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Radiogenomics for Precision Medicine With A Big Data Analytics Perspective

Published on Jan 1, 2019in IEEE Journal of Biomedical and Health Informatics4.22
· DOI :10.1109/jbhi.2018.2879381
A. Panayides11
Estimated H-index: 11
(UCY: University of Cyprus),
Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico)
+ 3 AuthorsConstantinos S. Pattichis34
Estimated H-index: 34
(UCY: University of Cyprus)
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Abstract
Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based sub-stratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways towards optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources towards new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this study is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.
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Published on Jan 1, 2019in arXiv: Learning
Alvaro E. Ulloa Cerna , Marios S. Pattichis25
Estimated H-index: 25
(UNM: University of New Mexico)
+ 5 AuthorsBrandon K. Fornwalt13
Estimated H-index: 13
We present an interpretable neural network for predicting an important clinical outcome (1-year mortality) from multi-modal Electronic Health Record (EHR) data. Our approach builds on prior multi-modal machine learning models by now enabling visualization of how individual factors contribute to the overall outcome risk, assuming other factors remain constant, which was previously impossible. We demonstrate the value of this approach using a large multi-modal clinical dataset including both EHR d...