Deep latent variable modelling for clinically significant subgroup discovery among transfusion recipients

Transfusion recipients are a heterogeneous group of patients, yet identifying these groups has traditionally relied on human-driven univariate analyses and domain knowledge instead of analyzing multivariate characteristics of individuals. Electronic health records (EHR) combined with unsupervised machine learning enables robust, data-driven way for phenotyping populations, providing finer-grained view on subgroup characteristics.

We introduce an extension to the Variational Autoencoder (VAE) framework and apply the model to EHR data of 19,629 adult transfusion recipients. The latent representation of VAEs approximates a low-dimensional manifold of input data, where patients with similar characteristics are embedded close to one another. The model integrates clustering via a Gaussian Mixture Model (GMM) prior to identify clinically relevant patient subgroups from diagnosis codes, laboratory values and demographics, while simultaneously classifying the type of transfused products. Final clusters are derived using a modified consensus clustering approach.

We identified six patient groups with distinct diagnosis, laboratory, demographic, and transfusion profiles. These clusters, including obstetric, anemic and trauma patients as well as groups with comorbidities, provide a refined characterization of transfusion-related phenotypes, revealing distinctions among subgroups. Our model achieved moderate classification accuracy, with AUROC of 0.879, 0.806 and 0.861, and PR-AUC of 0.448, 0.357 and 0.492 for red blood cells (RBC), plasma and platelets, respectively. Clustering accuracy remains consistent across training and testing.

Deep representation learning identified clinically meaningful subgroups of transfusion recipients and yielded a level of phenotypic resolution that extends beyond prior characterization. The model helps to understand the heterogeneous nature of patients requiring transfusion and provides insights on how different blood product profiles shift cluster assignments. These findings underscore the utility of latent variable modelling for population characterization and suggest potential applications in predicting transfusion demand, supporting targeted patient blood management strategies, and improving blood supply planning. Validation in external cohorts remains unestablished.