The biointelligence Framework: A new computational platform for biomedical knowledge computing

Toni Farley, Jeff Kiefer, Preston Lee, Daniel Von Hoff, Jeffrey M. Trent, Charles Colbourn, Spyro Mousses

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multilateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.

Original languageEnglish (US)
Pages (from-to)128-133
Number of pages6
JournalJournal of the American Medical Informatics Association
Issue number1
StatePublished - 2013

ASJC Scopus subject areas

  • Health Informatics


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