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FISUL: A Framework for Detecting Adverse Drug Events from Heterogeneous Medical Sources Using Feature Importance
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institute, Sweden.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences. Karolinska Institute, Sweden.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: Artificial Intelligence Applications and Innovations: Proceedings / [ed] John MacIntyre, Ilias Maglogiannis, Lazaros Iliadis, Elias Pimenidis, Springer, 2019, p. 139-151Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2019. p. 139-151
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 559
Keywords [en]
Adverse drug events, Feature importance, Predictive models, Clustering
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-177157DOI: 10.1007/978-3-030-19823-7_11ISBN: 978-3-030-19822-0 (print)ISBN: 978-3-030-19823-7 (electronic)OAI: oai:DiVA.org:su-177157DiVA, id: diva2:1379877
Conference
15th IFIP WG 12.5 International Conference, AIAI 2019, Hersonissos, Crete, Greece, May 24–26, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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