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Med Intensiva. 2019 Jan - Feb;43(1):52-57. doi: 10.1016/j.medin.2018.06.002. Epub 2018 Aug 2.

Big data and machine learning in critical care: Opportunities for collaborative research.

[Article in English, Spanish]

Author information

1
Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España. Electronic address: anunezreiz@gmail.com.
2
Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España.
3
MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States.
4
MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences & Technology, MIT, Cambridge, Massachusetts, United States; Division of Clinical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Biomedical Engineering and Telemedicine Group, Biomedical Technology Centre CTB, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain.
5
Department of Surgery, Hospital Clinico San Carlos de Madrid, Instituto de Investigación Sanitaria San Carlos, Universidad Complutense de Madrid, Madrid, Spain.
6
Unidad de Innovación, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain.
7
Life Supporting Technologies, epartamento de Tecnología Fotónica y Bioingeniería, Universidad Politècnica de Madrid, Madrid, Spain.
8
Service of Intensive Care Medicine, Hospital Universitari Joan XXIII, IISPV-URV, Tarragona, Spain.
9
Service of Intensive Care Medicine, Hospital Universitari Joan XXIII, IISPV-URV, Tarragona, Spain; Department of Electronic Engineering, Metabolomics Platform, Rovira i Virgili University, IISPV, Tarragona.
10
Servicio de Medicina Intensiva, Hospital Universitario Clínico San Carlos, Madrid, España. Electronic address: miguel.sanchez@salud.madrid.org.

Abstract

The introduction of clinical information systems (CIS) in Intensive Care Units (ICUs) offers the possibility of storing a huge amount of machine-ready clinical data that can be used to improve patient outcomes and the allocation of resources, as well as suggest topics for randomized clinical trials. Clinicians, however, usually lack the necessary training for the analysis of large databases. In addition, there are issues referred to patient privacy and consent, and data quality. Multidisciplinary collaboration among clinicians, data engineers, machine-learning experts, statisticians, epidemiologists and other information scientists may overcome these problems. A multidisciplinary event (Critical Care Datathon) was held in Madrid (Spain) from 1 to 3 December 2017. Under the auspices of the Spanish Critical Care Society (SEMICYUC), the event was organized by the Massachusetts Institute of Technology (MIT) Critical Data Group (Cambridge, MA, USA), the Innovation Unit and Critical Care Department of San Carlos Clinic Hospital, and the Life Supporting Technologies group of Madrid Polytechnic University. After presentations referred to big data in the critical care environment, clinicians, data scientists and other health data science enthusiasts and lawyers worked in collaboration using an anonymized database (MIMIC III). Eight groups were formed to answer different clinical research questions elaborated prior to the meeting. The event produced analyses for the questions posed and outlined several future clinical research opportunities. Foundations were laid to enable future use of ICU databases in Spain, and a timeline was established for future meetings, as an example of how big data analysis tools have tremendous potential in our field.

KEYWORDS:

Artificial intelligence; Bases de datos clínicos; Big data; Clinical databases; Collaborative work; Datathon; Inteligencia artificial; MIMIC III; Machine learning; Trabajo colaborativo

PMID:
30077427
DOI:
10.1016/j.medin.2018.06.002
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