3500results about "Drug references" patented technology

The invention relates to the technical field of medicine quality management, in particular to a medicine quality management method and system based on big data and artificial intelligence, and the method comprises the steps: obtaining the quality influence parameters of a medicine product in the production, transportation and storage processes through a multi-modal data collection device; noise is removed through data cleaning, data dimension reduction is carried out through principal component analysis and an auto-encoder, the analysis efficiency is improved, and the efficiency problem of mass data processing of a traditional method is solved. Time sequence analysis is carried out through the long-short-term memory network, abnormal nodes are recognized in time, manual sampling inspection is replaced, and accurate early warning is achieved. The graph neural network constructs a path tracking model, analyzes the relevance between nodes, traces the quality problem, and reckons the batch of affected drugs. Based on a traceability result, an XGBoost model generates quality risk scores of all links by analyzing historical and real-time data and sets a threshold value, and quality risks are early warned in real time.

The invention discloses a ligand information generation model training method and device and a ligand information generation method and device, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring sample receptor information and sample ligand information, wherein the binding affinity between a ligand described by the sample ligand information and a receptor described by the sample receptor information is not less than a set affinity; denoising the reference noise data based on the sample receptor information through a to-be-trained neural network model to obtain predicted ligand information; determining a first loss for characterizing a difference between the sample ligand information and the predicted ligand information; and training the neural network model based on the first loss to obtain a ligand information generation model. The reference ligand information can be generated based on the reference receptor information through the ligand information generation model, and the binding affinity between the ligand described by the reference ligand information and the receptor described by the reference receptor information is high.

The invention relates to a drug-disease relationship prediction method and system based on a dual-channel fusion knowledge graph. The method comprises the following steps: S1, constructing a biomedical fusion knowledge graph; s2, constructing a drug-disease sub-graph based on the biomedicine fusion knowledge graph; s3, constructing a dual-channel adaptive fusion feature module, and embedding a drug-disease sub-graph; s4, performing enhanced splicing on the drug-disease sub-graphs in the dual-channel fusion knowledge graph pre-embedded network, importing an edge-node iterative updating learning mechanism, training a spliced sub-graph relation perception learning network, and obtaining enhanced sub-graph feature embedding; and S5, utilizing the trained enhanced subgraph features to embed, calculate and output the prediction probability of the drug-disease relationship. According to the method, the semantic representation capability of the knowledge graph and the topological modeling advantages of the graph neural network are integrated, and efficient and accurate prediction of the drug-disease relationship is realized through multi-modal feature interaction, sub-graph enhancement and a dynamic sub-graph learning mechanism.

The present invention provides a method and intelligent agent for data collection and drug safety signal mining for PMS, which relates to the field of drug monitoring technology, including standardizing PMS data using a medical text pre-training model, and integrating drug molecular characteristics and clinical medication information to construct a multi-layer drug knowledge graph. A heterogeneous graph neural network with a time series feature extraction function is used to represent the graph, and the drug action mechanism rules are integrated into the training process to obtain a node vector representation of multi-dimensional information fusion. Based on the node vector representation, a multi-task learning framework is constructed to simultaneously complete the drug-adverse reaction association probability prediction and risk level prediction, and combine time series analysis to detect dynamic change characteristics to generate candidate signals. Finally, a hierarchical evaluation system is constructed to verify the candidate signal, and its reliability is confirmed in combination with clinical data, the target signal is output, the warning level is determined, and differentiated monitoring cycles and trigger thresholds are set to perform real-time risk warnings.

A knowledge graph-based method for recommending traditional Chinese medicine (TCM) prescriptions is provided. The method integrates a knowledge graph embedding model, a multi-head attention mechanism, graph convolutions, and other techniques to combine graph features with a recommendation system, for comprehensively considering patient conditions to recommend TCM prescription. Taking TCM case description texts as the research object and integrating with TCM knowledge graph information, the method draws from the clinical experience of renowned TCM experts and fully considers various individual factors such as medicinal properties, efficacy, medical conditions, and patient constitutions. Based on the holistic principles of TCM and the ideology of syndrome differentiation and treatment, the method selects different herbal combinations based on varying symptoms and medical conditions of patients. The proposed TCM prescription recommendation method, incorporating the knowledge graph, considers the complex relationships between individual signs as well as symptoms and medications.

