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Search: WFRF:(Allaart Corinne)

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  • Allaart, Corinne, et al. (author)
  • FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance
  • 2019
  • In: Artificial Intelligence Applications and Innovations. - Cham : Springer. - 9783030198220 - 9783030198237 ; , s. 139-151
  • Conference paper (peer-reviewed)abstract
    • Adverse drug events (ADEs) are considered to be highly important and critical conditions, while accounting for around 3.7% of hospital admissions all over the world. Several studies have applied predictive models for ADE detection; nonetheless, only a restricted number and type of features has been used. In the paper, we propose a framework for identifying ADEs in medical records, by first applying the Boruta feature importance criterion, and then using the top-ranked features for building a predictive model as well as for clustering. We provide an experimental evaluation on the MIMIC-III database by considering 7 types of ADEs illustrating the benefit of the Boruta criterion for the task of ADE detection.
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Type of publication
conference paper (1)
Type of content
peer-reviewed (1)
Author/Editor
Papapetrou, Panagiot ... (1)
Allaart, Corinne (1)
Mondrejevski, Lena (1)
University
Stockholm University (1)
Language
English (1)
Research subject (UKÄ/SCB)
Natural sciences (1)
Year
2019 (1)

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