Bridging the Gap Between Consumers' Medication Questions and Trusted Answers

• Published in: Studies in health technology and informatics Vol. 264; p. 25
• Main Authors: Abacha, Asma Ben, Mrabet, Yassine, Sharp, Mark, Goodwin, Travis R, Shooshan, Sonya E, Demner-Fushman, Dina
• Format: Journal Article
• Language: English
• Published: Netherlands 21.08.2019
• Subjects: Health Informatics; Data Collection; Natural Language Processing
• ISSN: 1879-8365
• DOI: 10.3233/SHTI190176

This paper addresses the task of answering consumer health questions about medications. To better understand the challenge and needs in terms of methods and resources, we first introduce a gold standard corpus for Medication Question Answering created using real consumer questions. The gold standard (https://github.com/abachaa/Medication_QA_MedInfo2019) consists of six hundred and seventy-four question-answer pairs with annotations of the question focus and type and the answer source. We first present the manual annotation and answering process. In the second part of this paper, we test the performance of recurrent and convolutional neural networks in question type identification and focus recognition. Finally, we discuss the research insights from both the dataset creation process and our experiments. This study provides new resources and experiments on answering consumers' medication questions and discusses the limitations and directions for future research efforts.