The invention discloses a patient medication risk early warning method, and relates to the technical field of medication risk evaluation. The method comprises the following steps: acquiring multi-dimensional data such as age, past medical history, allergy history, clinical examination data and prescription data of a patient, extracting chronic disease diagnosis information from the multi-dimensional data, and carrying out standardized coding to generate a common disease combination tag; calling a preset disease weight system, and calculating a common disease severity score in combination with an age factor; extracting metabolism-related indexes to construct a drug metabolism capability feature vector of the patient; mapping the drug combination, the co-disease tag and the metabolic feature vector to a clinical drug knowledge base to generate a comprehensive conflict detection result; and generating corresponding early warning information based on the conflict detection result and a preset early warning rule base, wherein the rule base comprises a mapping relationship between the conflict type and the early warning level. According to the method, drug use risk early warning can be realized according to the multi-disease coexistence state of the patient, and the drug use safety is improved.

The invention relates to the technical field of cardiovascular disease treatment, and discloses a cardiovascular disease medical decision support method based on a large model, and the method comprises the steps: obtaining multi-modal cardiovascular disease medical data, constructing a multi-source heterogeneous data set, carrying out the preprocessing and feature fusion, and constructing a combined representation model based on the pre-training large model and the fusion of traditional and western medicine knowledge. And designing a hierarchical space-time diagram convolutional network to generate a dynamic physiological state code of the patient, and constructing a multi-target optimization decision tree to generate a treatment scheme containing western medicines, traditional Chinese medicine prescriptions and non-drug intervention. A federal learning framework is adopted to cooperatively train a local model, data security is guaranteed, a global model is optimized, and a curative effect attribution module is further arranged to correct decision deviation. According to the method, multi-modal data and traditional Chinese and western medicine knowledge are integrated, personalized treatment scheme generation is achieved, data safety is guaranteed, and the cardiovascular disease diagnosis and treatment level is improved.

The present disclosure provides devices, systems, and methods for capturing and analyzing behavioral and physiological data of subjects for treatment, modification, and manipulation discovery. In some embodiments, a method for classifying a drug is provided. The method includes obtaining observational data concerning an animal subject to which the drug is administered, the observational data acquired using an enclosure for the animal subject, the enclosure instrumented with at least one sensing device. The method also includes extracting features by applying the observational data to a machine-learning feature-extraction component. The method further includes predicting a class label of the drug by applying the features to a machine-learning classifier component, the machine-learning classifier component trained to predict the class label of the drug from, at least in part, the features. The method further includes providing an indication of the class label.

The invention belongs to the technical field of medical resource intelligent management, and relates to a hospital comprehensive monitoring analysis method and platform based on artificial intelligence, and the method comprises the following steps: generating a medicine management basic data set; generating a primary prediction signal; adjusting a prediction weight according to the conflict level and outputting a reliable demand prediction instruction; a quantitative evaluation value reflecting the medicine turnover efficiency, the immediate pressure and the cost loss is calculated, and when the quantitative evaluation value exceeds a preset safety alert threshold value, an inventory abnormity alarm signal is generated; outputting an allocation strategy instruction containing the medicine flow direction, the allocation quantity and the time efficiency; and comparing a deviation value between the reliable demand prediction instruction and the allocation strategy instruction, and when the deviation value continuously exceeds a preset deviation threshold value, triggering an update prompt of the drug safety use limit rule base. The problem that demand prediction and clinical rule verification are separated in a traditional mode is solved.

