Mining unexpected temporal associations: Applications in detecting adverse drug reactions
Date
2008
Authors
Jin, H.
Chen, J.
He, H.
Williams, G.
Kelman, C.
O'Keefe, C.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Journal article
Citation
IEEE Transactions on Information Technology in Biomedicine, 2008; 12(4):488-500
Statement of Responsibility
Huidong (Warren) Jin, Jie Chen, Member, Hongxing He, Graham J. Williams, Chris Kelman and Christine M. O’Keefe
Conference Name
Abstract
In various real-world applications, it is very useful mining unanticipated episodes where certain event patterns unexpectedly lead to outcomes, e.g., taking two medicines together sometimes causing an adverse reaction. These unanticipated episodes are usually unexpected and infrequent, which makes existing data mining techniques, mainly designed to find frequent patterns, ineffective. In this paper, we propose unexpected temporal association rules (UTARs) to describe them. To handle the unexpectedness, we introduce a new interestingness measure, residual-leverage, and develop a novel case-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle the infrequency, we develop a new algorithm MUTARC to find pairwise UTARs. The MUTARC is applied to generate adverse drug reaction (ADR) signals from real-world healthcare administrative databases. It reliably shortlists not only six known ADRs, but also another ADR, flucloxacillin possibly causing hepatitis, which our algorithm designers and experiment runners have not known before the experiments. TheMUTARC performs much more effectively than existing techniques. This paper clearly illustrates the great potential along the new direction of ADR signal generation from healthcare administrative databases.
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Dissertation Note
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Description
Copyright © 2008 IEEE
Link to a related website: https://digital.library.adelaide.edu.au/dspace/bitstream/2440/52483/1/hdl_52483.pdf, Open Access via Unpaywall