Major New Papers On Big Data For Health Published In IEEE Journal Of Biomedical And Health Informatics
Leading international researchers contribute to new collection of research spanning bioinformatics to imaging, sensor and medical informatics for public health
London, UK, 24th July 2015 – A major collection of papers exploring the very latest research and key developments in Big Data for health informatics has been published by the IEEE’s Journal of Biomedical and Health Informatics (J-BHI). Authored by research teams from the world’s leading institutions, the papers of the Special Issue on “Big Data for Health” are available to download now via the IEEE Xplore® Digital Library at http://jbhi.embs.org/2015/07/08/special-issue-big-data-for-health/
Development in the fields of biomedical and health informatics are fast-moving and driving a major expansion in Big Data, both in terms of the sheer volume of information generated and the complexity, diversity, and richness of the data itself, which is now being acquired from multiple sources and platforms – from personal genomics to Twitter feeds. The growth of the Big Data opportunity also presents some key socio-legal challenges, requiring the development of new data analytic tools that can facilitate scalable, accessible and sustainable data infrastructure for the effective management of these large, multiscale, multimodal, distributed and heterogeneous data sets. Equally crucial is the ability to convert this data into actionable knowledge that can cost-effectively support clinical decision making, disease management, and care delivery. Professor Guang-Zhong Yang PhD, FREng, Editor-in-Chief of the IEEE J-BHI comments: “Big Data is emerging as a significant source of innovation in healthcare, accelerating the translational pathways from the laboratory bench to the patient’s bedside. This special issue assembles the leading researchers in this area and provides new insight into and understanding of how big data analytics can be harnessed in health systems to improve clinical decision-making and enhance efficiency of care provision, policy development and policy implementation.”
Professor Andrew Laine, President of the IEEE Engineering in Medicine & Biology Society (EMBS) added: “The potential impact of Big Data is only just beginning to be explored across many sectors, so this landmark collection of research comes at an important time, providing an authoritative picture of the current – and future – challenges and opportunities of exploiting Big Data for health informatics.”
The articles included in this Special Issue are:
• Big Data for Health by Andreu-Perez, J. et al
• Big Data, Big Knowledge: Big Data for Personalized Healthcare by Viceconti, M. et al
• Predicting Asthma-Related Emergency Department Visits Using Big Data by Ram, S. et al
• Predicting Days in Hospital Using Health Insurance Claims by Xie, Y. et al
• Hierarchical Classification of Large-Scale Patient Records for Automatic Treatment Stratification by Mei, K. et al
• We Feel: Mapping Emotion on Twitter by Larsen, M.E. et al
• Symmetrical Compression Distance for Arrhythmia Discrimination in Cloud-Based Big-Data Services by Lillo-Castellano, J.M. et al
• Optimal Drug Prediction From Personal Genomics Profiles by Sheng, J. et al
• Toward Noninvasive Quantification of Brain Radioligand Binding by Combining Electronic Health Records and Dynamic PET Imaging Data by Mikhno, A. et al
• Big Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging by Suinesiaputra, A. et al
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Development in the fields of biomedical and health informatics are fast-moving and driving a major expansion in Big Data, both in terms of the sheer volume of information generated and the complexity, diversity, and richness of the data itself, which is now being acquired from multiple sources and platforms – from personal genomics to Twitter feeds. The growth of the Big Data opportunity also presents some key socio-legal challenges, requiring the development of new data analytic tools that can facilitate scalable, accessible and sustainable data infrastructure for the effective management of these large, multiscale, multimodal, distributed and heterogeneous data sets. Equally crucial is the ability to convert this data into actionable knowledge that can cost-effectively support clinical decision making, disease management, and care delivery. Professor Guang-Zhong Yang PhD, FREng, Editor-in-Chief of the IEEE J-BHI comments: “Big Data is emerging as a significant source of innovation in healthcare, accelerating the translational pathways from the laboratory bench to the patient’s bedside. This special issue assembles the leading researchers in this area and provides new insight into and understanding of how big data analytics can be harnessed in health systems to improve clinical decision-making and enhance efficiency of care provision, policy development and policy implementation.”
Professor Andrew Laine, President of the IEEE Engineering in Medicine & Biology Society (EMBS) added: “The potential impact of Big Data is only just beginning to be explored across many sectors, so this landmark collection of research comes at an important time, providing an authoritative picture of the current – and future – challenges and opportunities of exploiting Big Data for health informatics.”
The articles included in this Special Issue are:
• Big Data for Health by Andreu-Perez, J. et al
• Big Data, Big Knowledge: Big Data for Personalized Healthcare by Viceconti, M. et al
• Predicting Asthma-Related Emergency Department Visits Using Big Data by Ram, S. et al
• Predicting Days in Hospital Using Health Insurance Claims by Xie, Y. et al
• Hierarchical Classification of Large-Scale Patient Records for Automatic Treatment Stratification by Mei, K. et al
• We Feel: Mapping Emotion on Twitter by Larsen, M.E. et al
• Symmetrical Compression Distance for Arrhythmia Discrimination in Cloud-Based Big-Data Services by Lillo-Castellano, J.M. et al
• Optimal Drug Prediction From Personal Genomics Profiles by Sheng, J. et al
• Toward Noninvasive Quantification of Brain Radioligand Binding by Combining Electronic Health Records and Dynamic PET Imaging Data by Mikhno, A. et al
• Big Heart Data: Advancing Health Informatics Through Data Sharing in Cardiovascular Imaging by Suinesiaputra, A. et al
Help employers find you! Check out all the jobs and post your resume.