Data science, learning, and applications to biomedical and health sciences

• Published in: Annals of the New York Academy of Sciences 2017-01, Vol.1387 (1), p.5-11
• Main Authors: Adam, Nabil R., Wieder, Robert, Ghosh, Debopriya
• Format: Article
• Language: English
• Subjects: Access to Information; analytics; Animals; Big Data; Biomedical Research - instrumentation; Biomedical Research - methods; Biomedical Research - trends; Biomedical Technology - instrumentation; Biomedical Technology - methods; Biomedical Technology - trends; Computational Biology - instrumentation; Computational Biology - methods; Computational Biology - standards; Computational Biology - trends; Data Mining - methods; Data Mining - trends; data sharing; Database Management Systems - instrumentation; Database Management Systems - standards; Database Management Systems - trends; Electronic Health Records - instrumentation; Electronic Health Records - standards; Electronic Health Records - trends; Humans; infrastructure; Machine Learning - trends; Self Care - instrumentation; Self Care - methods; Self Care - trends
• ISSN: 0077-8923; 1749-6632
• DOI: 10.1111/nyas.13309

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.