ContextMEL: Classifying Contextual Modifiers in Clinical Text

Abstract

Taking advantage of electronic health records in clinical research requires the development of natural language processing tools to extract data from unstructured text in different languages. A key task is the detection of contextual modifiers, such as understanding whether a concept is negated or if it belongs to the past. We present ContextMEL, a method to build classifiers for contextual modifiers that is independent of the specific task and the language, allowing for a fast model development cycle. ContextMEL uses annotation by experts to build a curated dataset, and state-of-the-art deep learning architectures to train models with it. We discuss the application of ContextMEL for three modifiers, namely Negation, Temporality and Certainty, on Spanish and Catalan medical text. The metrics we obtain show our models are suitable for industrial use, outperforming commonly used rule-based approaches such as the NegEx algorithm.