Ramachandran R
@annamalaiuniversity.ac.in
Assistant Professor, Computer and Information Science
Annamalai University
RESEARCH INTERESTS
Big Data, NLP, Machine Learning
Scopus Publications
Scopus Publications
R. Ramachandran and K. Arutchelvan
Springer Science and Business Media LLC
Dr. Arutchelvan K. and Ramachandran R.
ENGG Journals Publications
Nowadays, Natural Language Processing (NLP) plays a significant role in extracting the concealed information from the unstructured data which is being loaded with voluminous data over the web. Various tasks such as Tokenization, Stemming, Parts-of-Speech identification, Lemmatization, Named Entity Recognition (NER), etc are being popular in NLP research area. In recent years, NER is getting more attention among the researchers to extract the important entities from the huge set of documents. In life science domain NER is playing major role to identify the medical-term entities from the medical related documents such as literature documents, clinical trials, Electronic Medical Record (EMR), etc. This research work aims to provide a new NER approach to get the named entities from the medical literature documents. Instead of build and trained a new model, the proposed model works based on the Transfer Learning. In order to reduce the training time, the pre-trained model is re-trained with the newly annotated entities. The proposed NER produces better accuracy and able to identify a greater number of entities. The NER model is experimented with PubMed articles.
R. Ramachandran and K. Arutchelvan
Springer Science and Business Media LLC
There have been many changes in the medical field due to technological advances. The progression in technologies provides lot of opportunities to extract valuable insights from huge amount of unstructured data. The literature documents published by the researchers in medical domain consists enormous amount of knowledge. Many organizations are involving in retrieving the hidden information from the literature documents. Extracting the drug names, diseases, symptoms, route of administration, species and dosage forms from the textual document is an easy task due to the innovation of technologies in the Natural Language Processing. In this article, a new hybrid based approach is proposed to identify named entity from the medical literature documents. New dictionary has been built for route of administration, dosage forms and symptoms to annotate the entities in the medical documents. The annotated entities are trained by the blank Spacy machine learning model. The trained model provide a decent accuracy when compared with the existing model. The hybrid model is validated with the dictionary and human (optional)to calculate the confusion matrix. It is able to identify more entities than the prevailing model. The average F1 score for five entities of the proposed hybrid based approach 73.79%.
R. Ramachandran and K. Arutchelvan
IEEE
Medical literature comprises valued information, like clinical symptoms, diagnosis, dosage, and treatment for specific diseases of a particular disease. Named Entity Recognition (NER) is a primary process involved in the extraction of knowledge from unstructured text and providing it as a Knowledge Graph (KG). Several existing works of NER suffers from small scale human- labeled training dataset. Since the extraction of knowledge from medical literature is a difficult process, this paper aims to focus on the development of the NER model using machine learning (ML) approaches to improve efficiency. The proposed model is based on the optimized version of support vector machine (O-SVM) where the optimal parameters of the tree based SVM are tuned by the particle swarm optimization (PSO) algorithm. In addition, the medical dataset is initially preprocessed and then the classification process takes place via the O-SVM model. The weight and bias parameters in tree based SVM model are tuned by the PSO algorithm. The experimental results analysis of the O-SVM model is carried out and the results are compared with the state of art approaches in terms of diverse measures.