Discovering Sequential Patterns in a UK General Practice Database
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.
Cite
@article{arxiv.1307.1411,
title = {Discovering Sequential Patterns in a UK General Practice Database},
author = {Jenna Reps and Jonathan M. Garibaldi and Uwe Aickelin and Daniele Soria and Jack E. Gibson and Richard B. Hubbard},
journal= {arXiv preprint arXiv:1307.1411},
year = {2013}
}
Comments
2012 IEEE-EMBS International Conference on Biomedical and Health Informatics, pp 960-963, 2012