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2021 2021 IEEE International Conference on Prognostics and Health Management (ICPHM)  
Big Data to Discover High Yield Prognostics ApplicationsCody Coleman, Yi Zhang and Maria SealeOPELRUL: Optimally Weighted Ensemble Learner for Remaining Useful Life PredictionOnat Gungor, Tajana Rosing  ...  Opening: Conference Opening T 1: Tutorial 1 Introduction to Battery Prognostics and Early Life Prediction T2: Tutorial 2 PHM in the Future of Communications Networks T3: Tutorial 3 In-Time  ... 
doi:10.1109/icphm51084.2021.9486550 fatcat:mzu6vmhnzndyjdkedhg4mqm7am

ContainerStress: Autonomous Cloud-Node Scoping Framework for Big-Data ML Use Cases [article]

Guang Chao Wang, Kenny Gross, Akshay Subramaniam
2020 arXiv   pre-print
Deploying big-data Machine Learning (ML) services in a cloud environment presents a challenge to the cloud vendor with respect to the cloud container configuration sizing for any given customer use case  ...  Moreover, the OracleLabs and NVIDIA authors have collaborated on a ML benchmark study which analyzes the compute cost and GPU acceleration of any ML prognostic algorithm and assesses the reduction of compute  ...  rates, to truly Big Data use cases involving terabytes of data per month from large fleets of assets.  ... 
arXiv:2003.08011v1 fatcat:n6ha3dckdzeuddu33rfdf47t2a

Linking Big Data and Prediction Strategies

Shiming Yang, Lynn G. Stansbury, Peter Rock, Thomas Scalea, Peter F. Hu
2019 Critical Care Medicine  
Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis.  ...  Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex  ...  Attempts to resolve data variety issues must be approached as an ongoing endeavor encompassing the following techniques: Data profiling (e.g., Data Mentors, Metagenix) to discover hidden relationships  ... 
doi:10.1097/ccm.0000000000003739 pmid:30920408 fatcat:vtrs5ygsgba7djksymj6x77pyq

Putting the data before the algorithm in big data addressing personalized healthcare

Eli M. Cahan, Tina Hernandez-Boussard, Sonoo Thadaney-Israni, Daniel L. Rubin
2019 npj Digital Medicine  
Technologies leveraging big data, including predictive algorithms and machine learning, are playing an increasingly important role in the delivery of healthcare.  ...  Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data.  ...  Inductive algorithms have already been employed to discover causal relationships in datasets with large amounts of unlabeled data.  ... 
doi:10.1038/s41746-019-0157-2 pmid:31453373 pmcid:PMC6700078 fatcat:vgtwy5ie5nbafkt3dfwhr3gohu

Generalizable Biomarkers in Critical Care

Timothy E. Sweeney, Purvesh Khatri
2017 Critical Care Medicine  
BIG DATA AND BIOMARKERS One of the biggest benefits of the data-driven omics approach to biomarker discovery is the possibility of discovering novel pathobiology in the heterogeneity of critical illness  ...  Our group has worked with many collaborators in repeatedly demonstrating that leveraging biological and technical heterogeneity across multiple cohorts can identify generalizable diagnostic and prognostic  ... 
doi:10.1097/ccm.0000000000002402 pmid:28509729 pmcid:PMC5800880 fatcat:d4i6bdl7abhgnby7w73hoeq7ym

Machine Learning for Precision Health Economics and Outcomes Research (P-HEOR): Conceptual Review of Applications and Next Steps

Yixi Chen, Viktor V Chirikov, Xiaocong L Marston, Jingang Yang, Haibo Qiu, Jianfeng Xie, Ning Sun, Chengming Gu, Peng Dong, Xin Gao
2020 Journal of health economics and outcomes research  
Through a conceptualized example, the objective of this review is to highlight the capabilities and limitations of machine learning (ML) applications to P-HEOR and to contextualize the potential opportunities  ...  Latest methodology developments on bias and confounding control in ML applications to precision medicine are also summarized.  ...  value assessment to high-quality real-world data.  ... 
doi:10.36469/jheor.2020.12698 pmid:32685596 pmcid:PMC7299485 fatcat:bsh7pcivj5cqtgy5xxwcshmcpe

Multidimensional Integrative Genomics Approaches to Dissecting Cardiovascular Disease

Douglas Arneson, Le Shu, Brandon Tsai, Rio Barrere-Cain, Christine Sun, Xia Yang
2017 Frontiers in Cardiovascular Medicine  
To address this challenge, there have been numerous efforts to develop integrative genomics methods that can leverage multidimensional information from diverse data types to derive comprehensive molecular  ...  Clustering/dimensionality reduction-based approaches have the capacity to transform different data types into a common data space, thus facilitating downstream integration.  ...  leveraging information from multiple data types to predict a clinical outcome (92) .  ... 
doi:10.3389/fcvm.2017.00008 pmid:28289683 pmcid:PMC5327355 fatcat:ma3grswnsfdqfgmjga7cr4gjoe

Radiomics and radiogenomics for precision radiotherapy

Jia Wu, Khin Khin Tha, Lei Xing, Ruijiang Li
2018 Journal of Radiation Research  
Furthermore, these imaging-derived phenotypes can be linked with genomic data, i.e. radiogenomics, in order to understand their biological underpinnings or further improve the prediction accuracy of clinical  ...  We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy.  ...  High performance computational tools such as GPU [19] may be leveraged to process the images in order to mitigate various artifacts for radiomics analysis.  ... 
doi:10.1093/jrr/rrx102 pmid:29385618 pmcid:PMC5868194 fatcat:jgwi4xhp3zcvvg7tzeoubyter4