A computer-implemented method of training a machine learning model to identify biological entities for drug discovery is disclosed. The method comprises providing a training data set comprising a plurality of entity-linked text sequences, each text sequence including a mention of a biological entity, where the biological entity is linked to a corresponding biological entity identifier from a set of possible biological entity identifiers; masking the mention of the biological entity within each text sequence; encoding each masked text sequence into an input representation for a machine learning model; and training a machine learning model to predict the unique entity identifier of the masked biological entity based on the input representation. The described method is able to utilise the full breadth of the rich contextual information available in the biomedical text corpus to predict new biological targets for drug discovery and avoids the restrictions intrinsic to relationship prediction using knowledge graphs. The ability to identify more promising, biologically relevant targets in an automated manner, significantly reduces the requirement of human input and reduces the failure rate in targets that are progressed in the drug delivery pipeline.

Methods and systems for selecting a deep learning / machine learning model are described. In one embodiment, a plurality of models are optimized by addressing class imbalances among a first subset of records, each of the plurality of models are trained using the first subset of records, each of the plurality of models are implemented to predict a value of respective known target columns in each of a second subset of records, respective success rates for each of the plurality of models at predicting the respective known target columns for each of the second subset of records are determined, a first of the plurality of models having a highest success rate is selected, and the first of the plurality of models is implemented to decide a target column of a received authorization request based on predictor columns in the received authorization request.

Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and / or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and / or to track treatment and / or the progress of a disease.

Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and / or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and / or to track treatment and / or the progress of a disease.

The invention provides a drug-disease association prediction method and system based on heterogeneous node sequence representation, and belongs to the technical field of drug discovery in bioinformatics. Constructing a drug similarity network and a disease similarity network; constructing a heterogeneous biological interaction network by using the drug similarity network and the disease similarity network, and obtaining a first-order neighbor subgraph of the target node; splitting the first-order neighbor subgraph of the target node into a plurality of meta-relation bigraphs according to the meta-relation type, performing intra-domain message propagation and aggregation in each meta-relation bigraph, and updating the semantic features of the target node; fusing the semantic features of the target nodes in a cross-domain manner, updating the sequence features of the target nodes, and aggregating the features; obtaining vector representation of a target node through a multi-head self-attention mechanism by using the updated sequence features; and inputting the vector representations of the drug nodes and the disease nodes into a multi-layer perceptron to predict the drug-disease association probability. Semantic information confusion is effectively avoided, and then the accuracy of model prediction is improved.

The invention provides a method for drug screening and disease model construction of liver organoid, relates to the technical field of medical research, and adopts multi-modal data acquisition, fusion and intelligent analysis and quantum computing processing to optimize comprehensive application of data, so that the richness, accuracy and analysis efficiency of the data are remarkably improved, and the risk of drug screening and disease model construction of liver organoid is reduced. Multi-source data of genomics, transcriptomics and the like are collected comprehensively, an accurate research system of immune-hepatic disease interaction based on organoid is constructed, an in-vivo complex immune-hepatic disease microenvironment can be highly simulated, the proportion and activation state of immune cells are accurately regulated and controlled in an in-vitro model, functional and phenotypic changes of the immune cells are monitored in real time, and the immune-hepatic disease interaction accuracy is improved. According to the present invention, the interaction mechanism between the immune cells and the liver organ cells is deeply revealed, such that the accurate information basis is provided for the accurate determination of the drug action target, the development of the innovative drug for the immune check point or tumor microenvironment regulation is easily achieved, and the pertinence and the success rate of the drug research and development are substantially improved.

The invention provides a medical AI question and answer method based on retrieval enhancement, which comprises the following steps: acquiring a medical professional knowledge base, and constructing an entity recognition model and a medical knowledge graph; the method comprises the following steps: splitting a medical professional knowledge base, and creating a vector index and a full-text retrieval reverse index; intention classification is carried out, and initial processing is completed; performing multi-path mixed retrieval on the medical professional knowledge base to obtain a user question related text block; a filtering rule is preset, and the user question related text blocks are filtered; answering the user question according to a preset second prompt word, and answering the user question according to a preset third prompt word when the user question exceeds the range of the medical professional knowledge base or the multi-path mixed retrieval has no result. According to the method, related context information can be accurately searched, reliable reference information is provided for a generative large model to answer professional questions, the interactive experience of a medical question answering system is improved, and the accuracy and specialty of answer generation of the system are remarkably improved.

