Authors:Evan W. Patton & Deborah L. McGuinness
Concepts:eScience, Provenance, Data Science, Ontology, Linked Data, Machine Learning, & Natural Language Processing
Abstract:
Managing a complex illness often requires different treatment regimens spread over a long time. The complexity of these potentially life-threatening diagnoses can be daunting to patients while they are most vulnerable. We present a vision for artificial intelligence-enabled tools for assisting patients in managing the complex information given to them over the course of their treatments through the combination of existing and emerging techniques from natural language processing and knowledge representation. We provide examples from an actual breast cancer diagnosis and treatment plan and highlight the development of new combinations of techniques to build tools that can reason about data from a variety of sources and act as intelligence-augmenting agents. We conclude with a discussion of some additional challenges facing artificial intelligence practitioners as applications become patient-centric.
Date | Created By | Link |
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September 19, 2014 12:12:49 | Evan W. Patton | Download |
![]() | Mobile 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. |
![]() | Repurposing Drugs with Semantics (ReDrugS) Principal Investigator: Jonathan Dordick and Deborah L. McGuinness Description: We aim to find new effective treatments for disease using existing drugs. Our approach is to gather and integrate existing data using semantic technologies to help discover promising drug repurposing. |
![]() | Data Science Lead Professor: Peter Fox Description: 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: eScience |
![]() | Inference And Trust Lead Professor: Deborah L. McGuinness Description: Concepts: |
![]() | Knowledge Provenance Lead Professor: Deborah L. McGuinness Description: Concepts: Provenance, |
![]() | Semantic eScience Lead Professor: Peter Fox Description:
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 |
![]() | Web Science Lead Professor: Jim Hendler, Deborah L. McGuinness Description: Concepts: |