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

Maryam Panahiazar, Vahid Taslimitehrani, Ashutosh Jadhav, Jyotishman Pathak
{"title":"Empowering Personalized Medicine with Big Data and Semantic Web Technology: Promises, Challenges, and Use Cases.","authors":"Maryam Panahiazar,&nbsp;Vahid Taslimitehrani,&nbsp;Ashutosh Jadhav,&nbsp;Jyotishman Pathak","doi":"10.1109/BigData.2014.7004307","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74501,"journal":{"name":"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data","volume":"2014 ","pages":"790-795"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BigData.2014.7004307","citationCount":"80","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigData.2014.7004307","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 80

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.

Abstract Image

Abstract Image

Abstract Image

用大数据和语义网技术增强个性化医疗:承诺、挑战和用例。
在医疗保健领域,大数据工具和技术有潜力通过改善结果,同时降低每位患者的成本,创造巨大价值。诊断图像、基因测试结果和生物特征信息越来越多地生成并存储在电子健康记录中,这给我们带来了数据方面的挑战,这些数据本质上是大量、种类和速度的,因此需要新的方式来存储、管理和处理大数据。这就迫切需要开发新的、可伸缩的、可扩展的大数据基础设施和分析方法,使医疗保健提供者能够为个体患者访问知识,从而产生更好的决策和结果。在本文中,我们简要讨论了大数据的本质以及语义网和数据分析在生成“智能数据”方面的作用,这些“智能数据”提供可操作的信息,支持更好的个性化医疗决策。在我们看来,最大的挑战是创建一个系统,使医疗保健提供者和患者的大数据强大而智能,从而导致更有效的临床决策,改善健康结果,并最终管理医疗保健成本。我们强调了使用大数据的一些挑战,并提出需要一个语义数据驱动的环境来解决这些问题。我们用实际用例阐述了我们的愿景,并讨论了使用大数据和语义web技术增强个性化医疗的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信