The invention relates to the technical field of physical sign monitoring, in particular to a physical sign parameter monitoring method accompanying the medication process of a severe mental disease patient. According to the method, the dynamic change of the physical sign parameters is captured by acquiring the physical sign parameter time sequence curves of the to-be-monitored patient in the two stages before and after medication. For each time sequence curve, data value differences and fluctuation duration are analyzed, and abnormal fluctuation data segments are identified. Furthermore, a historical normal time sequence curve is utilized to determine a historical normal fluctuation section corresponding to the abnormal fluctuation, and the overall state of the physical sign parameters is visually reflected through comparative analysis and calculation of a stable index. Meanwhile, the physical sign condition of the patient is monitored by combining the stability index of the same physical sign parameter before and after medication and the time sequence characteristics of the abnormal fluctuation data segment. According to the method, the physical sign state of the patient is reflected more accurately by comparing the change of the physical sign parameters of the patient with the historical normal condition, and data support with higher credibility is provided for doctors to carry out medicine effect judgment or illness state analysis.

The invention relates to the technical field of tumor treatment, in particular to an intelligent decision-making system for tumor precise targeted medication based on individual mutation information, which comprises a data processing layer, a variation annotation and function prediction layer, a knowledge base integration layer and a scheme decision-making engine and report visualization module. According to the intelligent decision-making system for tumor precise targeted medication based on individual mutation information, a rule engine and a prediction model are combined, dynamic priority ranking is output, multi-model fusion decision making is achieved, and clinical scene deep adaptation is achieved by predicting primary and secondary drug resistance, calculating liver and kidney function adjusting dosage and generating a combined medication time sequence scheme; through an individualized drug delivery scheme, combination drug use optimization is achieved, a visual clinical report is generated, clinical executable operation is further strengthened, and through algorithm quantification, a dynamic knowledge graph, AI auxiliary decision making and a clinical operation closed loop, the next-generation technical research direction of a tumor precise drug use system can be represented.

The invention relates to the technical field of medical information, in particular to a hospital pharmacy dispensing information intelligent processing method and system, and the method comprises the following steps: S1, obtaining the basic data of a medicine in real time; s2, acquiring medical record information, current medication conditions and physiological parameters of the patient from a hospital information system; s3, analyzing an existing drug combination, and detecting potential drug incompatibility; s4, a medication scheme of the patient is automatically generated; s5, pushing the medication scheme and the taboo information generated in the step S4 to work terminals of doctors and pharmacists, and providing medication suggestions at the same time; and S6, continuously monitoring the reaction of the patient, and when abnormity is monitored, performing drug adjustment through an adjustment algorithm. According to the system and the method, the medicine data is updated in real time, the patient information is integrated, and incompatibility identification and medication adjustment are carried out by utilizing an intelligent algorithm, so that the safety and the efficiency of hospital pharmaceutical dispensing are remarkably improved, and meanwhile, the individuation and the scientificity of a treatment scheme are optimized.

The invention discloses a traditional Chinese medicine clinical research scheme generation method and system, and relates to the field of medicine. The method comprises the following steps: aiming at traditional Chinese medicine clinical research, constructing a knowledge graph covering evidence-based evidence elements PICO; establishing a traditional Chinese medicine cue word project and an element template based on evidence-based evidence elements PICO covered by the knowledge graph; identifying element information selected in the element template by adopting a traditional Chinese medicine cue word project, and generating a cue word feature vector; retrieving data matched with the cue word feature vector from the knowledge graph by adopting a trained language model; when the feature in the cue word feature vector cannot retrieve the matched data from the knowledge graph, automatically generating the matched data by using the trained language model; and integrating the matching data automatically generated by the trained language model and the matching data retrieved from the knowledge graph to obtain a traditional Chinese medicine clinical research scheme. The preparation efficiency and quality of the traditional Chinese medicine clinical research scheme can be effectively improved.

