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.
CITATION STYLE
Allaart, C. G., Mondrejevski, L., & Papapetrou, P. (2019). FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance. In IFIP Advances in Information and Communication Technology (Vol. 559, pp. 139–151). Springer New York LLC. https://doi.org/10.1007/978-3-030-19823-7_11
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