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.

Heterogeneous Data and Big Data Analytics

“…In addition, there are also problems of missing values and impurity in the high-volume data. Since data quality determines information quality, which will eventually affect the decision-making process, it is critical to develop efficient big data cleansing approaches to improve data quality for making accurate and effective decisions [8].…”

Aligning information technologies with evidence-based health-care activities: A design and evaluation framework

“…Attempts to define EHRs are generally either too vague to be meaningfully distinguished from other tools or rely on decomposing the system into its functional components, offloading the burden of definitional clarity onto the tools or systems of which they are composed (Jha et al, 2009;Tang & McDonald, 2006). The same problem exists in relation to other HIT systems (Tang & McDonald, 2006), including all new and emerging health informatics tools for the age of big data (Andreu-Perez, Poom, Merrifield, Wong, & Yang, 2015;Fang, Pouyanfar, Yang, Chen, & Iyengar, 2016;Tresp et al, 2016). As a result of this lack of clarity surrounding HITs, it is naturally difficult to understand exactly in what sense HITs can support or improve the quality of EBHC activities.…”

ICT: Health’s Best Friend and Worst Enemy?

“…Next-generation medical check-ups could benefit from the many types of health care data (Stylianou and Talias, 2017), including (Fang et al, 2016): i) human-generated (e.g., notes, email, and paper documents);…”