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      Machine learning in critical care: state-of-the-art and a sepsis case study

      , 1 , 2 , 3 , 1 , 4 , 4

      BioMedical Engineering OnLine

      BioMed Central

      5th International Work-Conference on Bioinformatics and Biomedical Engineering

      26-28 April 2017

      Critical care, Intensive care unit, Machine Learning, Sepsis

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          Abstract

          Background

          Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments.

          Results

          The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU.

          Conclusion

          We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.

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          Most cited references 68

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          The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3).

          Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.
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            APACHE II: a severity of disease classification system.

            This paper presents the form and validation results of APACHE II, a severity of disease classification system. APACHE II uses a point score based upon initial values of 12 routine physiologic measurements, age, and previous health status to provide a general measure of severity of disease. An increasing score (range 0 to 71) was closely correlated with the subsequent risk of hospital death for 5815 intensive care admissions from 13 hospitals. This relationship was also found for many common diseases. When APACHE II scores are combined with an accurate description of disease, they can prognostically stratify acutely ill patients and assist investigators comparing the success of new or differing forms of therapy. This scoring index can be used to evaluate the use of hospital resources and compare the efficacy of intensive care in different hospitals or over time.
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              • Record: found
              • Abstract: not found
              • Article: not found

              The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure

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                Author and article information

                Contributors
                avellido@cs.upc.edu
                Conference
                Biomed Eng Online
                Biomed Eng Online
                BioMedical Engineering OnLine
                BioMed Central (London )
                1475-925X
                20 November 2018
                20 November 2018
                2018
                : 17
                Issue : Suppl 1 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.
                Affiliations
                [1 ]GRID grid.6835.8, Intelligent Data Science and Artificial Intelligence (IDEAI) Research Center, , Universitat Politècnica de Catalunya, ; C. Jordi Girona, 1-3, 08034 Barcelona, Spain
                [2 ]Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Spain
                [3 ]Data Analytics in Medicine, EureCat, Avinguda Diagonal, 177, 08018 Barcelona, Spain
                [4 ]GRID grid.7080.f, 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
                Article
                569
                10.1186/s12938-018-0569-2
                6245501
                30458795
                © The Author(s) 2018

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                5th International Work-Conference on Bioinformatics and Biomedical Engineering
                Granada, Spain
                26-28 April 2017
                Categories
                Research
                Custom metadata
                © The Author(s) 2018

                Biomedical engineering

                sepsis, critical care, intensive care unit, machine learning

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