TY - GEN
T1 - Empowering personalized medicine with big data and semantic web technology
T2 - 2nd IEEE International Conference on Big Data, IEEE Big Data 2014
AU - Panahiazar, Maryam
AU - Taslimitehrani, Vahid
AU - Jadhav, Ashutosh
AU - Pathak, Jyotishman
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
KW - Big Data
KW - Health Care
KW - Personalized Medicine
KW - Semantic Web
KW - Smart Data
UR - http://www.scopus.com/inward/record.url?scp=84921714097&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84921714097&partnerID=8YFLogxK
U2 - 10.1109/BigData.2014.7004307
DO - 10.1109/BigData.2014.7004307
M3 - Conference contribution
AN - SCOPUS:84921714097
T3 - Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
SP - 790
EP - 795
BT - Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
A2 - Lin, Jimmy
A2 - Pei, Jian
A2 - Hu, Xiaohua Tony
A2 - Chang, Wo
A2 - Nambiar, Raghunath
A2 - Aggarwal, Charu
A2 - Cercone, Nick
A2 - Honavar, Vasant
A2 - Huan, Jun
A2 - Mobasher, Bamshad
A2 - Pyne, Saumyadipta
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 October 2014 through 30 October 2014
ER -