Premium
Data science, learning, and applications to biomedical and health sciences
Author(s) -
Adam Nabil R.,
Wieder Robert,
Ghosh Debopriya
Publication year - 2017
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1111/nyas.13309
Subject(s) - big data , variety (cybernetics) , data science , data sharing , computer science , medicine , artificial intelligence , data mining , alternative medicine , pathology
The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self‐tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data.