Deep learning and big data in healthcare: a double review for critical beginners

Bote Curiel, Luis and Muñoz Romero, Sergio and Gerrero Curieses, Alicia and Rojo Álvarez, José Luis (2019). Deep learning and big data in healthcare: a double review for critical beginners. "Applied Sciences", v. 9 (n. 11); pp. 1-34. ISSN 2076-3417. https://doi.org/10.3390/app9112331.

Description

Title: Deep learning and big data in healthcare: a double review for critical beginners
Author/s:
  • Bote Curiel, Luis
  • Muñoz Romero, Sergio
  • Gerrero Curieses, Alicia
  • Rojo Álvarez, José Luis
Item Type: Article
Título de Revista/Publicación: Applied Sciences
Date: 2019
ISSN: 2076-3417
Volume: 9
Subjects:
Freetext Keywords: Deep learning, Big data, Statistical learning, Healthcare, Electrocardiogram, Databases, MIMIC, Review, Machine learning
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2013-48439-C4-1-RPRINCIPIASUniversidad Rey Juan CarlosProcesado digital no lineal y aprendizaje estadístico con núcleos autocorrelación para aplicaciones en salud
Government of SpainTEC2013-48439-C4-2-RPRINCIPIASUnspecifiedProcesado digital no lineal y aprendizaje estadístico con núcleos autocorrelación para aplicaciones en salud
Government of SpainTEC2016-75161-C2-1-RFINALEUniversidad Rey Juan CarlosInvestigación traslacional y transferencia de un nuevo sistema de electrofisiología cardiaca no inavasiva de alta resolución
Government of SpainTEC2016-75161-C2-2-RFINALEUnspecifiedSistema automático y nueva algoritmia de procesamiento de señales cardíacas para monitorización prolongada mediante aprendizaje estadístico desde la práctica médica habitual
Government of SpainTEC2016-81900-REDTKERMESUniversidad de ValenciaAvances en métodos núcleo para datos estructurados
Government of SpainTEC2015-64835-C3-1-RFEDERUniversidad de AlcaláTecnologías de capa física fiables para comunicaciones sobre redes eléctricas

More information

Item ID: 67129
DC Identifier: https://oa.upm.es/67129/
OAI Identifier: oai:oa.upm.es:67129
DOI: 10.3390/app9112331
Official URL: https://www.mdpi.com/2076-3417/9/11/2331
Deposited by: Memoria Investigacion
Deposited on: 19 May 2021 06:42
Last Modified: 19 May 2021 06:42
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