# Identifying Patient Groups based on Frequent Patterns of Patient Samples

## TL;DR

This paper presents a method to identify meaningful patient groups using limited expert input by analyzing frequent patterns in patient care pathways, aiding healthcare insights.

## Contribution

It introduces a novel approach that leverages frequent pattern mining to efficiently group patients based on minimal expert-provided samples.

## Key findings

- Achieved F1-score of 0.7 for kidney injury grouping
- Achieved F1-score of 0.6 for diabetes grouping
- Effective with limited expert effort

## Abstract

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.