Discovering Sequential Patterns in a UK General Practice Database
This work addresses predictive healthcare for patients by identifying illness patterns, though it appears incremental as it applies existing methods to medical data.
The paper applied sequential rule mining to a UK General Practice database to predict future illnesses based on patient age, gender, and medical history, aiming to enable preventative actions and improve healthcare.
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.