Abstract
In the last few years, there has been a growing expectation created about the analysis of large amounts of data often available in organizations, which has been both scrutinized by the academic world and successfully exploited by industry. Nowadays, two of the most common terms heard in scientific circles are Big Data and Deep Learning. In this double review, we aim to shed some light on the current state of these different, yet somehow related branches of Data Science, in order to understand the current state and future evolution within the healthcare area. We start by giving a simple description of the technical elements of Big Data technologies, as well as an overview of the elements of Deep Learning techniques, according to their usual description in scientific literature. Then, we pay attention to the application fields that can be said to have delivered relevant real-world success stories, with emphasis on examples from large technology companies and financial institutions, among others. The academic effort that has been put into bringing these technologies to the healthcare sector are then summarized and analyzed from a twofold view as follows: first, the landscape of application examples is globally scrutinized according to the varying nature of medical data, including the data forms in electronic health recordings, medical time signals, and medical images; second, a specific application field is given special attention, in particular the electrocardiographic signal analysis, where a number of works have been published in the last two years. A set of toy application examples are provided with the publicly-available MIMIC dataset, aiming to help the beginners start with some principled, basic, and structured material and available code. Critical discussion is provided for current and forthcoming challenges on the use of both sets of techniques in our future healthcare.
Highlights
We are very likely at a crossroads today with respect to the role that information analysis is given worldwide and in our everyday lives
When the Convolutional Neural Networks (CNN) was trained with highly imbalanced data, the accuracy of the CNN reduced to 89.07% and 89.3%
We have completed an exploration of the current state of Big Data (BD) and Deep Learning (DL) with the objective of providing a snapshot of these two branches of Data Science
Summary
We are very likely at a crossroads today with respect to the role that information analysis is given worldwide and in our everyday lives. Everyone is familiar with several terms which come from the so-called Data Science [1], as they are continuously flooding the news, videos, courses, webs, and social media. We get the feeling that the awareness of organizations and companies is growing in terms of the relevance that they perceive from the large amounts of information sources currently available in and around them [2]. We suggest that successful understanding of the data exploitation keys can be equivalent to successful entrepreneurship, business, or management environments, as far as we were able to smartly apply it to our fields of expertise [3].
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