Tomin James

All Publications

• AI Guided Parenteral Nutrition Therapy After Hematopoietic Stem Cell Transplantation. NPJ digital medicine El Ariss, A. B., Phongpreecha, T., Ghanem, M., Kimari, M., James, T., Freche, E., Kerner, J. A., Gaudillière, B., Angst, M. S., Gaskari, S., Stevenson, D. K., Smith, M., Aghaeepour, N. 2026

  Abstract

  Adults undergoing hematopoietic cell transplantation often develop serious complications that cause rapid nutritional decline. We developed and evaluated an AI approach to standardize intravenous nutrition (total parenteral nutrition, TPN) during this vulnerable period. Using real-world records from Stanford Health Care (6402 transplants, 2008-2025), we analyzed 1473 adults who received TPN, totaling 27,447 patient-days, linking each day's clinical state to the next day's prescription. We created a library of 30 standardized TPN regimens and trained a model to recommend next-day dose adjustments based on laboratory data and the existing prescription. (Pearson r ≈ 0.71). We then assessed an AI policy learned from past care and found that the Reinforcement learning agent selected dose adjustments with a higher composite score than the existing clinical policy. These results show that AI-guided TPN is feasible and may enhance bedside decision-making for adult transplant care, warranting prospective evaluation.

  View details for DOI 10.1038/s41746-026-02652-z

  View details for PubMedID 42092179

• Development and validation of a pre-trained language model for neonatal morbidities: a retrospective, multicentre, prognostic study. The Lancet. Digital health Xie, F., Chung, P., Reiss, J. D., Tjoa, E., De Francesco, D., Phongpreecha, T., Haberkorn, W., Chakraborty, D., Chang, A. L., James, T., Kim, Y., Mataraso, S., Espinosa, C., Yang, L., Shu, C. H., Xue, L., Berson, E., Mohammadi, N., Shome, S., Reincke, S. M., Ghanem, M., Maric, I., Gaudilliere, B., Angst, M. S., Sylvester, K., Shaw, G. M., Prince, L. S., Stevenson, D. K., Aghaeepour, N. 2025: 100926

  Abstract

  Early identification and monitoring of neonatal morbidities are critical for timely interventions that can prevent complications, optimise resource use, and support families. Although traditional tools based on tabular data and biomarkers are beneficial, they are restricted in assessing the risk of morbidities in newborns. In this study, we developed NeonatalBERT, a pre-trained large language model (LLM) that estimates the risk of neonatal morbidities from clinical notes.This prognostic study investigated retrospective primary and external cohorts from two different quaternary-care academic medical centres in the USA: Stanford Health Care and Beth Israel Deaconess Medical Center. NeonatalBERT was initially pre-trained on clinical notes from the primary cohort and then fine-tuned separately for both cohorts. NeonatalBERT was also compared against other existing LLMs, such as BioBERT and Bio-ClinicalBERT, as well as traditional machine learning and logistic regression models using tabular features. NeonatalBERT was evaluated on 19 neonatal morbidities (respiratory distress syndrome, bronchopulmonary dysplasia, pulmonary haemorrhage, pulmonary hypertension, atelectasis, aspiration syndrome, intraventricular haemorrhage, periventricular leukomalacia, neonatal seizures, other CNS disorders, patent ductus arteriosus, cardiovascular instability, sepsis, candidiasis, anaemia, jaundice, necrotising enterocolitis, retinopathy of prematurity, and death) for the primary cohort and ten for the external cohort (respiratory distress syndrome, bronchopulmonary dysplasia, pulmonary haemorrhage, intraventricular haemorrhage, patent ductus arteriosus, sepsis, jaundice, necrotising enterocolitis, retinopathy of prematurity, and death). For each outcome, the area under the receiver operating characteristic curve, area under the precision-recall curve (AUPRC), and F1 scores were evaluated.32 321 newborns were included in the primary cohort, including 27 411 in the primary training set (mean gestational age 38·64 weeks [SD 2·30]; 13 056 [47·6%] female and 14 355 [52·4%] male newborns) and 4910 in the primary testing set (mean gestational age 38·64 [2·13] weeks; 2336 [47·6%] female and 2574 [52·4%] male newborns). Additionally, 7061 newborns were selected into the external cohort, including 5653 in the external training set (1567 [27·7%] premature and 4086 [72·3%] term births; 2614 [46·2%] female and 3039 [53·8%] male newborns) and 1408 in the external testing set (383 [27·2%] premature and 1025 [72·8%] term births; 624 [44·3%] female and 784 [55·7%] male newborns). In the primary cohort, the mean AUPRC over 19 outcomes was 0·291 (95% CI 0·268-0·314) for NeonatalBERT, 0·238 (0·217-0·259) for Bio-ClinicalBERT, 0·217 (0·197-0·236) for BioBERT, and 0·194 (0·177-0·211) for the traditional model using tabular data. In the external cohort, NeonatalBERT had a mean AUPRC of 0·360 (0·328-0·393), outperforming other models with the range of 0·224-0·333.Based on validation using two large-scale US datasets, NeonatalBERT effectively estimates the risk of neonatal morbidities from unstructured clinical notes of newborns. The promising results from this study show the potential of NeonatalBERT to enhance neonatal care and streamline hospital operations.National Institutes of Health, Burroughs Wellcome Fund, March of Dimes Foundation, Alfred E Mann Foundation, Gates Foundation, Christopher Hess Research Fund, Roberts Foundation Research Fund, Prematurity Research Center, and Stanford Maternal & Child Health Research Institute Postdoctoral Support funds.

