59 results about "Biomedical data" patented technology

The invention discloses a biomedical data analysis method based on adaptive multi-modal data fusion, and belongs to the technical field of multi-modal feature data processing. The method comprises the following steps: acquiring multi-modal data, and preprocessing the multi-modal data; extracting the preprocessed multi-modal data by adopting a modal specific neural network model; performing primary fusion on each extracted modal feature to generate a primary fusion feature; introducing an adaptive fusion module based on an attention mechanism to obtain final fusion features; and performing classification prediction on the final fusion features by using an FCNN model to complete classification of the multi-modal data. According to the invention, through dual mechanisms of primary average fusion and adaptive fusion, high-precision identification is maintained under the condition of multi-modal data missing, and performance reduction caused by incomplete data is avoided; meanwhile, the modal weight is dynamically adjusted through an attention mechanism, the fusion process can be optimized according to the quality and integrity of each modal data, and the feature expression ability is remarkably improved.

Apparatus for identification of abnormal biomedical features within images of biomedical data and methods used therein are described. The apparatus includes an image capture device, a processor connected to the image capture device, a memory connected to the processor, and a display device connected to the processor. The image capture device is configured to capture an image of biomedical data. The memory contains instructions configuring the processor to receive the image, extract a plurality of biomedical features from the biomedical data, receive repository data from a medical repository as a function of the plurality of biomedical features, generate at least a distance metric as a function of the plurality of biomedical features and the repository data, and highlight at least a biomedical feature within the image as a function of the at least a distance metric.

The invention relates to the technical field of biomedical data analysis, and discloses a data analysis method and system for genetic disease gene detection and a storage medium, and the method comprises the following steps: collecting a patient sample, and carrying out high-throughput sequencing to obtain original data; performing quality control and comparison processing on the data to generate variation detection data, and calculating variation information amount; suspicious variation sites are automatically screened, variation information amount is analyzed based on information entropy, and key variation is screened according to a preset threshold; performing Bayesian inference analysis on the key variation, calculating pathogenicity probability, and performing pathogenicity judgment based on an ACMG standard; calculating the matching degree of the key variation and the phenotype in combination with the phenotype information of the patient, and screening the variation conforming to phenotype characteristics; submitting the screening result to a doctor for auditing, and generating a final screening result; and generating a standardized gene detection report according to a final result. According to the method, pathogenic variation is efficiently and accurately recognized, and the automation level and clinical application value of genetic disease gene detection are improved.

The present disclosure discloses a method and system for constructing a knowledge graph of a standard data element of a biomedical dataset, comprising collecting relevant standard texts of data elements of different types of biomedical datasets and data of a relevant standard of the biomedical dataset; analyzing and summarizing the relevant standard texts of the data elements of the different types of biomedical datasets and the data of the relevant standard of the biomedical dataset; constructing a knowledge model of the knowledge graph of the standard data element of the biomedical dataset; extracting entity type data and attribute data from structured data and an unstructured text in the structured data; and obtaining the knowledge graph of the standard data element of the biomedical dataset by performing knowledge fusion on a plurality of types of data based on an a plurality of types of semantic associative relationships between one or more entity types.

The invention discloses a miRNA and disease relation prediction system and method based on causal feature selection. According to the system, the accuracy of miRNA and disease relation prediction is improved through a plurality of modules such as data acquisition, negative sample selection, feature selection and causal analysis, model training and evaluation and the like. The data acquisition module is used for acquiring miRNA expression data and disease characteristics from a biomedical database; the negative sample selection module is used for grouping unknown miRNA-disease relationships through k-means clustering and randomly selecting negative samples; the feature selection module adopts a causal feature selection algorithm to construct a Markov blanket so as to screen out miRNA features directly related to disease states, and redundancy is reduced; the model training module uses a single machine learning algorithm for training, and optimizes the model performance through cross validation; and finally, the evaluation module evaluates the model effect through indexes such as AUROC and AUPR. According to the method, the accuracy of miRNA and disease relation prediction is remarkably improved, and important support is provided for medical research.

