Finding Hidden Relationships Between Medical Concepts by Leveraging Metamap and Text Mining Techniques
Text is one of the most common ways to store data in this computerized world. At a glance, it may seem that those data are not interconnected. But in reality, data can have hidden connections. Therefore, in this research, a new model has been presented that can find hidden relationships between two medical concepts by using MetaMap and appropriate text-mining techniques. Specifically, the model creates a new comprehensive index structure and can find cross-document hidden links connecting topics of interest that most existing approaches have ignored. Experiments show the effectiveness of the proposed model in discovering new connections between topics.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Knowledge Transfer with Medical Language Embeddings
Identifying relationships between concepts is a key aspect of scientific knowledge synthesis. Finding these links often requires a researcher to laboriously search through scien- tific papers and databases, as the size o…
Knowledge Graph CompletionKnowledge GraphsLanguage ModellingTransfer LearningA Semantically-Aware Relevance Measure for Content-Based Medical Image Retrieval Evaluation
Performance evaluation for Content-Based Image Retrieval (CBIR) remains a crucial but unsolved problem today especially in the medical domain. Various evaluation metrics have been discussed in the literature to solve thi…
Content-Based Image RetrievalDescriptiveImage RetrievalKnowledge Graphs+2Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning
We introduce GENomic Encoding REpresentation with Language Model (GENEREL), a framework designed to bridge genetic and biomedical knowledge bases. What sets GENEREL apart is its ability to fine-tune language models to in…
Contrastive LearningData IntegrationKnowledge GraphsLanguage Modeling+1Finding New Connections between Concepts from Medline Database Incorporating Domain Knowledge
In this digital world, data is everything and significantly impacts our everyday lives. Interestingly, in this small world, everything is part of an ecosystem, where everything is connected, directly or indirectly. The s…
MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction
Relation extraction in the biomedical domain is challenging due to the lack of labeled data and high annotation costs, needing domain experts. Distant supervision is commonly used to tackle the scarcity of annotated data…
RelationRelation Extraction