Fig. 3: TPN2.0 is validated in a second hospital and outperforms baseline Elastic Net models.

a, To validate the model developed at Stanford, we extracted a second TPN dataset from UCSF. To check data consistency, we demonstrated that TPN compositions are associated with the weights of neonates in both sites (Pearson’s R and P value). b, The Stanford team did not have access to UCSF data, and models trained at Stanford were validated independently by the UCSF team. To examine TPN2.0 performance across sites, we report the distance between a prescribed TPN and another expert’s similar TPN order as the gold standard (Methods). We also report the relative difference, which is calculated as the absolute difference between TPN2.0 and the experts’ distances, divided by that of the expert. It is presented as ‘1 − Difference’, where higher values signify higher similarity between experts and TPN2.0 performance. Error bars, s.e. Comparing these distances between the experts’ TPN and TPN2.0 reveals a high correlation at both Stanford (n = 79,790 TPN from 5,913 patients) and UCSF (n = 63,273 TPN from 3,417 patients). The model also outperforms baseline ML (Elastic Net). Data are presented as mean values ± s.e.m. c, At both sites, TPN2.0 shows similar distance to experts for all components (Pearson’s R and P value). Here, lower distances suggest consistency among experts, as seen with components like levocarnitine or multivitamins. In contrast, higher distances reflect lower performance, such as in dextrose and amino acid contents, which are due to greater variability in practice. In contrast, levocarnitine or multivitamins are mostly a binary decision with better defined guidelines. The bands represent 95% confidence intervals.