EMRs2CSP : Mining Clinical Status Pathway from Electronic Medical Records

Fast advancement in computerized information obtaining procedures have prompted immense volume of information extraction of text. Most of the data is composed of either unstructured or semi-structured form of text. To make this unstructured form of data into structured form using text mining, natural language process (NLP) techniques and machine learning algorithms are used. Cancer based text are in the form of Electronic Health Record (EHR/EMR) and there are tools to extract the text. Health care and clinical practice create a lot of content manifestations, test results, analyse, medicines, also, results for patients. This clinical content, reported in wellbeing records, is a potential wellspring of information and an underused asset for improved social insurance. To improve understanding consideration, information on demonstrative, prognostic, inclining, and medication reaction markers are fundamental. In this paper explored different text mining approaches using machine learning, natural language processing and data mining techniques