Abstract
Sepsis is a significant cause of mortality and morbidity. There are now aggressive goal oriented treatments that can be used to help patients suffering from sepsis. By predicting which patients are more likely to develop sepsis, early treatment can potentially reduce their risks. However, diagnosing sepsis is difficult since there is no “standard” presentation, despite many published definitions of this condition.
In this work, data from a large observational cohort of patients – with variables collected at varying time periods – are observed in order to determine whether sepsis develops or not. A cluster analysis approach is used to form groups of correlated datapoints. This sequence of datapoints is then categorized on a per person basis and the frequency of transitioning from one grouping to another is computed. The result is posed as a Markov model which can accurately estimate the likelihood of a patient developing sepsis. A discussion of the implications and uses of this model is presented.
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Gwadry-Sridhar, F., Bauer, M., Lewden, B., Hamou, A. (2011). A Markov Analysis of Patients Developing Sepsis Using Clusters. In: Riaño, D., ten Teije, A., Miksch, S., Peleg, M. (eds) Knowledge Representation for Health-Care. KR4HC 2010. Lecture Notes in Computer Science(), vol 6512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18050-7_7
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DOI: https://doi.org/10.1007/978-3-642-18050-7_7
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