Q-UEL / HDN Method and approach development to address Population Health and EBM / CDSS etc
OSEHRA 2015 Summit Presentation Attached Below
1.The QEXL Consortium researches a THINKING Semantic Web for mining, linking, managing, and inferencing from, all data and knowledge.
2.Q-UEL is being designed as an alternative to IBM’s Watson for medicine by distributing computation across the Internet rather than on centralized high performance machines.
3.Q-UEL started with data miners but in modern form was in response to the 2010 Report of the President’s Council of Advisors in Science and Technology, requesting an “XML-like” Universal Exchange Language for healthcare.
4.Q-UEL has a formal unifying basis: it closely follows the Dirac notation and probabilistic algebra that has been a standard in physics since the 1940s.
5.Q-UEL positions itself as a Universal Second Language as Pivot/Hub, meaning 2N interconversions between N standards and implementations, not N2-N!!!
6.Q-UEL is capable of encoding, storing and communicating electronic health records, biomedical data and summary statistics from archive and population studies, all as statements about observations, information, and knowledge; it also has metastatements (rules) about manipulating statements.
7.Q-UEL tags can autosurf and spawn on the web to extract knowledge for automated reasoning on a “Thinking Web”. Here a recent development has been application to automated Systematic Reviews and meta-analysis.
8.Q-UEL is described in some 12 publications in major “hardcopy” peer-reviewed journals, IEEE etc., and there are some 100 related and/or older publications.
Our endeavors have resulted in the development of Data Science to deliver Health Knowledge by Probabilistic Inference. The solution developed addresses the following main points (besides many others), notably the interoperability challenges of delivering semantically relevant knowledge both at patient health (clinical) and public health level (ACO).
1. Overcomes challenges created by the large data sets; such as uncertainty and high-dimensionality.
2. Advances MU1, MU2 and MU3 goals by addressing risk factors by integrating CDSS into HIE.
3. Overcomes some serious inadequacies in the existing methods based on Bayes Net, that does not necessarily deliver AI driven (unsupervised) Knowledge Extraction by Inference that is validated for its coherence. This is significant since we are now starting to extract semantic tags and building semantic triple store from millions of records by automation and machine learning.
Looking forward to collaborate and find impetus to make more progress wrt OSEHRA vision.