Attributes for causal inference in electronic healthcare databases
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...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/3342/ |
| _version_ | 1848791004778332160 |
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| author | Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. |
| author_facet | Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. |
| author_sort | Reps, Jenna |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T18:21:37Z |
| format | Conference or Workshop Item |
| id | nottingham-3342 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:21:37Z |
| publishDate | 2013 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33422020-05-04T20:20:50Z https://eprints.nottingham.ac.uk/3342/ Attributes for causal inference in electronic healthcare databases Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. 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 Conference or Workshop Item PeerReviewed Reps, Jenna, Garibaldi, Jonathan M., Aickelin, Uwe, Soria, Daniele, Gibson, Jack E. and Hubbard, Richard B. (2013) Attributes for causal inference in electronic healthcare databases. In: CBMS 2013, The 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, 20-22 June 2013, Porto, Portugal. Biomedical Informatics Data Mining http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6627871 |
| spellingShingle | Biomedical Informatics Data Mining Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. Attributes for causal inference in electronic healthcare databases |
| title | Attributes for causal inference in electronic healthcare databases |
| title_full | Attributes for causal inference in electronic healthcare databases |
| title_fullStr | Attributes for causal inference in electronic healthcare databases |
| title_full_unstemmed | Attributes for causal inference in electronic healthcare databases |
| title_short | Attributes for causal inference in electronic healthcare databases |
| title_sort | attributes for causal inference in electronic healthcare databases |
| topic | Biomedical Informatics Data Mining |
| url | https://eprints.nottingham.ac.uk/3342/ https://eprints.nottingham.ac.uk/3342/ |