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Attributes for causal inference in electronic healthcare databases

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)

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)
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)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Daniel Soria
Date Deposited: 08 Dec 2022 15:23 UTC
Last Modified: 05 Nov 2024 13:04 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/98896 (The current URI for this page, for reference purposes)

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