  The identification of phenotypes within complex diseases or syndromes is a
fundamental component of precision medicine, which aims to adapt healthcare to
individual patient characteristics. Postoperative delirium (POD) is a complex
neuropsychiatric condition with significant heterogeneity in its clinical
manifestations and underlying pathophysiology. We hypothesize that POD
comprises several distinct phenotypes, which cannot be directly observed in
clinical practice. Identifying these phenotypes could enhance our understanding
of POD pathogenesis and facilitate the development of targeted prevention and
treatment strategies. In this paper, we propose an approach that combines
supervised machine learning for personalized POD risk prediction with
unsupervised clustering techniques to uncover potential POD phenotypes. We
first demonstrate our approach using synthetic data, where we simulate patient
cohorts with predefined phenotypes based on distinct sets of informative
features. We aim to mimic any clinical disease with our synthetic data
generation method. By training a predictive model and applying SHAP, we show
that clustering patients in the SHAP feature importance space successfully
recovers the true underlying phenotypes, outperforming clustering in the raw
feature space. We then present a case study using real-world data from a cohort
of elderly surgical patients. The results showcase the utility of our approach
in uncovering clinically relevant subtypes of complex disorders like POD,
paving the way for more precise and personalized treatment strategies.