The embodiment of the invention relates to a cognitive competence assessment processing method and device based on multi-modal data. The method comprises the following steps: setting two-stage data typing rules and typing feature coding rules of the multi-modal data; setting a corresponding score-classification relation table for the fluid intelligence scoring indexes of the two-stage data classification detailed rule; carrying out original data collection on the open biomedical database according to the two-stage data classification detailed rule, and carrying out training data arrangement according to the classification feature coding detailed rule, the score-classification relation table and the original data set; constructing a cognitive ability prediction model based on a LightGBM model; performing first-stage hyper-parameter optimization and full-data training on the cognitive competence prediction model, and then performing second-stage fault-tolerant training on the cognitive competence prediction model; and receiving current individual data after the two-stage training is finished, and performing cognitive ability evaluation and feature importance analysis based on the current individual data and the cognitive ability prediction model. According to the invention, the cognitive ability evaluation accuracy can be improved.

The invention provides a biomedical causal relationship inference method and system based on a Bayesian knowledge graph, and relates to the technical field of biomedical data mining and artificial intelligence, and the method comprises the steps: carrying out the multi-source evidence fusion of biomedical data, and obtaining a structured triple containing Bayesian confidence; analyzing the triple by using priori knowledge and obtaining a conditional probability table through parameterized filling; carrying out posteriori updating by using the Bayesian theorem; and analyzing the updated knowledge graph state by using a graph neural network model to obtain an inference result. Wherein the Bayesian inference module is combined with the graph neural network model, the former provides priori knowledge with confidence, the latter provides a fine path dependency relationship, and the accuracy and robustness of inference are remarkably improved. According to the method, the problems of evidence isomerism fragmentation, causal inference subjectization and knowledge discovery inefficiency are solved, and intelligent and automatic inference of the causal relationship is realized.

The invention provides a traditional Chinese medicine ancient book association construction method and device, equipment and a storage medium, and the method comprises the steps: converting a traditional Chinese medicine ancient book into an editable digital text, obtaining a standardized expression text of the traditional Chinese medicine ancient book, obtaining a semantic relationship and context information of the traditional Chinese medicine ancient book, and determining a relationship between key entities of the traditional Chinese medicine ancient book, constructing a knowledge graph in the traditional Chinese medicine ancient books; the method comprises the following steps: based on a knowledge graph in a traditional Chinese medicine ancient book, obtaining an association relationship of key entities of different traditional Chinese medicine ancient books, completing association construction of different traditional Chinese medicine ancient books, extracting disease and symptom characteristics in modern biomedical data, matching corresponding traditional Chinese medicine ancient book contents, and realizing association construction of the disease and symptom characteristics and the traditional Chinese medicine ancient book contents. According to the application, through association construction among different traditional Chinese medicine ancient books, in-depth analysis of traditional Chinese medicine knowledge is realized, and content in the traditional Chinese medicine ancient books is associated and matched with disease characteristics in modern biomedical data, so that the traditional Chinese medicine ancient books play an important role again.

Computing devices and methods for generating biomedical research hypotheses are provided. A method for generating biomedical research hypotheses includes iteratively performing the following steps through a plurality of iteration rounds: invoking, by a first agent, a first model to obtain research keywords and / or research topics; calling, by the second agent, the second model to obtain a biological research discovery; calling, by a third agent, a third model to generate a predetermined number of candidate biomedical research hypotheses based on the biomedical research background and the biomedical research discovery, the third model being determined from a large language model library based on a pre-constructed biomedical data set; and calling, by a fourth agent, a fourth model to perform hypothesis evaluation to determine the biomedical research hypothesis.

Embodiments described herein provide a data cloud platform that facilitates an end-to-end workflow for scientific and biomedical research, such that scientists and researchers may have easy access to scientific and biomedical data.

This application discloses a method, apparatus, device, and product for constructing a multidimensional knowledge graph, applicable to the field of data processing technology. The method includes: acquiring at least two biomedical databases, wherein the biomedical databases store different entities and entity relationships connecting the different entities; standardizing similar entities in the at least two biomedical databases to obtain at least two standardized entities; reconstructing entity relationships between different standardized entities based on the entity relationships between different entities in the at least two biomedical databases; and constructing the multidimensional knowledge graph based on the at least two standardized entities and the entity relationships between different standardized entities. This method can construct a multidimensional knowledge graph, primarily based on gene-related entities, by integrating databases.

