Automating Population Health Studies through Semantics and Statistics Semantic Statistics (SemStats)
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Authors
New, Alexander
Qi, Miao
Chari, Shruthi
Rashid, Sabbir
Seneviratne, Oshani
McCusker, Jamie
Erickson, John S.
McGuinness, Deborah L.
Bennett, Kristin P.
Issue Date
2019-10
Type
Article
Language
Keywords
semstats
Alternative Title
Abstract
With the rapid development of the Semantic Web, machines
are able to understand the contextual meaning of data, including in the
field of automated semantics-driven statistical reasoning. This paper introduces a semantics-driven automated approach for solving population
health problems with descriptive statistical models. A fusion of semantic
and machine learning techniques enables our semantically-targeted analytics framework to automatically discover informative subpopulations
that have subpopulation-specific risk factors significantly associated with
health conditions such as hypertension and type II diabetes. Based on
our health analysis ontology and knowledge graphs, the semanticallytargeted analysis automated architecture allows analysts to rapidly and
dynamically conduct studies for different health outcomes, risk factors,
cohorts, and analysis methods; it also lets the full analysis pipeline be
modularly specified in a reusable domain-specific way through the usage of knowledge graph cartridges, which are application-specific fragments of the underlying knowledge graph. We evaluate the semanticallytargeted analysis framework for risk analysis using the National Health
and Nutrition Examination Survey and conclude that this framework
can be readily extended to solve many different learning and statistical
tasks, and to exploit datasets from various domains in the future
Description
Full Citation
Alexander New, Miao Qi, Shruthi Chari, Sabbir M. Rashid, Oshani Seneviratne, Jim McCusker, John S. Erickson, Deborah L. McGuinness and Kristin P. Bennett. Automating Population Health Studies through Semantics and Statistics Semantic Statistics (SemStats). Co-located with the International Semantic Web Conference, Auckland, NZ, October, 2019.
Publisher
Springer