Drug interactions are interweaving effects between two or more drugs that can have desirable or harmful effects on patients health. In this thesis we for searched harmful drug interactions.
Our approach is based on two machine learning algorithms for association rule mining. We use two given hierarchies, one for drugs (ATC), the other for diseases (ICD), and one proprietary interaction database LexiComp.
A generalized association rule algorithm tries to find rules that contain basic elements as well as elements from given hierarchies.
The second algorithm uses high-utility pattern mining. The utility function was designed to use statistical information from both the data and the hierarchies.
Algorithms were tested on artificial data and on a dataset from University Medical Centre Ljubljana.
Detected rules were reviewed, analyzed, commented and evaluated by pharmacists. The results are promising as several interesting new rules and patterns are detected.