Elsevier

Medicina Intensiva

Volume 43, Issue 1, January–February 2019, Pages 52-57
Medicina Intensiva

PUESTA AL DÍA EN MEDICINA INTENSIVA: ACTUALIZACIÓN EN METODOLOGÍA EN MEDICINA INTENSIVA
Big data and machine learning in critical care: Opportunities for collaborative researchBig data y machine learning en medicina intensiva: oportunidades del trabajo colaborativo

https://doi.org/10.1016/j.medin.2018.06.002Get rights and content

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.

Resumen

La aparición de los sistemas de información clínica (SIC) en el entorno de los cuidados intensivos brinda la posibilidad de almacenar una ingente cantidad de datos clínicos en formato electrónico durante el ingreso de los pacientes. Estos datos pueden ser empleados posteriormente para obtener respuestas a preguntas clínicas, para su uso en la gestión de recursos o para sugerir líneas de investigación que luego pueden ser explotadas mediante ensayos clínicos aleatorizados. Sin embargo, los médicos clínicos carecen de la formación necesaria para la explotación de grandes bases de datos, lo que supone un obstáculo para aprovechar esta oportunidad. Además, existen cuestiones de índole legal (seguridad, privacidad, consentimiento de los pacientes) que deben ser abordadas para poder utilizar esta potente herramienta.

El trabajo multidisciplinar con otros profesionales (analistas de datos, estadísticos, epidemiólogos, especialistas en derecho aplicado a grandes bases de datos), puede resolver estas cuestiones y permitir utilizar esta herramienta para investigación clínica o análisis de resultados (benchmarking).

Se describe la reunión multidisciplinar (Critical Care Datathon) realizada en Madrid los días 1, 2 y 3 de diciembre de 2017. Esta reunión, celebrada bajo los auspicios de la Sociedad Española de Medicina Intensiva, Crítica y Unidades Coronarias (SEMICYUC) entre otros, fue organizada por el Massachusetts Institute of Technology (MIT), la Unidad de Innovación y el Servicio de Medicina Intensiva del Hospital Clínico San Carlos, así como el grupo de investigación «Life Supporting Technologies» de la Universidad Politécnica de Madrid. Tras unas ponencias de formación sobre big data, seguridad y calidad de los datos, y su aplicación al entorno de la medicina intensiva, un grupo de clínicos, analistas de datos, estadísticos, expertos en seguridad informática de datos realizaron sesiones de trabajo colaborativo en grupos utilizando una base de datos reales anonimizada (MIMIC III), para analizar varias preguntas clínicas establecidas previamente a la reunión.

El trabajo colaborativo permitió establecer resultados relevantes con respecto a las preguntas planteadas y esbozar varias líneas de investigación clínica a desarrollar en el futuro. Además, se sentaron las bases para poder utilizar las bases de datos de las UCI con las que contamos en España, y se estableció un calendario de trabajo para planificar futuras reuniones contando con los datos de nuestras unidades.

El empleo de herramientas de big data y el trabajo colaborativo con otros profesionales puede permitir ampliar los horizontes en aspectos como el control de calidad de nuestra labor cotidiana, la comparación de resultados entre unidades o la elaboración de nuevas líneas de investigación clínica.

Section snippets

Collaborative work

Since it is would be extremely unusual that one individual would have both the clinical training and the computer science background for BDA and ML, teams with professionals across different fields are required, composed of data scientists including statisticians and epidemiologists, clinicians and other biomedical researchers. The coordination of a group with different schedules, the establishment of clear objectives and an effective leadership to manage members with different cultures and who

Databases of critically ill patients

The use of BDA and ML techniques creates a new avenue for clinical research that includes non-traditional, non-academic investigators, especially when large anonymized databases of critical patients are shared under pre-specified data user agreement. The pioneers in this field are Beth Israel Deaconess Medical Center and MIT, whose partnership created the MIMIC database (Medical Information Mart in Intensive Care, http://mimic.physionet.org) for the research community. The MIMIC-III version

Initiatives in Spain

Although adoption of electronic medical records and databases of critically ill patients is still in the growth phase in our country, and not all Units have implemented an integrated digital information system, there are already some initiatives under way.

The Spanish Society of Critical Care (Sociedad Española de Medicina Intensiva, Crítica y Unidades Coronarias, SEMICYUC) has published reference standards for the implementation of Clinical Information Systems (CIS),17 and is in the process of

Critical Care Datathon Madrid

In order to promote the use of BDA and ML in healthcare in our country, the seventh Critical Care Datathon, organized jointly by the MIT Critical Data Group, the HCSC in Madrid and the LifeSTech from UPM, took place on December 1, 2 and 3, 2017 at Impact-Hub co-working space in Madrid. The event brought together clinicians, computer scientists and other professionals interested in health data science. HCSC and LifeSTech-UPM group collaborated in the event diffusion informing participants about

What the future holds and conclusions

We are conceivably at the dawn of a new era in intensive care, in which clinical decision making will increasingly be assisted by computers that perform data integration and analysis. The clinician will therefore have to navigate a specialty of critical care, that harnesses the power of data to individualize care in order to improve population health, where collaborative work with other non-healthcare specialists in the area of data science is essential to leverage all the information that is

Author's contributions

MIMIC III experts: Celi, Armengol, Deliberato, Paik, Pollard, Raffa, Torres.

Technical logistics (servers and database access): Celi, Armengol, Deliberato, Paik, Pollard, Raffa, Torres, Mayol, Chafer, Rey, Gonzalez, Fico, Lombroni, Hernandez, Lopez, Merino, Cabrera, Arredondo.

Preparation and presentation of research questions: Blesa, Martín, Nieto, Martínez, Álvarez, Sánchez, del Pino, Gil, Núñez.

Clinical support for the research questions: Sanchez, Núñez, Álvarez, Martínez, del Pino, Gil, Bodí,

Conflict of interests

The authors declare no conflict of interests.

References (28)

  • Brownlee J. Boosting and AdaBoost for Machine Learning 2016. Available from:...
  • SAS. Natural Language Processing. Available from:...
  • L.A. Celi et al.

    “Big data” in the intensive care unit. Closing the data loop

    Am J Respir Crit Care Med

    (2013)
  • T.J. Iwashyna et al.

    What's so different about big data? A primer for clinicians trained to think epidemiologically

    Ann Am Thorac Soc

    (2014)
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