecular properties of the one or more compounds in the selected subset, and evaluating the selected subset based on the determined subset score. The subset score is determined based on a frequency of the molecular properties in the population and on a frequency of the molecular properties in a sampled set comprising the training set and the selected subset.

The invention provides a drug recommendation method fusing medical record features and risk control, and belongs to the technical field of medical information. Existing drug recommendation depends on external medical knowledge to a great extent, so that a recommendation system is poor in expandability and insufficient in clinical practicability. On the basis, the invention provides a drug recommendation method which does not depend on external medical knowledge and is only based on electronic medical record data. According to the method, an individualized illness state recognition and evolution modeling module is designed, records most representative for the current illness state can be automatically recognized from historical medical records of a patient, the disease course development process is described, and individualized medication preferences and treatment modes are mined. According to the method, semantic features of data are fully utilized, so that joint optimization of recommendation risks and effects is realized under the condition of no external knowledge intervention. Experimental results show that the method is obviously superior to existing mainstream methods in multiple evaluation indexes, and has good accuracy, robustness and clinical application prospects.