Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing ac-curate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.

Automated summary

This study utilized k-means methods to subtype opioid use trajectories from EHR data and interpreted the resulting subtypes using decision trees. Finally, the discussion focused on incorporating these subtypes as features in static machine learning models for predicting opioid overdose and adverse events.