ASLForm: an adaptive self learning medical form generating system.

• Type of Material: Conference Paper
• Authors: Shuai Zheng, Emory University; Fusheng Wang, Emory University; James Lu, Emory University
• Language: English
• Date: 2013-01-01
• Publisher: Emory University Libraries
• Publication Version: Final Published Version
• Copyright Statement: ©2013 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
• Final Published Version (URL): https://knowledge.amia.org/
• Title of Journal or Parent Work: AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
• Volume: 2013
• Start Page: 1590
• End Page: 1599
• Keywords: Abstracting and Indexing as Topic; User-Computer Interface; Artificial Intelligence; Information Storage and Retrieval; Electronic Health Records; Information Systems
• Research Categories: Engineering, Biomedical; Computer Science

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.