Healthc Inform Res.  2017 Jul;23(3):141-146. 10.4258/hir.2017.23.3.141.

Text Mining in Biomedical Domain with Emphasis on Document Clustering

Affiliations
  • 1Head of Institutional Research, Skyline University College, Sharjah, UAE. vinairesearch@yahoo.com

Abstract


OBJECTIVES
With 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.
METHODS
This 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.
RESULTS
Text 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.
CONCLUSIONS
Text 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.

Keyword

Text Mining; Cluster Analysis; Classification; Natural Language Processing; Software

MeSH Terms

Classification
Cluster Analysis*
Data Mining*
Mining
Natural Language Processing

Cited by  1 articles

Text Mining of Biomedical Articles Using the Konstanz Information Miner (KNIME) Platform: Hemolytic Uremic Syndrome as a Case Study
Ricardo A. Dorr, Juan J. Casal, Roxana Toriano
Healthc Inform Res. 2022;28(3):276-283.    doi: 10.4258/hir.2022.28.3.276.


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