title: Transforming big data into computational models for personalized medicine and health care.
abstract: Health care systems generate a huge volume of different types of data. Due to the complexity and challenges inherent in studying medical information, it is not yet possible to create a comprehensive model capable of considering all the aspects of health care systems. There are different points of view regarding what the most efficient approaches toward utilization of this data would be. In this paper, we describe the potential role of big data approaches in improving health care systems and review the most common challenges facing the utilization of health care big data.
authors.affiliation: Emergency Medicine Department, University of Michigan, Ann Arbor, Michigan, USA; University of Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bio-informatics, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: S M
authors.initials: SM
authors.lastname: Reza Soroushmehr
authors.affiliation: Emergency Medicine Department, University of Michigan, Ann Arbor, Michigan, USA; University of Michigan Center for Integrative Research in Critical Care (MCIRCC), University of Michigan, Ann Arbor, Michigan, USA; Department of Computational Medicine and Bio-informatics, University of Michigan, Ann Arbor, Michigan, USA.
authors.forename: Kayvan
authors.initials: K
authors.lastname: Najarian
mesh_terms: D019295:Computational Biology; D003627:Data Interpretation, Statistical; D057225:Data Mining; D016208:Databases, Factual; D003695:Delivery of Health Care / Q000458:organization & administration / Q000592:standards*; D006801:Humans; D066230:Patient-Specific Modeling; D057285:Precision Medicine
keywords: big data; challenges; computational method; health care system; personalized medicine