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DCS Evaluation - Cluster Analysis of DCS
Ozyigit, T.; Egi, M.S. (2017). DCS Evaluation - Cluster Analysis of DCS, in: Balestra, C. et al. The science of diving. Things your instructor never told you. pp. [214-225]
In: Balestra, C.; Germonpré, P. (2017). The science of diving. Things your instructor never told you. Lambert Academic Publishing/Éditions Acrodacrolivres: Villers-la-Ville. ISBN 978-2-512007-36-4. [262] pp.
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  • Ozyigit, T.
  • Egi, M.S.
Abstract
    By its nature, Decompression Sickness (DCS) can manifest itself in many various forms. Different organs and body functions can be affected by free inert gas in tissues and circulation. As a result complex presentations of the disease, from mild to severe, are observed in many cases. The treatment of DCS is currently based on recompression therapy in hyperbaric chambers. The pressure/time combination of the treatment is partly dependent on the type of DCS. Although expert medical doctors may apply custom treatments, in remote areas such as petroleum and natural gas platforms Diver Medical Technicians (DMT) initiate recompression treatment immediately. After evaluating the type of DCS according to the observed signs and symptoms, standard recompression procedures such as US Navy Treatment Tables are applied. In order to apply the optimum treatment table, classification of DCS is useful. Existing classifications of DCS are based on expert opinion and experience. Some of these classifications try to create a link with the (proposed) pathophysiology of the DCS; however, other, different, possible classifications of DCS have been suggested. Another way to classify DCS is by using multivariate statistical methods. A suitable statistical method for DCS classification is cluster analysis. Cluster analysis is the generic name for a wide variety of procedures that can be used to create a classification. More specifically, a clustering method is a multivariate statistical procedure that starts with a data set containing information about a sample of entities and attempts to reorganize these entities into relatively homogenous groups. The main idea is to empirically group DCS patients according to their observed signs and symptoms. In this way an objective classification of DCS for assisting expert knowledge is possible.
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