The invention provides a drug relocation interpretation text generation method and device, equipment and a medium, and relates to the technical field of drug relocation, and the method comprises the following steps: extracting structural features from biomedical data, and carrying out graph construction through the structural features and a relationship set to obtain a medical unified graph; performing global and local graph processing on the medical unified graph, and according to global features and local features, optimizing a network structure through mutual information maximization and calculating an association score between the medicine and the disease through an inner product to obtain a medicine-disease relationship prediction result; performing path search processing according to a breadth-first search algorithm and the drug-disease relationship prediction result, and calculating a search path entity and relationship embedded aggregation matching score to obtain an interpretation path prediction result; and constructing a language model, and inputting the interpretation path prediction result into the language model for text conversion to generate a relocation interpretation text. The problems that prediction logic and mechanism are difficult to understand visually, and the reliability of a decision made by prediction data is affected are solved.

The invention discloses a multi-modal biomedical data security fusion query treatment method and system. The method comprises the following steps: receiving a fusion query request; legality verification is carried out through the block chain smart contract; decomposing the ontology model based on the multi-modal metadata into sub-query tasks and distributing the sub-query tasks; each data holder node generates an intermediate result identified by a unified pseudonym identifier in a local privacy protection computing environment; executing data alignment and aggregation operation of privacy protection; and returning a fusion result and recording the key event in the block chain smart contract. According to the method, integrated treatment with data availability and invisibility, flexible query, process auditing and contribution incentive is achieved, and the method is suitable for safety collaborative analysis of multi-mode biomedical data such as genomes, images and electronic medical records.

This invention discloses a method, system, device, and storage medium for predicting drug-disease associations based on cross-propagation fusion and diffusion-guided multi-scale Transformer. The method acquires multi-source drug similarity, disease similarity, and known drug-disease association data; fuses multi-source similarity networks through local neighborhood sparsification and bidirectional cross-propagation; introduces noise based on the potential diffusion process and learns denoising representations; models the local-global interaction relationship between drugs and diseases using a multi-scale Transformer encoder; and finally outputs a drug-disease association probability score for ranking candidate treatment associations. This invention can improve the robustness and predictive performance of drug-disease association prediction under multi-source biomedical data and can be used for prioritizing drug relocation candidates.

The present invention belongs to the field of three-dimensional visualization technology and discloses a three-dimensional visualization method for a biomedical platform data map. The method includes installing SQL Server in the biomedical platform; constructing a biomedical database through SQL services; collecting biomedical product data and production and sales geographic data through a vertical search engine; transmitting the data to the platform in the form of a data stream; cleaning the collected medical data and storing it in the biomedical database; constructing a biomedical platform map canvas through a map construction program; constructing a map model on the map canvas, and displaying the map model in association with the biomedical data through key geographic information in the biomedical data; and constructing a biomedical knowledge graph, and associating the knowledge graph with the map model through a vector file. The present invention can reduce the number of displayed tables by constructing a biomedical platform map canvas through a map construction program, thereby improving the efficiency of constructing data maps.

Interactive Biomedical Data Analytics and Visualization Computer Interface

Apparatus for identification of abnormal biomedical features within images of biomedical data and methods used therein are described. The apparatus includes an image capture device, a processor connected to the image capture device, a memory connected to the processor, and a display device connected to the processor. The image capture device is configured to capture an image of biomedical data. The memory contains instructions configuring the processor to receive the image, extract a plurality of biomedical features from the biomedical data, receive repository data from a medical repository as a function of the plurality of biomedical features, generate at least a distance metric as a function of the plurality of biomedical features and the repository data, and highlight at least a biomedical feature within the image as a function of the at least a distance metric.

