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DOI: 10.1016/j.medine.2019.06.012
Available online 5 July 2019
Big Data Analysis and Machine Learning in Intensive Care Units
Big Data Analysis y Machine Learning en medicina intensiva
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A. Núñez Reiza,
Corresponding author
anunezreiz@gmail.com

Corresponding author.
, M.A. Armengol de la Hozb,c,d, M. Sánchez Garcíaa
a Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, Spain
b Department of Anesthesia, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
c Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
d Biomedical Engineering and Telemedicine Group, Biomedical Technology Centre CTB, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
This item has received
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Received 05 September 2018. Accepted 21 October 2018
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Table 1. Steps in constructing a Machine Learning system.
Table 2. Aspects of clinical research studied using Big Data Analysis and Machine Learning techniques.
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Abstract

Intensive care is an ideal environment for the use of Big Data Analysis (BDA) and Machine Learning (ML), due to the huge amount of information processed and stored in electronic format in relation to such care. These tools can improve our clinical research capabilities and clinical decision making in the future.

The present study reviews the foundations of BDA and ML, and explores possible applications in our field from a clinical viewpoint. We also suggest potential strategies to optimize these new technologies and describe a new kind of hybrid healthcare-data science professional with a linking role between clinicians and data.

Keywords:
Big Data Analysis
Machine Learning
Artificial intelligence
Secondary electronic health record data analysis
Resumen

La gran cantidad de información que se procesa informáticamente en el entorno de la medicina intensiva la convierte en un campo ideal para el empleo de técnicas conocidas como Big Data Analysis (BDA) y Machine Learning (ML), que pueden permitir en el futuro mejorar nuestra capacidad de investigación clínica y dirigir de manera más precisa las terapias que proporcionamos a nuestros pacientes.

En este artículo se revisan los conceptos fundamentales sobre BDA y ML, y se estudian sus posibles aplicaciones al ámbito de la medicina intensiva, desde un punto de vista del clínico. También se plantean potenciales estrategias para sacar el máximo partido a estas tecnologías emergentes, incluyendo la aparición de un nuevo tipo de profesional sanitario encargado de actuar como enlace entre la parte clínica y la ingeniería de datos.

Palabras clave:
Big Data Analysis
Machine Learning
Inteligencia artificial
Análisis secundario de datos clínicos electrónicos

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