title: Leveraging rich annotations to improve learning of medical concepts from clinical free text.
abstract: Information extraction from clinical free text is one of the key elements in medical informatics research. In this paper we propose a general framework to improve learning-based information extraction systems with the help of rich annotations (i.e., annotators provide the medical assertion as well as evidences that support the assertion). A special graphical interface was developed to facilitate the annotation process, and we show how to implement this framework with a state-of-the-art context-based question answering system. Empirical studies demonstrate that with about 10% longer annotation time, we can significantly improve the accuracy of the system. An approach to provide supporting evidence for test documents is also briefly discussed with promising preliminary results.
authors.affiliation: Knowledge Solutions, Siemens Medical Solutions, Malvern, PA, USA.
authors.forename: Shipeng
authors.initials: S
authors.lastname: Yu
authors.forename: Faisal
authors.initials: F
authors.lastname: Farooq
authors.forename: Balaji
authors.initials: B
authors.lastname: Krishnapuram
authors.forename: Bharat
authors.initials: B
authors.lastname: Rao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057286:Electronic Health Records; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing