Ontology highlight
ABSTRACT:
SUBMITTER: Ibrahim ZM
PROVIDER: S-EPMC7025363 | biostudies-literature | 2020 Mar
REPOSITORIES: biostudies-literature
Ibrahim Zina M ZM Wu Honghan H Hamoud Ahmed A Stappen Lukas L Dobson Richard J B RJB Agarossi Andrea A
Journal of the American Medical Informatics Association : JAMIA 20200301 3
<h4>Objectives</h4>Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 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 intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR data.<h4>Materials and methods</ ...[more]