Open AccessPreprocessing Methods for Unstructured Healthcare Text Data
Author(s) -
Niki Patel,
Assoc Professor
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1024.1292s19
Subject(s) - unstructured data , computer science , preprocessor , data pre processing , lexical analysis , information retrieval , data retrieval , data collection , data mining , big data , artificial intelligence , statistics , mathematics
At present, the amount unstructured text data is increasing exponentially from the past periodically. Information retrieval (IR) from these unstructured text data is challenging. As the data users foresee for particular/specific outcomes. Retrieval of the significant outcomes depends on the fashion, how they are associated/indexed. Unstructured text data like clinical data containing more health information requires challenging preprocessing methods, which also help to reduce the size of the dataset so that it will optimize the performance of the IR system. In this paper, we have proposed the pre-processing methods such as Data collection, Data Cleaning, Tokenization, Stemming, Removal of Stop words which will efficiently help the data users to find the specific patterns from the unstructured text data.