Extended Data Fig. 3: TPN2.0 exhibited improved unexplained variability than current best practices.

a, An AE model was used to generate a latent representation of the patients, followed by clustering, in order to identify homogeneous patient groups. The model was applied to patient characteristics and lab test values. During this process, a compressed latent representation of the input is extracted. This representation is then grouped by K-means clustering. The optimal number of clusters are determined by using Silhouette scores and Within-Cluster Sum of Squares (WCSS) using the KneeLocator method. Subsequently, the variance of each TPN component within each cluster was calculated for both TPN2.0 and the actual prescriptions. b, The mean variance across all clusters (weighted by cluster size) is visualized. The scatter plot shows lower mean variances within the patients in the same group for all components in TPN2.0 compared to current best practice.