Discovering sequential patterns in a UK general practice database

Reps, Jenna; Garibaldi, Jonathan M.; Aickelin, Uwe; Soria, Daniele; E. Gibson, Jack E.; Hubbard, Richard B.

Abstract

The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowing the implementation of preventative actions. In this paper sequential rule mining is applied to a General Practice database to find rules involving a patients age, gender and medical history. By incorporating these rules into current health-care a patient can be highlighted as susceptible to a future illness based on past or current illnesses, gender and year of birth. This knowledge has the ability to greatly improve health-care and reduce health-care costs.

Citation

Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., E. Gibson, J. E., & Hubbard, R. B. Discovering sequential patterns in a UK general practice database. Presented at 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics

• Conference Name: 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics
• End Date: Jan 7, 2012
• Publication Date: Jun 7, 2012
• Deposit Date: Jul 18, 2013
• Publicly Available Date: Jul 18, 2013
• Peer Reviewed: Peer Reviewed
• Keywords: Learning; Computational Engineering, Finance, and Science; Applications
• Public URL: https://nottingham-repository.worktribe.com/output/1009185
• Additional Information: Copyright 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.