Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Duplicate detection in adverse drug reaction surveillance
Data Mining and Knowledge Discovery
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Temporal pattern discovery for trends and transient effects: its application to patient records
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Unexpected Temporal Associations: Applications in Detecting Adverse Drug Reactions
IEEE Transactions on Information Technology in Biomedicine
Guest Editorial: Special Issue on impacting patient care by mining medical data
Data Mining and Knowledge Discovery
Characterizing mammography reports for health analytics
Proceedings of the 1st ACM International Health Informatics Symposium
Characterizing Mammography Reports for Health Analytics
Journal of Medical Systems
Robust discovery of local patterns: subsets and stratification in adverse drug reaction surveillance
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Towards heterogeneous temporal clinical event pattern discovery: a convolutional approach
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Case-based reasoning in comparative effectiveness research
IBM Journal of Research and Development
Towards healthcare business intelligence in long-term care
Computers in Human Behavior
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Large collections of electronic patient records provide a vast but still underutilised source of information on the real world use of medicines. They are maintained primarily for the purpose of patient administration, but contain a broad range of clinical information highly relevant for data analysis. While they are a standard resource for epidemiological confirmatory studies, their use in the context of exploratory data analysis is still limited. In this paper, we present a framework for open-ended pattern discovery in large patient records repositories. At the core is a graphical statistical approach to summarising and visualising the temporal association between the prescription of a drug and the occurrence of a medical event. The graphical overview contrasts the observed and expected number of occurrences of the medical event in different time periods both before and after the prescription of interest. In order to effectively screen for important temporal relationships, we introduce a new measure of temporal association, which contrasts the observed-to-expected ratio in a time period immediately after the prescription to the observed-to-expected ratio in a control period 2 years earlier. An important feature of both the observed-to-expected graph and the measure of temporal association is a statistical shrinkage towards the null hypothesis of no association, which provides protection against highlighting spurious associations. We demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the United Kingdom. The identified patterns include temporal relationships between drug prescriptions and medical events suggestive of persistent and transient risks of adverse events, possible beneficial effects of drugs, periodic co-occurrence, and systematic tendencies of patients to switch from one medication to another.