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Survey on Effective Signaling of Adverse Drug Reactions in Health Database


Affiliations
1 Department of Computer Science in Sri Ramkrishna College of Arts and Science for Women, Coimbatore, India
     

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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.
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  • Survey on Effective Signaling of Adverse Drug Reactions in Health Database

Abstract Views: 255  |  PDF Views: 2

Authors

A. K. Selvanayaki
Department of Computer Science in Sri Ramkrishna College of Arts and Science for Women, Coimbatore, India

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.