Natural Language Processing (NLP) and Machine Learning concepts are gaining rapid importance in the era of digitalization of data. The value of data keeps changing over time and makes it important to harness that value for performing in depth research in various domains. Extracting information from clinical text helps in automated terminology management, data mining, de-identification of clinical text, research subject identification and studying effect of research on them, predicting the onset and progress of various chronic diseases, disease-treatment-side effect analysis etc. Methods based on NLP and Machine Learning tends to perform better in this area but more experience is required to analyse clinical text than the biomedical literature. The issues and opportunities in information extraction from the clinical text need to be intensively reviewed to find new avenues in this domain of research.

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Natural Language Processing Machine Learning Clinical Text Information Extraction Electronic Health Records Viterbi Algorithm.