The invention belongs to the technical field of data collaborative computing, and discloses a federated learning-based cross-institution biological medicine data collaborative computing method and system. Comprising the steps of performing statistical modeling on biological medicine data of each medical institution, and constructing a data portrait of each medical institution; local training hyper-parameters of the medical institutions are generated in a self-adaptive mode according to the data portraits, local dynamic training is executed on a pre-constructed global model, and local model parameter increments of the medical institutions are calculated; quantitatively evaluating the contribution index of each medical institution, and carrying out differentiated distribution on the computing resource quota and the training weight quota of each medical institution; fairly aggregating local model parameter increments of the medical institutions, and optimizing a global model; according to the method, a federal learning ecosystem which has competitive vitality and keeps cooperative balance is constructed, a feasible technical scheme is provided for safety sharing and value mining of cross-institution biological medicine data, and the dilemma of data islands is effectively broken through.

User Interface Design for Biomedical Data Visualization and Analysis

Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

System (100) and method (200) for aiding drug development by determining a curable action value of a target protein are disclosed. The system (100) comprises a protein data extraction module (110), a protein data filtration module (120), and a final expression level calculator (150). The protein data extraction module (110) is configured for parsing and identifying information related to target proteins from a public database. The public database comprises data related to proteins. The protein data filtration module (120) is configured for filtering out irrelevant information from the identified information related to the target proteins. The final expression level calculator (150) is configured for calculating the final expression level of the identified relevant information. The curable action of the target protein is based upon the final expression level. The invention aids in drug development by determining the curable action of the protein in the disease based on biomedical data corpus.

The invention discloses an AI-driven traditional Chinese medicine-glioma target point synergistic effect network prediction system, which comprises a multi-mode biomedical data acquisition and preprocessing module, an AI-driven traditional Chinese medicine-glioma target point network prediction module and a target point prediction module, the data acquisition module is used for acquiring heterogeneous biomedical data associated with traditional Chinese medicine components and biological activity thereof, glioma related targets and molecular characteristics thereof, and traditional Chinese medicine-target interaction from a plurality of data sources; and the multi-level biological network construction module is electrically connected with the multi-modal biomedical data acquisition and preprocessing module. The invention relates to the technical field of bioinformatics. According to the AI-driven traditional Chinese medicine-glioma target point synergistic effect network prediction system, massive heterogeneous biomedical data are effectively integrated and standardized through the multi-mode biomedical data acquisition and preprocessing module, the defects that a data source is single and complex association is difficult to capture in a traditional method are overcome, and the multi-level biological network construction module has the advantages of high efficiency, high reliability and the like. A multi-dimensional and multi-scale biological network is constructed from the perspective of system biology.

This application relates to the field of natural language processing technology, and in particular to a biomedical information extraction method based on a large language model. The method includes: acquiring the biomedical dataset to be processed and performing standardized preprocessing; converting the relation extraction data into high-dimensional vectors and constructing a local vector library; acquiring the medical information text to be processed as the query text, and performing a two-stage example retrieval and filtering in the local vector library to obtain a high-quality example set; generating context examples and performing context learning to understand the current task requirements and generate the information extraction results of the query text; and parsing the information extraction results. Based on the reordering capability of the cross-encoder model, this application designs a two-stage retrieval and reordering mechanism. By ensuring that the ICL examples provided to the large language model have both high semantic relevance and high task guidance, it significantly improves the accuracy and robustness of the model in biomedical named entity recognition and relation extraction tasks.

The invention relates to the field of biomedical data processing, in particular to a method for realizing functional near-infrared artifact removal by an auto-encoder based on long and short term memory, which is used for processing original near-infrared signals in three stages: 1) extracting morphological characteristics of the original near-infrared signals through a down-sampling module; 2) a long and short term memory module captures time correlation between single samples to enhance features; and 3) the up-sampling module recovers and reconstructs the morphological characteristic information of the near-infrared signal from the potential space. And finally, outputting the reconstructed pure near-infrared signal at an output layer. According to the method, the defect that a traditional motion artifact removal method for signal processing excessively depends on professional knowledge and optimal selection of model parameters of a brain area is overcome, full-automatic removal of the motion artifacts is achieved, and the result shows that the method obtains good denoising results on different data sets.

