Open Access Open Access  Restricted Access Subscription or Fee Access

Survey on Effective Signaling of Adverse Drug Reactions in Health Database

A.K. Selvanayaki

Abstract


Adverse drug reactions, concerned with unintended responses to a medicinal product. The phrase “responses to a medicinal product” means that a causal relationship between a medicinal product and an adverse event is at least a reasonable possibility. Various algorithms which are used to signal adverse drug reactions are considered. The Mining Unexpected Temporal Association Rules (MUTAR) algorithm is proposed for generating Unexpected Temporal Association Rules without giving the antecedent or the consequent. Mining Unexpected Temporal Association Rules given the Consequent (MUTARC) Algorithm used to shortlist a medicine when symptom is given. A new interestingness measure, residual-leverage is introduced to handle unexpectedness. MUTARA (Mining Unexpected Temporal Association Rule Antecedent) Algorithm is used to shortlist symptoms when medicine is given. To handle the unexpectedness a new interestingness measure, unexpected-leverage is introduced and a user-based exclusion technique is given for its calculation.

Keywords


Adverse Drug Reaction, Association rules, Mining methods and algorithms, MUTARA and MUTARC, Signal detection, Unexpected Temporal Association Rules

Full Text:

PDF

References


L. Cao, C. Zhang, Y. Zhao, P.S. Yu, and G. Williams, “DDDM2007: Domain Driven Data Mining,” ACM SIGKDD, vol. 9, no. 2, pp. 84-86, 2007.

The ICH Expert Working Group, “Post-Approval Safety Data Management: Definitions and Standards for Expedited Reporting,” Nov. 2003.

Huidong Jin, Jie Chen, Hongxing He, Graham Williams, Chris Kelman, Christine M. O’Keefe, "Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions," data mining journal IEEE Transactions on Knowledge and Data Engineering, March 6, 2005.

G.I. Webb, “Efficient Search for Association Rules,” Proc. ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD ’00), pp. 99-107, 2000.

H. Jin, J. Chen, H. He, C. Kelman, D. McAullay, and C.M. O’Keefe, “Signalling Potential Adverse Drug Reactions from Multiple Administrative Health Data,” Proc. Mining Multiple Information Sources (MMIS ’08), pp. 9-17, Aug. 2008.

Geoffrey I. Webb, “OPUS: An Efficient Admissible Algorithm for Unordered Search,” webb@deakin.edu.au.1993.

H. Jin, J. Chen, C. Kelman, H. He, D.McAullay, C.M. O’Keefe,” Mining Unexpected Associations for Signalling Potential Adverse Drug Reactions from Administrative Health Databases,” PAKDD 2006; pp. 867-876, 2006.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.