Semantic text mining and its application in biomedical domain

2021 ◽  
Author(s):  
Illhoi Yoo
Keyword(s):  
Text Mining ◽  
Biomedical Domain

A systematic review of text mining approaches applied to various application areas in the biomedical domain

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sudha Cheerkoot-Jalim ◽  
Kavi Kumar Khedo
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Biomedical Application ◽  
Data Sources ◽  
Biomedical Text ◽  
Biomedical Text Mining ◽  
Biomedical Domain ◽  
Content Type ◽  
Health Related ◽  
Research Questions

Purpose This work shows the results of a systematic literature review on biomedical text mining. The purpose of this study is to identify the different text mining approaches used in different application areas of the biomedical domain, the common tools used and the challenges of biomedical text mining as compared to generic text mining algorithms. This study will be of value to biomedical researchers by allowing them to correlate text mining approaches to specific biomedical application areas. Implications for future research are also discussed. Design/methodology/approach The review was conducted following the principles of the Kitchenham method. A number of research questions were first formulated, followed by the definition of the search strategy. The papers were then selected based on a list of assessment criteria. Each of the papers were analyzed and information relevant to the research questions were extracted. Findings It was found that researchers have mostly harnessed data sources such as electronic health records, biomedical literature, social media and health-related forums. The most common text mining technique was natural language processing using tools such as MetaMap and Unstructured Information Management Architecture, alongside the use of medical terminologies such as Unified Medical Language System. The main application area was the detection of adverse drug events. Challenges identified included the need to deal with huge amounts of text, the heterogeneity of the different data sources, the duality of meaning of words in biomedical text and the amount of noise introduced mainly from social media and health-related forums. Originality/value To the best of the authors’ knowledge, other reviews in this area have focused on either specific techniques, specific application areas or specific data sources. The results of this review will help researchers to correlate most relevant and recent advances in text mining approaches to specific biomedical application areas by providing an up-to-date and holistic view of work done in this research area. The use of emerging text mining techniques has great potential to spur the development of innovative applications, thus considerably impacting on the advancement of biomedical research.

Application of text mining in the biomedical domain

Methods ◽  
2015 ◽  
Vol 74 ◽  
pp. 97-106 ◽  
Author(s):  
Wilco W.M. Fleuren ◽  
Wynand Alkema
Keyword(s):  
Text Mining ◽  
Biomedical Domain

scholarly journals An analysis on the entity annotations in biological corpora

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 96 ◽  
Author(s):  
Mariana Neves
Keyword(s):  
Text Mining ◽  
Biomedical Domain ◽  
Semantic Annotations ◽  
Biological Domain

Collection of documents annotated with semantic entities and relationships are crucial resources to support development and evaluation of text mining solutions for the biomedical domain. Here I present an overview of 36 corpora and show an analysis on the semantic annotations they contain. Annotations for entity types were classified into six semantic groups and an overview on the semantic entities which can be found in each corpus is shown. Results show that while some semantic entities, such as genes, proteins and chemicals are consistently annotated in many collections, corpora available for diseases, variations and mutations are still few, in spite of their importance in the biological domain.

Applications of Ontologies and Text Mining in the Biomedical Domain

2010 ◽  
pp. 261-283 ◽  
Author(s):  
A. Jimeno-Yepes ◽  
R. Berlanga-Llavori ◽  
D. Rebholz-Schuchmann
Keyword(s):  
Text Mining ◽  
Domain Knowledge ◽  
Text Processing ◽  
Named Entity Recognition ◽  
Real Life ◽  
User Interaction ◽  
Entity Recognition ◽  
Ontology Development ◽  
Biomedical Domain ◽  
Processing Techniques

Ontologies represent domain knowledge that improves user interaction and interoperability between applications. In addition, ontologies deliver precious input to text mining techniques in the biomedical domain, which might improve the performance in different text mining tasks. This chapter will explore on the mutual benefits for ontologies and text mining techniques. Ontology development is a time consuming task. Most efforts are spent in the acquisition of terms that represent concepts in real life. This process can use the existing scientific literature and the World Wide Web. The identification of concept labels, i.e. terms, from these sources using text mining solutions improves ontology development since the literature resources make reference to existing terms and concepts. Furthermore, automatic text processing techniques profit from ontological resources in different tasks, for example in the disambiguation of terms and the enrichment of terminological resources for the text mining solution. One of the most important text mining tasks that exploits ontological resources consists of the mapping of concepts to terms in textual sources (e.g. named entity recognition, semantic indexing) and the expansion of queries in information retrieval.

Science and technology text mining:: Text Mining of the Journal Cortex

2013 ◽  
Author(s):  
Ronald N. Kostoff ◽  
◽  
Henry A. Buchtel ◽  
John Andrews ◽  
Kirstin M. Pfiel
Keyword(s):  
Text Mining ◽  
Science And Technology
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