  View details for DOI 10.1016/j.landig.2025.100926

  View details for PubMedID 41419365

• Epigenomic profile of GBA1 in Parkinson's disease. Parkinsonism & related disorders Berson, E., Zaghroun, R., Santoro, M., Bukhari, S., Seong, D., Shu, C. H., Perna, A., James, T., Montine, K. S., Serrano, G. E., Beach, T. G., Keene, C. D., Chang, H. Y., Corces, M. R., Cholerton, B., Aghaeepour, N., Montine, T. J. 2025; 140: 108066

  Abstract

  While genome-wide association studies have identified GBA1 as a key gene contributing to disease severity and cognitive decline in PD, its molecular effects remain poorly understood.We used integrative bulk ATAC-seq across six brain regions from autopsied individuals with PD and varying genetic risk to characterize region- and cell type-specific molecular differences. Using Cellformer, an AI-based bulk ATAC-seq-deconvolution tool, we determined cell type-specific effects of GBA1 on PD disease progression and then validated our findings using whole transcriptome data from blood samples.Epigenomic differences between PD with ("GBA+"; n = 15) and without ("GBA-", n = 15) GBA1 variants were localized in substantia nigra. Nineteen chromatin-accessible regions strictly separated GBA+ from GBA-, including the promoter sites of key genes such as CACNA1C, EHMT1, and SLC25A48. The effect in GBA + spanned the main cell types in brain, and chromatin differences between GBA- and GBA + increased with neuropathologic progression of disease. Significant differences in the epigenomic profile in GBA+ were observed in neuronal cells (AUROC = 0.8, AUPRC = 0.8, P-value<0.0001). Validation in blood samples distinguished between GBA+ and GBA-subtypes, achieving AUROC values of 0.99. Over 5000 transcripts in blood cells distinguished GBA+ from GBA-, validating key genes and pathways from our epigenomic analysis of brain regions.Our study provides novel insights into the cell type-specific epigenomic and transcriptomic landscape of GBA+ and its molecular divergence from other PD subtypes, and highlights potential therapeutic targets for this genetically defined subset of PD.

  View details for DOI 10.1016/j.parkreldis.2025.108066

  View details for PubMedID 41033114

• Mitigation of outcome conflation in predicting patient outcomes using electronic health records. Journal of the American Medical Informatics Association : JAMIA Reincke, S. M., Espinosa, C., Chung, P., James, T., Berson, E., Aghaeepour, N. 2025

  Abstract

  OBJECTIVES: Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated with false positives. This study aims to evaluate the impact of confounders on the specificity of single-outcome prediction models and assess the effectiveness of a multi-class architecture in mitigating outcome conflation.MATERIALS AND METHODS: We evaluated a state-of-the-art model predicting pancreatic cancer from disease code sequences in an independent cohort of 2.3 million patients and compared this single-outcome model with a multi-class model designed to predict multiple cancer types simultaneously. Additionally, we conducted a clinical simulation experiment to investigate the impact of confounders on the specificity of single-outcome prediction models.RESULTS: While we were able to independently validate the pancreatic cancer prediction model, we found that its prediction scores were also correlated with ovarian cancer, suggesting conflation of outcomes due to underlying confounders. Building on this observation, we demonstrate that the specificity of single-outcome prediction models is impaired by confounders using a clinical simulation experiment. Introducing a multi-class architecture improves specificity in predicting cancer types compared to the single-outcome model while preserving performance, mitigating the conflation of outcomes in both the real-world and simulated contexts.DISCUSSION: Our results highlight the risk of outcome conflation in single-outcome AI prediction models and demonstrate the effectiveness of a multi-class approach in mitigating this issue.CONCLUSION: The number of predicted outcomes needs to be carefully considered when employing AI disease risk prediction models.

