Predicting Severe Sepsis Using Text from the Electronic Health Record

نویسندگان

  • Phil Culliton
  • Michael Levinson
  • Alice Ehresman
  • Joshua Wherry
  • Jay S. Steingrub
  • Stephen I. Gallant
چکیده

Employing a machine learning approach we predict, up to 24 hours prior, a diagnosis of severe sepsis. Strongly predictive models are possible that use only text reports from the Electronic Health Record (EHR), and omit structured numerical data. Unstructured text alone gives slightly better performance than structured data alone, and the combination further improves performance. We also discuss advantages of using unstructured EHR text for modeling, as compared to structured EHR data.

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عنوان ژورنال:
  • CoRR

دوره abs/1711.11536  شماره 

صفحات  -

تاریخ انتشار 2017