New Ways to Manage Pandemics: Using Technologies in the Era of COVID-19, a Narrative Review

Ali Khaleghi, Mohammad Reza Mohammadi, Gila Pirzad Jahromi, Hadi Zarafshan
2020 Iranian Journal of Psychiatry  
Method: This nonsystematic literature review was conducted on different technologies and their impact and applications in the COVID-19 epidemic using proper search keywords on the PubMed, Google Scholar  ...  However, main challenges still need to be addressed for obtaining the full capacities of the technologies to support health care systems.  ...  for big data aggregation, and result in difficulty of swift online application of deep merging.  ... 
doi:10.18502/ijps.v15i3.3816 pmid:33193772 pmcid:PMC7603586 fatcat:umxhwox4anhmvis7xvla3iskh4

A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions

Shan Ren, Yingfeng Zhang, Yang Liu, Tomohiko Sakao, Donald Huisingh, Cecilia M.V.B. Almeida
2019 Journal of Cleaner Production  
To provide theoretical foundations for the research community to further develop scientific insights in applying big data analytics to smart manufacturing, it is necessary to summarize the existing research  ...  However, according to the literature, big data analytics and smart manufacturing were individually researched in academia and industry.  ...  Acknowledgements The authors would like to acknowledge the financial supports of National Science Foundation of China (51675441), the Fundamental Research Funds for the Central Universities (3102017jc04001  ... 
doi:10.1016/j.jclepro.2018.11.025 fatcat:qggzourcsbac5eq45kajzlc4ay

The Scope, Methods and Applications of Biomedical Data Mining

Trudie Steyn, Nico Martins
2022 Journal of Biomedical and Sustainable Healthcare Applications  
The objectives of this study are to help biomedical researchers to attain intuitive and clear appreciative of the applications of data-mining technologies on biomedical BD to enhance to creation of biomedical  ...  In that case, DM has novel merits in biomedical Big Data (BD) studies, mostly in large-scale biomedical datasets.  ...  This approach has a broad range of applications, and the link between clusters is simple to discover; nevertheless, it has a high temporal complexity.  ... 
doi:10.53759/0088/jbsha202202003 fatcat:bmypbiyozrduhj2fi7hay4ugq4

Radiogenomics for Precision Medicine With A Big Data Analytics Perspective

Andreas S. Panayides, Marios Pattichis, Stephanos Leandrou, Costas Pitris, Anastasia Constantinidou, Constantinos S. Pattichis
2019 IEEE journal of biomedical and health informatics  
The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine.  ...  challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.  ...  Big data programming models and deep learning [16] paradigms are instrumental to radiogenomics research, not only to leverage computationally intensive tasks, but most importantly to contribute to new  ... 
doi:10.1109/jbhi.2018.2879381 pmid:30596591 fatcat:rqmjhmdmr5h3rdaody264ogs24

Guest Editorial for Selected Papers from BIOKDD 2018 and DMBIH 2018

Da Yan, Xin Gao, Samah J. Fodeh, Jake Y. Chen
2020 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
the TCBB staff for the supported to make this special issue possible.  ...  As guest editors of this special issue, we would like to thank the contributing authors, BIOKDD 2018 and DMBIH 2018 program committee, the TCBB reviewers who reviewed papers in this special issue, and  ...  Another interesting observation is that 5 out of the 11 accepted works adopt deep learning, including novel applications over biological data in addition to the more straightforward applications over clinical  ... 
doi:10.1109/tcbb.2020.3020443 fatcat:gaif2gykifg3ncqigqov6djfam

An integrative systems biology approach for precision medicine in diabetic kidney disease

Skander Mulder, Habib Hamidi, Matthias Kretzler, Wenjun Ju
2018 Diabetes, obesity and metabolism  
molecular pathways in DKD and led to the development of candidate prognostic molecular biomarkers.  ...  Early identification of patients at high risk for progression and individualizing therapies have the potential to mitigate kidney complications due to diabetes.  ...  identify patients at high risk of progression (prognostic markers); and to predict the patients' responses to treatment (predictive markers).  ... 
doi:10.1111/dom.13416 fatcat:2bt6aav7nnd7nkxa3gugqqu52y

Decision Support Systems in Oncology

Seán Walsh, Evelyn E.C. de Jong, Janna E. van Timmeren, Abdalla Ibrahim, Inge Compter, Jurgen Peerlings, Sebastian Sanduleanu, Turkey Refaee, Simon Keek, Ruben T.H.M. Larue, Yvonka van Wijk, Aniek J.G. Even (+4 others)
2019 JCO Clinical Cancer Informatics  
DSSs have many stakeholders-clinicians, medical directors, medical insurers, patient advocacy groups-and are a natural consequence of big data in health care.  ...  A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data-clinical, imaging, biologic, genetic  ...  The next step is application, which leverages this knowledge to enhance decision making.  ... 
doi:10.1200/cci.18.00001 pmid:30730766 pmcid:PMC6873918 fatcat:buikakpfqjebtlv7irxauehgey
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