journal article
Jun 14, 2016
Computational Health Informatics in the Big Data Age
Abstract
The explosive growth and widespread accessibility of digital health data have led to a surge of research activity in the healthcare and data sciences fields. The conventional approaches for health data management have achieved limited success as they are incapable of handling the huge amount of complex data with high volume, high velocity, and high variety. This article presents a comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods. We have summarized the challenges into four Vs (i.e., volume, velocity, variety, and veracity) and proposed a systematic data-processing pipeline for generic big data in health informatics, covering data capturing, storing, sharing, analyzing, searching, and decision support. Specifically, numerous techniques and algorithms in machine learning are categorized and compared. On the basis of this material, we identify and discuss the essential prospects lying ahead for computational health informatics in this big data age.
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References
Details
- Published
- Jun 14, 2016
- Vol/Issue
- 49(1)
- Pages
- 1-36
- License
- View
Authors
Cite This Article
Ruogu Fang, Samira Pouyanfar, Yimin Yang, et al. (2016). Computational Health Informatics in the Big Data Age. ACM Computing Surveys, 49(1), 1-36. https://doi.org/10.1145/2932707
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