The invention discloses a biomedical knowledge graph construction method, which belongs to the technical field of knowledge graphs, and comprises the following steps: S1, data preprocessing: classifying and grading multi-source biomedical data from a literature database, an electronic medical record, genome data and a medical image, and desensitizing sensitive information through a differential privacy technology; s2, knowledge extraction and annotation: using a BERT-BiLSTM-CRF-based joint model to extract entities, combining an attention mechanism to identify relationships between the entities, and adopting an ontology framework to perform semantic annotation; s3, cross-source alignment and fusion: calculating entity similarity based on a graph embedding algorithm, and integrating entities and relationships in different data sources through a probability soft alignment model; and S4, a quality control system: constructing a three-level verification mechanism comprising a rule engine, statistical analysis and expert feedback, and dynamically monitoring the integrity, consistency and timeliness of the knowledge graph.

The invention relates to the technical field of biomedical data management, in particular to a biological big data storage and management system and method. Multi-source biological data is stored in a distributed mode through a redundancy mechanism, an index tag is set for each storage unit, the data level of each piece of biological sub-data is set based on the biological sub-data, then the access level of an access user is set based on identity information of the access user, and target data is determined according to the index tag input by the access user; and if the access level of the access user is greater than or equal to the data level of the target data and the environmental data of the target data accords with an environmental data threshold value, calling and displaying the target data. Therefore, an access user with a lower access level cannot view the biological data with a high data level, so that the safety of the biological data is improved, and the biological data is prevented from being leaked.

An apparatus for tracking cardiac indices containing a processor configured to receive cardiac input data from a patient including a plurality of cardiac signals, input the cardiac input data into a cardiac panel, the cardiac panel including a plurality of cardiac models, and generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the at least one cardiac machine learning model, wherein at least one cardiac index of the one or more cardiac indices includes a probability of the patient satisfying at least one grading threshold.

This application relates to the field of biomedical data analysis technology and discloses a data analysis method, system, and storage medium for genetic disease gene detection. The method includes the following steps: collecting patient samples and performing high-throughput sequencing to obtain raw data; performing quality control and comparison processing on the data to generate variant detection data and calculate the amount of variant information; automatically screening suspicious variant sites, analyzing the amount of variant information based on information entropy, and screening key variants according to preset thresholds; performing Bayesian inference analysis on key variants, calculating the probability of pathogenicity, and making pathogenicity determinations based on the ACMG standard; calculating the match between key variants and phenotypes based on patient phenotypic information, and screening variants that meet phenotypic characteristics; submitting the screening results to a doctor for review to generate the final screening results; and generating a standardized genetic test report based on the final results. This method efficiently and accurately identifies pathogenic variants, improving the automation level and clinical application value of genetic disease gene detection.

Apparatus for tracking cardiac indices and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory contains instructions configuring the processor to receive cardiac input data from a patient, input the cardiac input data into a cardiac panel including a plurality of cardiac models, at least one cardiac model of which is configured to calculate a cardiac index, includes at least one cardiac machine learning model, and is configured to calculate a cardiac index associated with diastolic dysfunction, generate one or more cardiac indices from the cardiac panel as a function of the cardiac input data and the cardiac machine learning model, wherein at least one cardiac index includes a probability of the patient satisfying at least one grading threshold, and display the at least one cardiac index through a graphical user interface.

This application discloses a method and system for secure fusion query governance of multimodal biomedical data. The method includes: receiving a fusion query request; verifying its legitimacy through a blockchain smart contract; decomposing it into sub-query tasks based on a multimodal metadata ontology model and distributing them; each data holder node generating intermediate results identified by a unified pseudonym in its local privacy-preserving computing environment; performing privacy-preserving data alignment and aggregation operations; returning the fusion result and recording key events in the blockchain smart contract. This application achieves integrated governance that ensures data is available but not visible, queries are flexible, processes are auditable, and contributions are incentivized. It is applicable to the secure collaborative analysis of multimodal biomedical data such as genomics, imaging, and electronic medical records.