Discovering Sequential Patterns in a UK General Practice Database

TL;DR

This paper applies sequential rule mining to a UK General Practice database to identify patterns that predict future illnesses based on patient demographics and medical history, aiming to improve healthcare and reduce costs.

Contribution

It introduces a method for discovering predictive sequential patterns in medical data, integrating patient demographics and history to enhance healthcare decision-making.

Findings

Identified rules linking age, gender, and medical history to future illnesses.

Demonstrated potential for early intervention and prevention.

Showed reduction in healthcare costs through predictive pattern recognition.

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.