Identifying relationships among drug side-effects using probabilistic association rule mining

Side-effects of medical drugs have important implications for health care. Knowledge of side-effects can help guide appropriate prescription of drugs to patients. The study of drug side-effects is also a rich area of informatics research, for example linking side-effects to genetic variation or finding new uses for drugs by comparative analysis of side-effects. This thesis contributes to the latter area of research by proposing a method to determine associations between side-effects. The problem is cast in the form of a merged basket analysis problem. A modified version of association rule mining together with the use of hierarchical terminology is employed to rank potential associations between side-effects. Results are validated by comparison with conventional association rule mining under different assumptions of uncertainty in a real world dataset. The proposed approach is general enough to be applicable to a range of problems where observations are probabilistic.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Computer Science (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2014

Author dc:creator

• Rajendran, Rajasekar

Advisor dc:contributor.advisor

• Deendayal, Dinakarpandian

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/10355/45647

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/45647