Automatically extracting information needs from Ad Hoc clinical questions.
Automatically extracting information needs from ad hoc clinical questions is an important step towards medical question answering. In this work, we first explored supervised machine-learning approaches to automatically classify an ad hoc clinical question into general topics. We then evaluated different methods for automatically extracting keywords from an ad hoc clinical question. Our methods were evaluated on the 4,654 clinical questions maintained by the National Library of Medicine. Our best systems or methods showed F-score of 76% for the task of question-topic classification and an average F-score of 56% for extracting keywords from ad hoc clinical questions.
Yu
Hong
H
Departments of Health Sciences, Computer Science, Medical InformatiUniversity of Wisconsin-Milwaukee, Wisconsin, USA.
Cao
Yong-Gang
YG
D000465:Algorithms; D001185:Artificial Intelligence; D003142:Communication; D020000:Decision Support Systems, Clinical; D033181:Information Dissemination / Q000379:methods*; D020407:Internet; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D019095:Point-of-Care Systems; D019114:Remote Consultation / Q000379:methods*; D014584:User-Computer Interface