Reps, Jenna, Garibaldi, Jonathan M., Aickelin, Uwe, Soria, Daniele, Gibson, Jack E., Hubbard, Richard B. (2013) Attributes for causal inference in electronic healthcare databases. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems. . pp. 548-549. IEEE E-ISBN 978-1-4799-1053-3. (doi:10.1109/CBMS.2013.6627871) (KAR id:98896)
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| Official URL: https://doi.org/10.1109/CBMS.2013.6627871 |
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Abstract
Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria. © 2013 IEEE.
| Item Type: | Conference or workshop item (Paper) |
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| DOI/Identification number: | 10.1109/CBMS.2013.6627871 |
| Additional information: | cited By 1 |
| Uncontrolled keywords: | causal inference |
| Subjects: | Q Science > QA Mathematics (inc Computing science) |
| Institutional Unit: | Schools > School of Computing |
| Former Institutional Unit: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
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| Depositing User: | Daniel Soria |
| Date Deposited: | 08 Dec 2022 15:23 UTC |
| Last Modified: | 22 Jul 2025 09:13 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/98896 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Soria, Daniele.
| Creator's ORCID: |
 https://orcid.org/0000-0002-0164-8218
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