Deep Learning and Big Data in Healthcare: A Double Review for Critical Beginners

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.

A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description

“…The idea of combining MCA with bootstrap resampling and Parzen windows was first proposed by Corral-De-Witt et al [42], in the context of emergency analysis from alert recordings in 911 units, and that methodology was extended here by stabilizing the resampling method and leveraging kernel methods and domain descriptions in order to give improved confidence volumes. From a Data Science point of view, two highly recommendable directions to cover in hospitality and CRM analysis are the inclusion of machine learning methods providing knowledge discovery in labeled data, such as the Information Variable Identifier method [53] or the use of nonlinear mappings to embeddings, either to higher-dimensional feature spaces (in terms of kernel methods) or to lower-dimensional embedding spaces (in the context of autoencoders and deep learning) [24,62].…”

Deep Learning Algorithms based Fingerprint Authentication: Systematic Literature Review

“…Beside biometrics, DL have witnessed tremendous applications in various domain such as computer vision, remote sensing, natural language processing, education, etc. [16]. The focus of the study is biometrics.…”

A Resampling Univariate Analysis Approach to Ovarian Cancer From Clinical and Genetic Data

“…Several applications and solutions in academia and industry fall into areas such as finance, remote sensing, transportation, education, marketing and advertising, or tourism [5]. In addition, Data Science along with Big Data are leading healthcare towards a new era because in the past decades there has been a massive growth in biomedical data, such as genomic sequences, electronic health records (EHR), and biomedical signals and images [6]. These applications include areas like individual disease diagnosis, disease prognosis, disease prevention and prediction, or design of tailored health treatments based on lifestyle [7].…”