Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Mining Adverse Drug Events Using Multiple Feature Hierarchies and Patient History Windows
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: 19th IEEE International Conference on Data Mining Workshops: Proceedings / [ed] Panagiotis Papapetrou, Xueqi Cheng, Qing He, IEEE, 2019Conference paper, Published paper (Refereed)
Abstract [en]

We study the problem of detecting adverse drug events in electronic health records. The challenge is this work is to aggregate heterogeneous data types involving lab measurements, diagnoses codes and medications codes. An earlier framework proposed for the same problem demonstrated promising predictive performance for the random forest classifier by using only lab measurements as data features. We extend this framework, by additionally including diagnosis and drug prescription codes, concurrently. In addition, we employ the concept of hierarchies of clinical codes as proposed by another work, in order to exploit the inherently complex nature of the medical data. Moreover, we extended the state-of-the-art by considering variable patient history lengths before the occurrence of an ADE event rather than a patient history of an arbitrary length. Our experimental evaluation on eight medical datasets of adverse drug events, five different patient history lengths, and six different classifiers, suggests that the integration of these additional features on the different window lengths provides significant improvements in terms of AUC while employing medically relevant features.

Place, publisher, year, edition, pages
IEEE, 2019.
Series
IEEE International Conference on Data Mining workshops, ISSN 2375-9232, E-ISSN 2375-9259
National Category
Computer Sciences
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-178340DOI: 10.1109/ICDMW.2019.00135ISBN: 978-1-7281-4897-7 (print)ISBN: 978-1-7281-4896-0 (electronic)OAI: oai:DiVA.org:su-178340DiVA, id: diva2:1388408
Conference
19th IEEE International Conference on Data Mining Workshops (ICDMW), Beijing, China, 8–11 November, 2019
Available from: 2020-01-24 Created: 2020-01-24 Last updated: 2020-01-24Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Search in DiVA

By author/editor
Bampa, MariaPapapetrou, Panagiotis
By organisation
Department of Computer and Systems Sciences
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 8 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf