Semantic Modeling of Cohort Descriptions in Research Studies

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Semantic Modeling of Cohort Descriptions in Research Studies [Download]

Abstract:

Recommendations in ADA’s Standards of Medical Care in Diabetes guideline are supported by findings from scientific publications (primarily clinical trials and case studies). We propose an approach rooted in Information Extraction and Knowledge Representation techniques to augment guideline representations with population descriptions from cited literature. We develop a Cohort Ontology, modeling patient groups and interventions mentioned in cited publications, ultimately aiming at assisting physicians and computers to suggest recommendations for complex patients using cohort alignment.

Related Projects:

TW LogoHealth Empowerment by Analytics, Learning, and Semantics (HEALS) Project (HEALS)
Co Investigator: Deborah L. McGuinness
Description: The Center for Health Empowerment by Analytics, Learning, and Semantics (HEALS) is a five-year collaboration between Rensselaer and IBM aimed at researching how the application of advanced cognitive computing capabilities can help people to understand and improve their own health conditions.

Related Research Areas:

Data Science
Lead Professor: Peter Fox
Description: Science has fully entered a new mode of operation. Data science is advancing inductive conduct of science driven by the greater volumes, complexity and heterogeneity of data being made available over the Internet. Data science combines of aspects of data management, library science, computer science, and physical science using supporting cyberinfrastructure and information technology. As such it is changing the way all of these disciplines do both their individual and collaborative work.

Data science is helping scienists face new global problems of a magnitude, complexity and interdisciplinary nature whose progress is presently limited by lack of available tools and a fully trained and agile workforce.

At present, there is a lack formal training in the key cognitive and skill areas that would enable graduates to become key participants in escience collaborations. The need is to teach key methodologies in application areas based on real research experience and build a skill-set.

At the heart of this new way of doing science, especially experimental and observational science but also increasingly computational science, is the generation of data.

Concepts: