Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On Classifying Sepsis Heterogeneity in the ICU: Insight Using Machine
  Learning   Current machine learning models aiming to predict sepsis from Electronic
Health Records (EHR) do not account for the heterogeneity of the condition,
despite its emerging importance in prognosis and treatment. This work
demonstrates the added value of stratifying the types of organ dysfunction
observed in patients who develop sepsis in the ICU in improving the ability to
recognise patients at risk of sepsis from their EHR data. Using an ICU dataset
of 13,728 records, we identify clinically significant sepsis subpopulations
with distinct organ dysfunction patterns. Classification experiments using
Random Forest, Gradient Boost Trees and Support Vector Machines, aiming to
distinguish patients who develop sepsis in the ICU from those who do not, show
that features selected using sepsis subpopulations as background knowledge
yield a superior performance regardless of the classification model used. Our
findings can steer machine learning efforts towards more personalised models
for complex conditions including sepsis.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: physics
27
The categories of this paper are: comp-gas
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The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph