  The availability of large and deep electronic healthcare records (EHR)
datasets has the potential to enable a better understanding of real-world
patient journeys, and to identify novel subgroups of patients. ML-based
aggregation of EHR data is mostly tool-driven, i.e., building on available or
newly developed methods. However, these methods, their input requirements, and,
importantly, resulting output are frequently difficult to interpret, especially
without in-depth data science or statistical training. This endangers the final
step of analysis where an actionable and clinically meaningful interpretation
is needed.This study investigates approaches to perform patient stratification
analysis at scale using large EHR datasets and multiple clustering methods for
clinical research. We have developed several tools to facilitate the clinical
evaluation and interpretation of unsupervised patient stratification results,
namely pattern screening, meta clustering, surrogate modeling, and curation.
These tools can be used at different stages within the analysis. As compared to
a standard analysis approach, we demonstrate the ability to condense results
and optimize analysis time. In the case of meta clustering, we demonstrate that
the number of patient clusters can be reduced from 72 to 3 in one example. In
another stratification result, by using surrogate models, we could quickly
identify that heart failure patients were stratified if blood sodium
measurements were available. As this is a routine measurement performed for all
patients with heart failure, this indicated a data bias. By using further
cohort and feature curation, these patients and other irrelevant features could
be removed to increase the clinical meaningfulness. These examples show the
effectiveness of the proposed methods and we hope to encourage further research
in this field.
