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Grouping patients meaningfully can give insights about the different types ofpatients, their needs, and the priorities. Finding groups that are meaningfulis however very challenging as background knowledge is often required todetermine what a useful grouping is. In this paper we propose an approach thatis able to find groups of patients based on a small sample of positive examplesgiven by a domain expert. Because of that, the approach relies on very limitedefforts by the domain experts. The approach groups based on the activities anddiagnostic/billing codes within health pathways of patients. To define such agrouping based on the sample of patients efficiently, frequent patterns ofactivities are discovered and used to measure the similarity between the carepathways of other patients to the patients in the sample group. This approachresults in an insightful definition of the group. The proposed approach isevaluated using several datasets obtained from a large university medicalcenter. The evaluation shows F1-scores of around 0.7 for grouping kidney injuryand around 0.6 for diabetes.

Identifying Patient Groups based on Frequent Patterns of Patient Samples