title: Automatic Generation of Conditional Diagnostic Guidelines.
abstract: The diagnostic workup for many diseases can be extraordinarily nuanced, and as such reference material text often contains extensive information regarding when it is appropriate to have a patient undergo a given procedure. In this work we employ a three task pipeline for the extraction of statements indicating the conditions under which a procedure should be performed, given a suspected diagnosis. First, we identify each instance in the text where a procedure is being recommended. Next we examine the context around these recommendations to extract conditional statements that dictate the conditions under which the recommendation holds. Finally, corefering recommendations across the document are linked to produce a full recommendation summary. Results indicate that each underlying task can be performed with above baseline performance, and the output can be used to produce concise recommendation summaries.
authors.affiliation: IBM Almaden Research, San Jose, CA, USA.
authors.forename: Tyler
authors.initials: T
authors.lastname: Baldwin
authors.affiliation: IBM Almaden Research, San Jose, CA, USA.
authors.forename: Yufan
authors.initials: Y
authors.lastname: Guo
authors.affiliation: IBM Almaden Research, San Jose, CA, USA.
authors.forename: Tanveer
authors.initials: T
authors.lastname: Syeda-Mahmood
mesh_terms: D003933:Diagnosis; D006801:Humans; D009323:Natural Language Processing; D017410:Practice Guidelines as Topic; D060388:Support Vector Machine