This document summarizes a research article about using text mining techniques such as document clustering in the biomedical domain. It discusses how text mining can extract unknown knowledge from unstructured biomedical documents by preprocessing texts, applying natural language processing, and clustering or classifying documents. The techniques facilitate mining vast amounts of knowledge from published research that would otherwise be difficult to conclude without automated analysis.

1. Text Mining in Biomedical Domain with
Emphasis on Document Clustering
Article in Healthcare Informatics Research 23(3):141-146 · July 2017
DOI: 10.4258/hir.2017.23.3.141
1st Vinaitheerthan Renganathan
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.
Link
http://www.e-hir.org/journal/view.html?uid=1244&start=&sort=Regnum-
0&scale=50&key=&oper=&key_word=&year1=&year2=&Vol=023&Num=03&PG=&book=Journal&m
od=current_issue&sflag=&sub_box=Y&aut_box=Y&sos_box=&pub_box=Y&key_box=&abs_box=&ye
ar=2017