@article{JMAI4935,
author = {Roseanne Yeung and Enrico Capobianco},
title = {Big data in health and disease: re-processing information for discovery and validation},
journal = {Journal of Medical Artificial Intelligence},
volume = {2},
number = {0},
year = {2019},
keywords = {},
abstract = {A lot has been already said about the emerging role of big data in health and disease. Large scale data efforts are increasingly being undertaken in response to the advent of Personalized and Precision Medicine and in association with both the “omics revolution” and the Electronic Health Records centrality. big data have demonstrated that their complex characteristics bring both strength factors and bottlenecks to research problems widely identified, analyzed and reviewed across many sectors of medicine and public health. As the most significant feature of big data is “variety”, and this implies heterogeneity, our knowledge in complex disease contexts may substantially benefit from the fusion of different data types when a major role is assigned to harmonization and interoperability strategies. We discuss of an example, diabetes.},
issn = {2617-2496}, url = {https://jmai.amegroups.org/article/view/4935}
}