  View details for DOI 10.1093/jamia/ocaf033

  View details for PubMedID 40056434

• PregMedNet: Multifaceted Maternal Medication Impacts on Neonatal Complications. medRxiv : the preprint server for health sciences Kim, Y., Marić, I., Kashiwagi, C. M., Han, L., Chung, P., Reiss, J. D., Butcher, L. D., Caoili, K. J., Berson, E., Xue, L., Espinosa, C., James, T., Shome, S., Xie, F., Ghanem, M., Seong, D., Chang, A. L., Reincke, S. M., Mataraso, S., Shu, C. H., Francesco, D. D., Becker, M., Kumar, W. M., Wong, R., Gaudilliere, B., Angst, M. S., Shaw, G. M., Bateman, B. T., Stevenson, D. K., Prince, L. S., Aghaeepour, N. 2025

  Abstract

  While medication intake is common among pregnant women, medication safety remains underexplored, leading to unclear guidance for patients and healthcare professionals. PregMedNet addresses this gap by providing a multifaceted maternal medication safety framework based on systematic analysis of 1.19 million mother-baby dyads from U.S. claims databases. A novel confounding adjustment pipeline was applied to systematically control confounders for multiple medication-disease pairs, robustly identifying both known and novel maternal medication effects. Notably, one of the newly discovered associations was experimentally validated, demonstrating the reliability of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms of newly identified associations were generated using a graph learning method. These findings highlight PregMedNet's value in promoting safer medication use during pregnancy and maternal-neonatal outcomes.

  View details for DOI 10.1101/2025.02.13.25322242

  View details for PubMedID 39990567

  View details for PubMedCentralID PMC11844599

• 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. M., Berson, E., Reiss, J. D., Kim, Y., Ghanem, M., Shu, C. H., 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; 7 (2): 293-306

  Abstract

  Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes.

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

  View details for PubMedID 40008295

  View details for PubMedCentralID PMC11847705

• 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

• Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends. Heliyon Ghanem, M., Espinosa, C., Chung, P., Reincke, M., Harrison, N., Phongpreecha, T., Shome, S., Saarunya, G., Berson, E., James, T., Xie, F., Shu, C. H., Hazra, D., Mataraso, S., Kim, Y., Seong, D., Chakraborty, D., Studer, M., Xue, L., Marić, I., Chang, A. L., Tjoa, E., Gaudillière, B., Tawfik, V. L., Mackey, S., Aghaeepour, N. 2024; 10 (7): e29050

  Abstract

  Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field.The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test.The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning".Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

  View details for DOI 10.1016/j.heliyon.2024.e29050

  View details for PubMedID 38623206

  View details for PubMedCentralID PMC11016610

• Maternal vaccination to prevent adverse pregnancy outcomes: An underutilized molecular immunological intervention? Journal of molecular biology Giles, M. L., Sing Way, S., Marchant, A., Aghaepour, N., James, T., Schaltz-Buchholzer, F., Zazara, D., Arck, P., Kollmann, T. R. 2023: 168097

  Abstract

  Adverse pregnancy outcomes including maternal mortality, stillbirth, preterm birth, intrauterine growth restriction cause millions of deaths each year. More effective interventions are urgently needed. Maternal immunization could be one such intervention protecting the mother and newborn from infection through its pathogen-specific effects. However, many adverse pregnancy outcomes are not directly linked to the infectious pathogens targeted by existing maternal vaccines but rather are linked to pathological inflammation unfolding during pregnancy. The underlying pathogenesis driving such unfavourable outcomes have only partially been elucidated but appear to relate to altered immune regulation by innate as well as adaptive immune responses, ultimately leading to aberrant maternal immune activation. Maternal immunization, like all immunization, impacts the immune system beyond pathogen-specific immunity. This raises the possibility that maternal vaccination could potentially be utilised as a pathogen-agnostic immune modulatory intervention to redirect abnormal immune trajectories towards a more favourable phenotype providing pregnancy protection. In this review we describe the epidemiological evidence surrounding this hypothesis, along with the mechanistic plausibility and present a possible path forward to accelerate addressing the urgent need of adverse pregnancy outcomes.

  View details for DOI 10.1016/j.jmb.2023.168097

  View details for PubMedID 37080422