title: Automated identification of medical concepts and assertions in medical text.
abstract: 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.
authors.affiliation: Knowledge Solutions, Siemens Healthcare. Malvern, PA USA.
authors.forename: Rómer
authors.initials: R
authors.lastname: Rosales
authors.forename: Faisal
authors.initials: F
authors.lastname: Farooq
authors.forename: Balaji
authors.initials: B
authors.lastname: Krishnapuram
authors.forename: Shipeng
authors.initials: S
authors.lastname: Yu
authors.forename: Glenn
authors.initials: G
authors.lastname: Fung
mesh_terms: D057286:Electronic Health Records; D006801:Humans; D009323:Natural Language Processing; D012660:Semantics