Open AccessNLP Algorithms Endowed f or Automatic Extraction of Information from Unstructured Free Text Reports of Radiology Monarchy
Author(s) -
Vaishali M. Kumbhakarna,
Sonali B. Kulkarni,
Apurva D. Dhawale
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l8009.1091220
Subject(s) - artificial intelligence , conditional random field , crfs , computer science , cluster analysis , support vector machine , machine learning , random forest , algorithm , natural language processing , information extraction , statistical classification , classifier (uml)
Natural Language Processing (NLP) Algorithms are the key factors for automatic information extraction form the unstructured free-text radiology reports .To extract clinically important findings and recommendations, various NLP algorithms are used . A rule-based NLP system is used in most of the automated IE applications in medical domain; whereas some applications are using Random Forest classifier, PageRank Algorithm, clustering algorithm, Conditional Random Fields (CRF) algorithm, and deep learning-based approaches. Some papers found with methods used for IE like, Support Vector Machines (SVMs), linear-chain conditional random fields (LC-CRFs), k-means or k-medoids algorithm ,Affinity Propagation (AP) clustering algorithm , supervised machine learning algorithm and many more. Thus through this survey we can say that, NLP methods used to extract information ,brings new insights into already known clinical evidences. It also helps to identify previously unknown treatment and causal relations between biomedical entities .Therefore NLP algorithms has empowered Radiology monarchy.