Big Data and Biomedical Informatics: A Challenging Opportunity

Abstract

Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

What Does Big Data Mean for Wearable Sensor Systems?

“…The term big data may also encompass the new analysis methods and technologies required to store and understand big datasets [11]. It has been suggested that four Vs, volume, velocity, variety, and veracity, should be used to describe these qualities of a big dataset [12]; here volume relates to the complexity of the dataset, rather than the memory capacity required to store the dataset, although there is an expectation that both would often be correlated. Obviously, the classification of a dataset as being big data is a relative notion which is determined by the current state-of-the-art in data hosting and analysis techniques.…”

Big Data - Smart Health Strategies

“…For an overview of big data technologies, challenges, and application examples in health and biomedicine, we refer the readers to the Editorial [15] and the contribution of the IMIA Working group "Data Mining and Big Data Analytics" [16] of this IMIA Yearbook.…”

Big Data Technologies

“…More precisely, it is widely agreed that the term "big data" is related to concurrent presence of the V properties, the most important being volume, variety and velocity, 2 but also including veracity and variability. While volume is somehow obvious since such data are "large" in size, variety is related to the different format in which the data are collected, from structured information to text, from images to signals.…”

“…The IT architectures and studies have to deal with privacy and data protection issues, which may hamper the practical implementation of a large data integration plan. 2,21,22 Moreover, the heterogeneous quality of the data that may be integrated and collected can lead to overconfident conclusions. 23 Finally, the reproducibility of the results obtained may need to be carefully assessed.…”

“…23 Finally, the reproducibility of the results obtained may need to be carefully assessed. 2 In summary, the issue of dealing with big data is certainly a hot topic in health care. This requires proper understanding of the issues and case studies to assess the real benefits that the new technologies may provide.…”