Evaluation of Named Entity Recognition Algorithms Using Clinical Text Data

Abstract

Named Entity Recognition (NER) is one of the most important research areas in the field of medical. Presently, most of the clinical NER research is based on two approaches as Knowledge Engineering (KE) and Machine Learning (ML). KE is used a word lookup table approach and ML is known as supervised learning approach. The aim of this work is to evaluate a recent algorithm in KE and ML approaches using various clinical text databases. Therefore, the NOBLE Coder and Clinical Named Entity Recognition (CliNER) algorithms are selected, NOBLE Coder is depended on KE approach and CliNER is ML approach. The two algorithms will be described and compared its performance on three openly available datasets that is obtained from Medical Information Mart for Intensive Care II (MIM-IC II), Pittsburgh Medical Center, and i2b2 2010 challenge. Among these datasets, the annotated data are included which is used to detect the highest sensitivity and specificity on each algorithm. The randomly distributed patient reports were taken as input data to these algorithms. By executing these algorithms, the information is extracted and which classified into predefined concept types, for example medical problems, treatments and tests. The accuracy of both algorithms is calculated using standard measures. The taken two algorithms are analyzed based on the produced results. Finally, the best among two is suggested for better use in clinical data.