Applying knowledge-anchored hypothesis discovery methods to advance clinical and translational research: the OAMiner project

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 19; no. 6; pp. 1110 - 1114
• Main Authors: Payne, Philip R O, Jackson, Rebecca D, Best, Thomas M, Borlawsky, Tara B, Lai, Albert M, James, Stephen, Gurcan, Metin N
• Format: Journal Article
• Language: English
• Published: England BMJ Group 01.11.2012
• Subjects: Case Reports; Data Mining; Humans; Image Processing, Computer-Assisted; Knowledge Bases; Natural Language Processing; Osteoarthritis, Knee; Translational Medical Research - methods; Translational Medical Research - statistics & numerical data; User-Computer Interface
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1136/amiajnl-2011-000736

The conduct of clinical and translational research regularly involves the use of a variety of heterogeneous and large-scale data resources. Scalable methods for the integrative analysis of such resources, particularly when attempting to leverage computable domain knowledge in order to generate actionable hypotheses in a high-throughput manner, remain an open area of research. In this report, we describe both a generalizable design pattern for such integrative knowledge-anchored hypothesis discovery operations and our experience in applying that design pattern in the experimental context of a set of driving research questions related to the publicly available Osteoarthritis Initiative data repository. We believe that this 'test bed' project and the lessons learned during its execution are both generalizable and representative of common clinical and translational research paradigms.