Patents
Literature
Patsnap Copilot is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
Patsnap Copilot

72 results about "Healthcare associated" patented technology

Chinese medical knowledge atlas construction method based on deep learning

ActiveCN106776711AEasy to handleRelationship Accurate and ComprehensiveWeb data indexingSemantic analysisKnowledge unitHealthcare associated
The invention relates to the technology of a knowledge atlas, and aims to provide a Chinese medical knowledge atlas construction method based on deep learning. The Chinese medical knowledge atlas construction method comprises the following steps: obtaining relevant data of a medical field from a data source; using a word segmentation tool to carry out word segmentation on unstructured data, and using an RNN (Recurrent Neural Network) to finish a sequence labeling task to identify entities related to medical care, so as to realize the extraction of knowledge units; carrying out feature vector construction on the entity, and utilizing the RNN to carry out sequence labeling and finish the identification of a relationship among the knowledge units; carrying out entity alignment, and then utilizing the extracted entities and the relationship between the entities to construct the knowledge atlas. According to the Chinese medical knowledge atlas construction method, a recurrent neural network is artfully used for extracting the knowledge units and identifying the relationship among the knowledge units so as to favorably finish the processing of the unstructured data. According to the Chinese medical knowledge atlas construction method, features suitable for the medical care field are put forward to carry out a training task of a network. Compared with general features, the features put forward by the method can better represent a medical entity, and therefore, the relationship among the extracted knowledge units can be more accurate and comprehensive.
Owner:ZHEJIANG UNIV

Medical question-answering system construction method based on language model and entity matching

The invention discloses a medical question-answering system construction method based on a language model and entity matching. The method comprises the steps: S1, data collection; S2, deep neural network model design; S3, named entity recognition model training and knowledge graph construction; S4, complete medical retrieval type question-answering system construction. The method specifically comprises: collecting network medical discussion posts, cleaning the network medical discussion posts, and storing the cleaned network medical discussion posts into ElasticSearch to serve as a retrieval data set; processing open source data of the competition data set by using a medical natural language, and training a named entity recognition model related to medical treatment; and collecting a public data set of the open source website to form a medical knowledge graph so as to expand a retrieval process. According to the medical question-answering system method based on language model and entity matching, after the question-answering system is constructed and recalled, finely arranged and comprehensively scored, the most appropriate answer is output in combination with a reasonable scoring mechanism, and the defects of a retrieval type question-answering system and a knowledge graph type question-answering system are overcome.
Owner:SICHUAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products