Fig. 6: TPN2.0 by PI-transformer enables physician-in-the-loop recommendations that adhere to pharmacist guidelines.

a, A PI-transformer was developed to cluster TPN compositions over time. To predict TPN composition at time t (Ŷ_t) for patient P_n, future data (X_t+i) is masked to prevent information leakage. The model employs a positional encoder for daily TPN to generate latent representations that the decoder combines with previous TPN data (Y_t−i) to predict Ŷ_t. In pretraining, teacher forcing is used; in fine-tuning, ‘inference as training’ is applied as the decoder autoregressively processes previous predictions. During inference, the model predictions could also be replaced by actual prescriptions if needed. The predictions are further utilized to calculate TPN characteristics. b, TNP2.0 recommendations comply with pharmacist guidelines and rules. These computed values, together with ten pharmacist guidelines/physical expectations (Supplementary Table 2)—including osmolarity, dextrose concentrations and calcium phosphate solubility limits—are integrated into boundary condition losses to enforce clinical standards. TPN2.0 with PI-transformer exhibited the fewest violations among all algorithms tested (n = 79,790 prescriptions from 5,913 patients). c, The performance of TPN2.0 improves with increased physician intervention. Simulated interventions, in which 10% of the TPN2.0 recommendations that are least consistent with actual prescriptions are replaced by the actual prescriptions’ values in the decoder, further enhance performance. At 0% intervention, the model also outperforms the baseline teacher forcing method. This analysis mimics real-world scenarios where physicians modify AI recommendations, and shows that closer collaboration between AI and clinicians enhances model accuracy. Data are presented as mean values ± s.e.m. d, In one illustrative case, the model’s zinc prediction of 362 mcg kg⁻¹ on day 1 was modified to 200 mcg kg⁻¹ according to the actual prescription. The gray area represents the distribution of all zinc values in the data. After the intervention, the model maintained a zinc of 200 mcg kg−1 for the following 8 days, consistent with the actual prescriptions. Subsequently, the prediction shifted back to approximately 350 mcg kg⁻¹—a change that was later followed by the physicians. This indicates the ability of the model to balance clinical judgment with the data-driven approach.