Text Mining in Biomedical Domain with Emphasis on Document Clustering

ObjectivesWith the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured...

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Bibliographic Details
Main Author: Vinaitheerthan Renganathan
Format: Article
Language:English
Published: The Korean Society of Medical Informatics 2017-07-01
Series:Healthcare Informatics Research
Subjects:
Online Access:http://e-hir.org/upload/pdf/hir-23-141.pdf
Description
Summary:ObjectivesWith the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents.MethodsThis paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain.ResultsText mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail.ConclusionsText mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.
ISSN:2093-3681
2093-369X