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In this paper, we present our approach to extracting structured information from unstructured Electronic Health Records (EHR)  which can be used to, for example, study adverse drug reactions in patients due to chemicals in their products. Our solution uses a combination of Natural Language Processing (NLP) techniques and a web-based annotation tool to optimize the performance of a custom Named Entity Recognition (NER)  model trained on a limited amount of EHR training data. This work wasarXiv:1910.11241v2 fatcat:vfwj3welx5br5d47jpn4u2ggt4