Ensemble approach for identifying medical concepts with special attention to lexical scope

Health-care services are implanted by deploying roots of information extraction techniques. This extraction process is laborious and time-consuming due to unavailability of medical experts. Thus, in the present task, we were motivated to develop an automated extraction system for identifying medical and non-medical concepts. These concepts help to extract the key information from medical corpora. Not only medical concepts but also their non-medical counterparts are equally important for diagnosis purposes. Hence, we have employed three different approaches such as unsupervised, supervised, and their combined ensemble version to identify both medical and non-medical terms (words/phrases). The unsupervised module consists of two phases: parts-of-speech (POS) tagging followed by searching in a domain-specific lexicon, namely WordNet of Medical Event (WME 3.0). On the other hand the supervised module is designed by two machine learning classifiers, namely Naive Bayes and Conditional Random Field (CRF) along with various features like category, POS, sentiment, etc. Finally, we have combined the important outcomes of unsupervised and supervised modules and developed two versions of ensemble module (Ensemble-I and Ensemble-II). All the modules identify uni-gram, bi-gram, tri-gram, and more than tri-gram medical concepts and separate non-medical words or phrases in a context. In order to evaluate all modules of concept identification system, we have prepared an experimental dataset. It has been split into three parts, namely training, development, and test. We observed that ensemble module provides better output in contrast with individual modules and Ensemble-I outperforms Ensemble-II in identifying medical concepts consisting of all possible n-grams. The result analysis shows that the F-measures of 0.91 and 0.94 have been obtained for identifying medical concepts and non-medical words/phrases using both of the ensemble modules, respectively. The present research reports the initial steps to build an automated concept identification framework in health-care. This system assists in designing various domain-specific applications like annotation, categorization, recommendation system, etc.