Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Appropriate drug prescription for an increasingly ageing and multi-morbid population can be a challenge for general practitioners. This study uses unsupervised learning methods to identify different types of patient profiles which could inform policymakers and regulators about patterns of drug use, and identify specific clusters of users with unknown drug effects (risk and benefit). Hard and soft clustering methods are proposed to detect patterns of medication use by patients and to estimate the probability of belonging to a certain patient profile. Results showed the presence of expected as well as a surprising patient profile based on fracture risk factors. Challenges associated with unsupervised learning using electronic medical record data are described and an approach for evaluating models in the presence of unlabeled data using internal and external cluster evaluation methods is presented, such that it can be extended to other unsupervised learning applications within healthcare and beyond. To our knowledge, this is the first study proposing cluster analysis for detecting drug utilisation patterns from electronic healthcare records in the routinely-collected SIDIAP database.

Original publication




Conference paper

Publication Date





194 - 198