Comparing data-mining algorithms developed for longitudinal observational databases
Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limit...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2012
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| Online Access: | https://eprints.nottingham.ac.uk/2036/ |
| _version_ | 1848790710330851328 |
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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 | Longitudinal observational databases have become
a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases. In this paper we investigate the robustness of four recently developed
algorithms that mine longitudinal observational databases by
applying them to The Health Improvement Network (THIN) for
six drugs with well document known negative side effects. Our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior |
| first_indexed | 2025-11-14T18:16:56Z |
| format | Conference or Workshop Item |
| id | nottingham-2036 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:16:56Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-20362020-05-04T20:22:46Z https://eprints.nottingham.ac.uk/2036/ Comparing data-mining algorithms developed for longitudinal observational databases Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation databases are not restricted by many of the limitations associated with the more conventional methods that have been developed for spontaneous reporting system databases. In this paper we investigate the robustness of four recently developed algorithms that mine longitudinal observational databases by applying them to The Health Improvement Network (THIN) for six drugs with well document known negative side effects. Our results show that none of the existing algorithms was able to consistently identify known adverse drug reactions above events related to the cause of the drug and no algorithm was superior 2012 Conference or Workshop Item PeerReviewed Reps, Jenna, Garibaldi, Jonathan M., Aickelin, Uwe, Soria, Daniele, Gibson, Jack E. and Hubbard, Richard B. (2012) Comparing data-mining algorithms developed for longitudinal observational databases. In: UKCI 2012, the 12th Annual Workshop on Computational Intelligence, 5-7 Sept 2012, Edinburgh, Scotland. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6335771 |
| spellingShingle | Reps, Jenna Garibaldi, Jonathan M. Aickelin, Uwe Soria, Daniele Gibson, Jack E. Hubbard, Richard B. Comparing data-mining algorithms developed for longitudinal observational databases |
| title | Comparing data-mining algorithms developed for longitudinal observational databases |
| title_full | Comparing data-mining algorithms developed for longitudinal observational databases |
| title_fullStr | Comparing data-mining algorithms developed for longitudinal observational databases |
| title_full_unstemmed | Comparing data-mining algorithms developed for longitudinal observational databases |
| title_short | Comparing data-mining algorithms developed for longitudinal observational databases |
| title_sort | comparing data-mining algorithms developed for longitudinal observational databases |
| url | https://eprints.nottingham.ac.uk/2036/ https://eprints.nottingham.ac.uk/2036/ |