The invention relates to the technical field of drug concentration detection, and relates to a clinical pharmaceutical blood concentration detection method, which comprises the following steps: capturing drug molecules in a blood sample based on a micro-fluidic chip, enabling a sensor to detect the drug molecules and combine with the surface of the drug molecules, and collecting high-sensitivity blood reaction data. According to the method, main variables in data are identified and utilized by adopting a principal component analysis method, so that the complexity of spectral data is effectively reduced, meanwhile, the most critical information for calculating the drug concentration is reserved, the data processing flow is optimized, the analysis speed and efficiency are improved, and the prediction of the drug concentration is more accurate; according to the method, more reliable data support is provided for clinic, the data is smoothed through the moving average algorithm, random fluctuation caused by sampling errors or external variables is reduced, the overall quality and credibility of the data are improved, errors can be obviously reduced, and therefore doctors are supported to conduct more accurate medicine dosage adjustment and curative effect evaluation.

Methods, apparatus, systems, and articles of manufacture are disclosed for generation and application of models for therapeutic prediction and processing. A balance of precision and recall can be applied to at least one of a toxicity-related model or an efficacy-related model to configure immunotherapy treatment of a patient and / or cohort of patients. Model output can be evaluated differently depending on a determine patient selection criterion to trigger different actions.

The invention relates to the technical field of medical information, in particular to an esophageal cancer multi-mode treatment effect comprehensive evaluation system which comprises a patient data acquisition module, a drug resistance dynamic monitoring and adjusting module, an immune system response dynamic analysis module and a treatment effect evaluation module. The patient data acquisition module is used for acquiring multi-modal health data of a patient; the drug resistance dynamic monitoring and adjusting module analyzes drug resistance signs appearing in the treatment process; the immune system response dynamic analysis module comprehensively evaluates the state change of the immune system; and the treatment effect evaluation module evaluates the treatment effect of the patient in real time based on the drug resistance and immune system response analysis result, and provides treatment adjustment suggestions for the doctor according to the evaluation result. According to the invention, the accuracy of treatment is improved, the risk of treatment failure is reduced, and decision support based on data is provided for doctors.

The invention discloses a multi-modal fusion-based traditional Chinese medicine recommendation method, system and device and a medium, and the method comprises the steps: constructing a heterogeneous graph according to a core representative object and preprocessed traditional Chinese medicine multi-modal data; inputting the heterogeneous graph into a graph neural network for data fusion to obtain traditional Chinese medicine multi-modal fusion data; constructing a knowledge graph based on the traditional Chinese medicine multi-modal fusion data to obtain a traditional Chinese medicine knowledge graph; segmenting the traditional Chinese medicine knowledge graph into sub-graphs processed by multiple agents according to the drug effect and the drug use safety, and obtaining a medicinal material sub-graph associated with a drug effect agent and a safety sub-graph associated with a safety agent; based on the medicinal material sub-graph and the safety sub-graph, collaborative iteration optimization is carried out through a medicinal material combination strategy and a medicinal material safety checking strategy to select medicinal materials, and an optimal compound recommended by the traditional Chinese medicine is obtained; and inputting the optimal compound into the fine-tuned large language model, and determining a target compound recommended by the traditional Chinese medicine. The accuracy of traditional Chinese medicine recommendation can be improved.

The invention discloses a construction method of a traditional Chinese medicine alert question-answering system based on a large language model. The method comprises the following steps: establishing a traditional Chinese medicine knowledge base; constructing a retrieval enhancement generation model PV-RAG oriented to drug alert, and training and optimizing the PV-RAG model by taking the traditional Chinese medicine knowledge base as a training set; the PV-RAG model comprises a retrieval module, a text extraction module, a query rewriting module and a generation module; and constructing an intelligent question and answer application end, and generating a traditional Chinese medicine alert answer according to a natural language question input by a user. According to the traditional Chinese medicine alert question-answering system based on the large language model and the retrieval enhancement generation technology, deep semantic understanding of traditional Chinese medicine literatures is achieved through the text embedding model, multi-source data such as traditional Chinese medicine specifications, traditional Chinese medicine classics and clinical research literatures are effectively integrated, and the traditional Chinese medicine alert question-answering system is established. And rich knowledge support is provided for traditional Chinese medicine alert questions and answers.

