Automated identification of medical concepts and assertions in medical text.

This paper describes a machine learning, text processing approach that allows the extraction of key medical information from unstructured text in Electronic Medical Records. The approach utilizes a novel text representation that shares the simplicity of the widely used bag-of-words representation, but can also represent some form of semantic information in the text. The large dimensionality of this type of learning models is controlled by the use of a ℓ(1) regularization to favor parsimonious models. Experimental results demonstrate the accuracy of the approach in extracting medical assertions that can be associated to polarity and relevance detection.