@inproceedings{92060a1fe6d74f91b64c0ea599291f96,
title = "Formalizing evidence type definitions for drug-drug interaction studies to improve evidence base curation",
abstract = "In this research we aim to demonstrate that an ontology-based system can categorize potential drug-drug interaction (PDDI) evidence items into complex types based on a small set of simple questions. Such a method could increase the transparency and reliability of PDDI evidence evaluation, while also reducing the variations in content and seriousness ratings present in PDDI knowledge bases. We extended the DIDEO ontology with 44 formal evidence type definitions. We then manually annotated the evidence types of 30 evidence items. We tested an RDF/OWL representation of answers to a small number of simple questions about each of these 30 evidence items and showed that automatic inference can determine the detailed evidence types based on this small number of simpler questions. These results show proof-of-concept for a decision support infrastructure that frees the evidence evaluator from mastering relatively complex written evidence type definitions.",
keywords = "Artificial intelligence, Drug interactions, Ontologies",
author = "Joseph Utecht and Mathias Brochhausen and John Judkins and Jodi Schneider and Boyce, {Richard D.}",
note = "Publisher Copyright: {\textcopyright} 2017 International Medical Informatics Association (IMIA) and IOS Press.; 16th World Congress of Medical and Health Informatics: Precision Healthcare through Informatics, MedInfo 2017 ; Conference date: 21-08-2017 Through 25-08-2017",
year = "2017",
doi = "10.3233/978-1-61499-830-3-960",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press",
pages = "960--964",
editor = "Zhao Dongsheng and Gundlapalli, {Adi V.} and Jaulent Marie-Christine",
booktitle = "MEDINFO 2017",
address = "Netherlands",
}