Chi-Hung Shu

All Publications

• Author Correction: AI-guided precision parenteral nutrition for neonatal intensive care units. Nature medicine Phongpreecha, T., Ghanem, M., Reiss, J. D., Oskotsky, T. T., Mataraso, S. J., De Francesco, D., Reincke, S. M., Espinosa, C., Chung, P., Ng, T., Costello, J. M., Sequoia, J. A., Razdan, S., Xie, F., Berson, E., Kim, Y., Seong, D., Szeto, M. Y., Myers, F., Gu, H., Feister, J., Verscaj, C. P., Rose, L. A., Sin, L. W., Oskotsky, B., Roger, J., Shu, C. H., Shome, S., Yang, L. K., Tan, Y., Levitte, S., Wong, R. J., Gaudillière, B., Angst, M. S., Montine, T. J., Kerner, J. A., Keller, R. L., Shaw, G. M., Sylvester, K. G., Fuerch, J., Chock, V., Gaskari, S., Stevenson, D. K., Sirota, M., Prince, L. S., Aghaeepour, N. 2025

  View details for DOI 10.1038/s41591-025-03691-x

  View details for PubMedID 40205201

• AI-guided precision parenteral nutrition for neonatal intensive care units. Nature medicine Phongpreecha, T., Ghanem, M., Reiss, J. D., Oskotsky, T., Mataraso, S. J., De Francesco, D., Reincke, S. M., Espinosa, C., Chung, P., Ng, T., Costello, J. M., Sequoia, J. A., Razdan, S., Xie, F., Berson, E., Kim, Y., Seong, D., Szeto, M. Y., Myers, F., Gu, H., Feister, J., Verscaj, C. P., Rose, L. A., Sin, L. W., Oskotsky, B., Roger, J., Shu, C. H., Shome, S., Yang, L. K., Tan, Y., Levitte, S., Wong, R. J., Gaudillière, B., Angst, M. S., Montine, T. J., Kerner, J. A., Keller, R. L., Shaw, G. M., Sylvester, K. G., Fuerch, J., Chock, V., Gaskari, S., Stevenson, D. K., Sirota, M., Prince, L. S., Aghaeepour, N. 2025

  Abstract

  One in ten neonates are admitted to neonatal intensive care units, highlighting the need for precise interventions. However, the application of artificial intelligence (AI) in guiding neonatal care remains underexplored. Total parenteral nutrition (TPN) is a life-saving treatment for preterm neonates; however, implementation of the therapy in its current form is subjective, error-prone and resource-consuming. Here, we developed TPN2.0-a data-driven approach that optimizes and standardizes TPN using information collected routinely in electronic health records. We assembled a decade of TPN compositions (79,790 orders; 5,913 patients) at Stanford to train TPN2.0. In addition to internal validation, we also validated our model in an external cohort (63,273 orders; 3,417 patients) from a second hospital. Our algorithm identified 15 TPN formulas that can enable a precision-medicine approach (Pearson's R = 0.94 compared to experts), increasing safety and potentially reducing cost. A blinded study (n = 192) revealed that physicians rated TPN2.0 higher than current best practice. In patients with high disagreement between the actual prescriptions and TPN2.0, standard prescriptions were associated with increased morbidities (for example, odds ratio = 3.33; P value = 0.0007 for necrotizing enterocolitis), while TPN2.0 recommendations were linked to reduced risk. Finally, we demonstrated that TPN2.0 employing a transformer architecture enabled guideline-adhering, physician-in-the-loop recommendations that allow collaboration between the care team and AI.

  View details for DOI 10.1038/s41591-025-03601-1

  View details for PubMedID 40133525

  View details for PubMedCentralID 10593864

• A machine learning approach to leveraging electronic health records for enhanced omics analysis (vol 7, pg 293, 2025) NATURE MACHINE INTELLIGENCE Mataraso, S. J., Espinosa, C. A., Seong, D., Reincke, S., Berson, E., Reiss, J. D., Kim, Y., Ghanem, M., Shu, C., James, T., Tan, Y., Shome, S., Stelzer, I. A., Feyaerts, D., Wong, R. J., Shaw, G. M., Angst, M. S., Gaudilliere, B., Stevenson, D. K., Aghaeepour, N. 2025

  View details for DOI 10.1038/s42256-025-01021-x

  View details for Web of Science ID 001449397200001

• Persistence of a Proteomic Signature After a Hypertensive Disorder of Pregnancy. Hypertension (Dallas, Tex. : 1979) Hlatky, M. A., Shu, C. H., Stevenson, D. K., Shaw, G. M., Stefanick, M. L., Boyd, H. A., Melbye, M., Plummer, X. D., Sedan, O., Wong, R. J., Aghaeepour, N., Winn, V. D. 2025

  Abstract

  A hypertensive disorder of pregnancy is associated with a higher risk of cardiovascular disease later in life, but the potential mechanistic links are unknown.We recruited 2 groups of women, 1 during pregnancy and another at least 2 years after delivery. Cases had a hypertensive disorder of pregnancy, and controls had a normotensive pregnancy. The pregnancy cohort had study visits antepartum and postpartum; the mid-life group made a single study visit. We assayed 7228 plasma proteins, applied machine learning to identify proteomics signatures at each time point, and performed enrichment analyses to identify relevant biological pathways.The pregnancy cohort (58 cases and 46 controls) had a mean age of 33.8 years, and the mid-life group (71 cases and 74 controls) had a mean age of 40.8 years. Protein levels differed significantly between cases and controls at each time point: 6233 antepartum, 189 postpartum, and 224 in mid-life. The postpartum protein signature discriminated well between cases and controls (c-index=0.78), and it also discriminated well in the independent mid-life samples (c-index=0.72). Pathway analyses identified differences in the complement and coagulation cascades that persisted across the antepartum, postpartum, and mid-life samples. The 28 proteins present in both the postpartum and mid-life signatures included 5 complement factors (3, B, H, H-related-1, and C1r-subcomponent-like) and coagulation factor IX.Differences in protein expression persist for years after a hypertensive disorder of pregnancy. The consistent differences in the complement and coagulation pathways may contribute to the increased risk of later life cardiovascular disease.

  View details for DOI 10.1161/HYPERTENSIONAHA.124.24490

  View details for PubMedID 39981573

• A machine learning approach to leveraging electronic health records for enhanced omics analysis NATURE MACHINE INTELLIGENCE Mataraso, S. J., Espinosa, C. A., Seong, D., Reincke, S., Berson, E., Reiss, J. D., Kim, Y., Ghanem, M., Shu, C., James, T., Tan, Y., Shome, S., Stelzer, I. A., Feyaerts, D., Wong, R. J., Shaw, G. M., Angst, M. S., Gaudilliere, B., Stevenson, D. K., Aghaeepour, N. 2025

  View details for DOI 10.1038/s42256-024-00974-9

  View details for Web of Science ID 001397087200001