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. 2014 Oct:2014:790-795.
doi: 10.1109/BigData.2014.7004307.

Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases

Maryam Panahiazar  1 , Vahid Taslimitehrani  2 , Ashutosh Jadhav, Jyotishman Pathak
Affiliations

Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases

Maryam Panahiazar et al. Proc IEEE Int Conf Big Data. 2014 Oct.

Abstract

In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating "smart data" which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.

Keywords: Big Data; Health Care; Personalized Medicine; Semantic Web; Smart Data.

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Figures

Figure 1
Figure 1
Mayo’s Big Data Architecture
Figure 2
Figure 2
Hadoop MapReduce framework with 16-node cluster for processing of the search queries with UMLS MetaMap.
Figure 3
Figure 3
Functional overview a mappers task: annotation of the search queries with UMLS concepts and semantic types using MetaMap tool.
Figure 4
Figure 4
Annotation of Images with the Concept from Ontology
Figure 5
Figure 5
Image Maker is a tools Implemented for Annotating Images to add Meta Data to the Images
See this image and copyright information in PMC

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