Discovery of association rules in medical data

TLDR

Data mining discovers useful information from large databases and is profitably used across industries, often by identifying association rules that reveal relationships among items. The study applies association‑rule mining to a large medical‑record database to uncover relationships between procedures performed and reported diagnoses. The authors used random sampling to extract association rules from the medical‑record database, following standard data mining concepts. The analysis produced association rules linking procedures to diagnoses.

Abstract

Data mining is a technique for discovering useful information from large databases. This technique is currently being profitably used by a number of industries. A common approach for information discovery is to identify association rules which reveal relationships among different items. In this paper, we use this approach to analyse a large database containing medical-record data. Our aim is to obtain association rules indicating relationships between procedures performed on a patient and the reported diagnoses. Random sampling was used to obtain these association rules. After reviewing the basic concepts associated with data mining, we discuss our approach for identifying association rules and report on the rules generated.