A Reasoning And Hypothesis-Generation Framework Based On Scalable Graph Analytics
- ORNL
Finding actionable insights from data has always been difficult. As the scale and forms of data increase tremendously, the task of finding value becomes even more challenging. Data scientists at Oak Ridge National Laboratory are leveraging unique leadership infrastructure (e.g. Urika-XA and Urika-GD appliances) to develop scalable algorithms for semantic, logical and statistical reasoning with unstructured Big Data. We present the deployment of such a framework called ORiGAMI (Oak Ridge Graph Analytics for Medical Innovations) on the National Library of Medicine s SEMANTIC Medline (archive of medical knowledge since 1994). Medline contains over 70 million knowledge nuggets published in 23.5 million papers in medical literature with thousands more added daily. ORiGAMI is available as an open-science medical hypothesis generation tool - both as a web-service and an application programming interface (API) at http://hypothesis.ornl.gov . Since becoming an online service, ORIGAMI has enabled clinical subject-matter experts to: (i) discover the relationship between beta-blocker treatment and diabetic retinopathy; (ii) hypothesize that xylene is an environmental cancer-causing carcinogen and (iii) aid doctors with diagnosis of challenging cases when rare diseases manifest with common symptoms. In 2015, ORiGAMI was featured in the Historical Clinical Pathological Conference in Baltimore as a demonstration of artificial intelligence to medicine, IEEE/ACM Supercomputing and recognized as a Centennial Showcase Exhibit at the Radiological Society of North America (RSNA) Conference in Chicago. The final paper will describe the workflow built for the Cray Urika-XA and Urika-GD appliances that is able to reason with the knowledge of every published medical paper every time a clinical researcher uses the tool.
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1255686
- Resource Relation:
- Conference: Cray User Group, London, United Kingdom, 20160508, 20160512
- Country of Publication:
- United States
- Language:
- English
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