title: ASLForm: an adaptive self learning medical form generating system.
abstract: To facilitate the process of extracting information from narrative medical reports and transforming extracted data into standardized structured forms, we present an interactive, incrementally learning based information extraction system - ASLForm. ASLForm provides users a convenient interface that can be used as a simple data extraction and data entry system. It is unique, however, in its ability to transparently analyze and quickly learn, from users' interactions with a small number of reports, the desired values for the data fields. Additional user feedback (through acceptance decision or edits on the generated values) can incrementally refine the decision model in real-time, which further reduces users' interaction effort thereafter. The system eventually achieves high accuracy on data extraction with minimal effort from users. ASLForm requires no special configuration or training sets, and is not constrained to specific domains, thus it is easy to use and highly portable. Our experiments demonstrate the effectiveness of the system.
authors.affiliation: Department of Mathematics and Computer Science, Emory University, Atlanta, GA.
authors.forename: Shuai
authors.initials: S
authors.lastname: Zheng
authors.affiliation: Department of Biomedical Informatics, Emory University, Atlanta, GA ; Center for Comprehensive Informatics, Emory University, Atlanta, GA.
authors.forename: Fusheng
authors.initials: F
authors.lastname: Wang
authors.affiliation: Department of Mathematics and Computer Science, Emory University, Atlanta, GA.
authors.forename: James J
authors.initials: JJ
authors.lastname: Lu
mesh_terms: D000043:Abstracting and Indexing; D001185:Artificial Intelligence; D057286:Electronic Health Records; D016247:Information Storage and Retrieval / Q000379:methods*; D007256:Information Systems; D014584:User-Computer Interface