Towards Next Generation Integrative Mobile Semantic Health Information Assistants

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Presented at the Medical Device Day 2014


We aim to build a 24x7 health assistant platform that uses semantic technologies to integrate health data from a variety of resources to support patients in understanding when and how to actively engage in their care, thus potentially increasing a feeling of informed control and empowerment. We motivate the work with a use case helping a newly diagnosed breast cancer patient understand and participate in her treatment plan. We cross-reference data and information in clinical notes with broader medical online resources using natural language processing (e.g., Cross-source Information Extraction, Text Mining and Linking) and Watson-inspired technologies to provide contextualized and referenced information to support browsing and question answering.


DateCreated ByLink
November 10, 2014
Evan W. PattonDownload

Related Projects:

Mobile Health Project LogoMobile Health
Principal Investigator: Deborah L. McGuinness
Description: The Mobile Health project aims to bring semantic representations of medical data collected from a variety of consumer and medical grade devices and integrate those data on an individual's mobile smartphone. Combined with the reasoning capabilities of semantic web and technologies such as IBM Watson, this project plans to enable personalized health care through the instrumented self.

Related Research Areas:

Inference And Trust
Lead Professor: Deborah L. McGuinness
Description: Inference And Trust
Concepts: Semantic Web
Knowledge Provenance
Lead Professor: Deborah L. McGuinness
Description: Knowledge Provenance
Concepts: Provenance, Semantic Web
Semantic eScience
Lead Professor: Peter Fox
Science has fully entered a new mode of operation. E-science, defined as a combination of science, informatics, computer science, cyberinfrastructure and information technology is changing the way all of these disciplines do both their individual and collaborative work.
As semantic technologies have been gaining momentum in various e-Science areas (for example, W3C's new interest group for semantic web health care and life science), it is important to offer semantic-based methodologies, tools, middleware to facilitate scientific knowledge modeling, logical-based hypothesis checking, semantic data integration and application composition, integrated knowledge discovery and data analyzing for different e-Science applications.
Partially influenced by the Artificial Intelligence community, the Semantic Web researchers have largely focused on formal aspects of semantic representation languages or general-purpose semantic application development, with inadequate consideration of requirements from specific science areas. On the other hand, general science researchers are growing ever more dependent on the web, but they have no coherent agenda for exploring the emerging trends on the semantic web technologies. It urgently requires the development of a multi-disciplinary field to foster the growth and development of e-Science applications based on the semantic technologies and related knowledge-based approaches.

Concepts: eScience