The invention discloses an optimization analysis system for simulating an anesthesia process, relates to the technical field of anesthesia simulation optimization analysis, and is used for solving the problem of inaccurate anesthesia individualized simulation decision. By constructing an anesthesia simulation system fusing a pharmacokinetic and pharmacodynamic model and a physiological coupling model, dynamic process simulation under individual physiological states and operative characteristics is realized, state evolution trajectories of an induction period, a maintenance period and a resuscitation period can be accurately deduced, and by introducing deep learning calculation and stage continuity constraints, the accuracy of the state evolution trajectories of the induction period, the maintenance period and the resuscitation period is improved. The method comprises the following steps of: establishing a multi-path drug delivery and ventilation strategy, setting a risk-efficiency scoring function, completing state tracking and index evaluation of the whole process, generating a strategy scoring set with confidence information, further supporting an abnormal probability judgment and risk triggering mechanism by the system, and outputting key monitoring parameters and coping strategies, so as to improve the simulation accuracy and stability; therefore, closed-loop control of strategy optimization and risk early warning is realized, and the credibility of the anaesthesia simulation process is improved.

The invention provides a chronic disease co-patient accurate medication real-time management system based on an AI large model, and the system comprises accurate medication guided by the AI large model, through the mathematical modeling and simulation technology, the related information of patients, medicines and diseases is integrated, and accurate medication is guided. According to the method, the administration scheme can be formulated more accurately, the curative effect is improved, side effects are reduced, a patient medication report is provided for physicians and pharmacists, the physicians and pharmacists can dynamically master patient medication information in real time, reference is provided for the physicians to formulate the next medication scheme, and the treatment effect on chronic disease patients is improved.

The invention provides an integrated dynamic drug metabolism monitoring and early warning system, and relates to the technical field of drug metabolism monitoring and early warning, the integrated dynamic drug metabolism monitoring and early warning system comprises a data acquisition module, a data processing module, a model construction module, an early warning mechanism module and a user interaction module, the data acquisition module is composed of an optical sensor, a chemical sensor and an electrophysiological sensor, according to the system, the concentration of drugs and related physiological parameters are monitored in real time, different types of biological signals are captured, and the sensitivity and selectivity of the sensors are considered in the design of the data acquisition module so as to ensure the stability and accuracy in different physiological states. Drug concentration and physiological parameters are monitored in real time from multiple dimensions, sensors select and cooperate to form an efficient acquisition network according to performance expression, data quality is optimized in combination with a multi-source data fusion algorithm (such as Kalman filtering and a weighted average method), and a data processing module adopts a statistical analysis algorithm to clean standardized data.

The invention discloses a tumor patient medication side effect management method and system based on big data analysis, and the method comprises the following steps: collecting and preprocessing patient data and drug data, and extracting patient features and drug features, the patient data comprising genome data provided by a patient; constructing a prediction model which comprises a time sequence analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model and is used for predicting risk scores of different types of side effects of the patient by taking the drug characteristics and the patient characteristics as input of the prediction model; inputting the basic information, the medication information and the genome data of the current patient into the trained prediction model to obtain risk assessment of various side effects of the current patient; according to the risk assessment result, personalized adjustment suggestions suitable for the patient are selected from a preset intervention scheme library and provided for the current patient. The scientificity and accuracy of administration side effect management in the treatment stage can be effectively improved.

The invention discloses a drug use dynamic risk assessment method and system for an elderly patient, and relates to the technical field of drug use risk assessment, and the method comprises the steps: collecting static factor data and dynamic factor data of the elderly patient, carrying out the preprocessing of the collected static factor data and dynamic factor data, and generating a feature vector for risk assessment; calculating a comprehensive risk score through a preset evaluation rule based on the feature vector; the comprehensive risk score is compared with a preset risk grading threshold value, the medication compliance risk grade of the elderly patient is determined, and the system comprises a data collection module, a preprocessing module, a risk assessment module and a grading module. The comprehensiveness and timeliness of medication compliance risk assessment of elderly patients are improved, and technical support is provided for medication intervention.