1 research outputs found
Identifying Patient Groups based on Frequent Patterns of Patient Samples
Grouping patients meaningfully can give insights about the different types of
patients, their needs, and the priorities. Finding groups that are meaningful
is however very challenging as background knowledge is often required to
determine what a useful grouping is. In this paper we propose an approach that
is able to find groups of patients based on a small sample of positive examples
given by a domain expert. Because of that, the approach relies on very limited
efforts by the domain experts. The approach groups based on the activities and
diagnostic/billing codes within health pathways of patients. To define such a
grouping based on the sample of patients efficiently, frequent patterns of
activities are discovered and used to measure the similarity between the care
pathways of other patients to the patients in the sample group. This approach
results in an insightful definition of the group. The proposed approach is
evaluated using several datasets obtained from a large university medical
center. The evaluation shows F1-scores of around 0.7 for grouping kidney injury
and around 0.6 for diabetes