TY - JOUR
T1 - Mining unexpected temporal associations
T2 - Applications in detecting adverse drug reactions
AU - Jin, Huidong
AU - Chen, Jin
AU - He, Hongxing
AU - Williams, Graham J.
AU - Kelman, Chris
AU - O'Keefe, Christine M.
PY - 2008/7
Y1 - 2008/7
N2 - 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. The MUTARC 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.
AB - 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. The MUTARC 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.
KW - Adverse drug reaction (ADR)
KW - Data mining
KW - Healthcare administrative databases
KW - Pharmacovigilance
KW - Unanticipated episode
KW - Unexpected temporal association
UR - http://www.scopus.com/inward/record.url?scp=48449095979&partnerID=8YFLogxK
U2 - 10.1109/TITB.2007.900808
DO - 10.1109/TITB.2007.900808
M3 - Article
SN - 1089-7771
VL - 12
SP - 488
EP - 500
JO - IEEE Transactions on Information Technology in Biomedicine
JF - IEEE Transactions on Information Technology in Biomedicine
IS - 4
ER -