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International Conference on Bioinformatics and Biomedical Engineering

IWBBIO 2017: Bioinformatics and Biomedical Engineering pp 15–30Cite as

Machine Learning for Critical Care: An Overview and a Sepsis Case Study

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10208))

Abstract

Biology in general and medicine and healthcare in particular are facing the critical challenge of exponentially increasing data availability. The core of this challenge is putting these data to work through computer-based knowledge extraction methods. In the medical context this could take the form of medical decision support systems for diagnosis, prognosis or general management. Arguably, one of the most data dependent clinical environments is the critical care unit and by extension the whole area of critical care. Fresh approaches to data analysis in critical care are required, and Computational Intelligence and Machine Learning methods have already shown their usefulness in tackling problems in the area. This brief paper aims to be an introduction to the use of such methods in critical care.

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Acknowledgments

This research was partially funded by Spanish TIN2016-79576-R research project and carried out under the Shockomics programme funded under the 7\(^{th}\) Framework Program of the European Union (EU Grant 602706).

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Authors and Affiliations

  1. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya, 08034, Barcelona, Spain

    Alfredo Vellido & Carles Morales

  2. Data Analytics in Medicine, EureCat, Barcelona, Spain

    Vicent Ribas

  3. Critical Care Deparment, Vall d’Hebron University Hospital, Shock, Organ Dysfunction and Resuscitation (SODIR) Research Group, Vall d’ Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, 08035, Barcelona, Spain

    Adolfo Ruiz Sanmartín & Juan Carlos Ruiz-Rodríguez

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  1. Alfredo Vellido
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  2. Vicent Ribas
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  4. Adolfo Ruiz Sanmartín
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  5. Juan Carlos Ruiz-Rodríguez
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Correspondence to Alfredo Vellido .

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  1. Universidad de Granada, Granada, Spain

    Ignacio Rojas

  2. Universidad de Granada, Granada, Spain

    Francisco Ortuño

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Vellido, A., Ribas, V., Morales, C., Ruiz Sanmartín, A., Ruiz-Rodríguez, J.C. (2017). Machine Learning for Critical Care: An Overview and a Sepsis Case Study. In: Rojas, I., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2017. Lecture Notes in Computer Science(), vol 10208. Springer, Cham. https://doi.org/10.1007/978-3-319-56148-6_2

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