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    A comparative analysis of sepsis digital phenotyping methods

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    Author
    Fedyukova, A; Pires, D; Capurro, D
    Date
    2021-02
    Source Title
    2021 Australasian Computer Science Week Multiconference
    Publisher
    ACM
    University of Melbourne Author/s
    Capurro, Daniel
    Affiliation
    Computing and Information Systems
    Metadata
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    Document Type
    Conference Paper
    Citations
    Fedyukova, A., Pires, D. & Capurro, D. (2021). A comparative analysis of sepsis digital phenotyping methods. 2021 Australasian Computer Science Week Multiconference, pp.1-4. ACM. https://doi.org/10.1145/3437378.3437398.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/260506
    DOI
    10.1145/3437378.3437398
    Abstract
    Health data captured in Electronic health records (EHRs) have enabled the development of computational approaches to improve patient management and treatment, including early diagnosis of severe conditions such as sepsis. The validity of these efforts, however, largely relies on which sepsis definition is used and the quality of the underlying data. Here we tested different sepsis definitions to better understand how phenotyping approaches may impact the classification accuracy of sepsis prediction algorithms. To assess the extent to which sepsis definitions (dis)agree with each other, we have analised a large cohort of patients admitted to the ICU (over 22,000) from MIMIC-IV. Cases were classified as septic and non-septic using the Sepsis-3 definition as a standard and compared with different ICD-10-based sepsis phenotyping criteria. Most of administrative sepsis definitions agreed with each other when identifying positive sepsis cases. At the same time, we identified considerable disagreement between Sepsis-3 and administrative definitions. This discrepancy affected machine learning algorithms’ predictive performance. Two algorithms out of three built on Sepsis-3 outperformed models based on other phenotypes. Experiments demonstrate that phenotype definitions can significantly influence a predictive model performance. This highlights the importance of consistent and validated digital phenotyping criteria.

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