Background:Clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing (NLP) system. In recent development modules of cTAKES, a negation detection (ND) algorithm is used to improve annotation capabilities and simplify automatic identification of negative context in large clinical documents. In this research, the two types of ND algorithms used are lexicon and syntax, which are analyzed using a database made openly available by the National Center for Biomedical Computing. The aim of this analysis is to find the pros and cons of these algorithms.Methods:Patient medical reports were collected from three institutions included the 2010 i2b2/VA Clinical NLP Challenge, which is the input data for this analysis. This database includes patient discharge summaries and progress notes. The patient data is fed into five ND algorithms: NegEx, ConText, pyConTextNLP, DEEPEN and Negation Resolution (NR). NegEx, ConText and pyConTextNLP are lexicon-based, whereas DEEPEN and NR are syntax-based. The results from these five ND algorithms are post-processed and compared with the annotated data. Finally, the performance of these ND algorithms is evaluated by computing standard measures including F-measure, kappa statistics and ROC, among others, as well as the execution time of each algorithm.Results:This research is tested through practical implementation based on the accuracy of each algorithm’s results and computational time to evaluate its performance in order to find a robust and reliable ND algorithm.Conclusions:The performance of the chosen ND algorithms is analyzed based on the results produced by this research approach. The time and accuracy of each algorithm are calculated and compared to suggest the best method.
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.
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