_:n8798c06f26af4e279b9770aef7c41418xb43
<http://schema.org/description>
"Grouping patients meaningfully can give insights about the different types of\npatients, their needs, and the priorities. Finding groups that are meaningful\nis however very challenging as background knowledge is often required to\ndetermine what a useful grouping is. In this paper we propose an approach that\nis able to find groups of patients based on a small sample of positive examples\ngiven by a domain expert. Because of that, the approach relies on very limited\nefforts by the domain experts. The approach groups based on the activities and\ndiagnostic/billing codes within health pathways of patients. To define such a\ngrouping based on the sample of patients efficiently, frequent patterns of\nactivities are discovered and used to measure the similarity between the care\npathways of other patients to the patients in the sample group. This approach\nresults in an insightful definition of the group. The proposed approach is\nevaluated using several datasets obtained from a large university medical\ncenter. The evaluation shows F1-scores of around 0.7 for grouping kidney injury\nand around 0.6 for diabetes."
<https://deepai.org/profile/mark-hoogendoorn>