ArticleNPJ digital medicine2023
Development and external validation of a pretrained deep learning model for the prediction of non-accidental trauma.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
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- Prediction of antibiotic-associated cutaneous adverse drug reactions using electronic health record foundation models.NPJ digital medicine · 2026Article
- A Real-Time Dynamic Warning Method for MODS in Trauma Sepsis Patients Based on a Pre-Trained Transfer Learning Algorithm.Diagnostics (Basel, Switzerland) · 2026Article
- Multicenter validation of a scalable, interpretable, multitask prediction model for multiple clinical outcomes.NPJ digital medicine · 2025Article
- Pretrained patient trajectories for adverse drug event prediction using common data model-based electronic health records.Communications medicine · 2025Article
- Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.The Lancet. Digital health · 2025Article
- Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally validated with the ANSWER cohort.Arthritis research & therapy · 2025Article
- Exploring the Potential of an AI Chatbot as a Supplementary Tool for Nutritional Prescription Hospital Discharge: A Preliminary Study.Scientifica · 2025Article
- Artificial intelligence-based personalized clinical decision-making for patients with localized prostate cancer: surgery versus radiotherapy.The oncologist · 2024Article
- The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.JMIR medical informatics · 2024Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-accidental trauma (NAT) is deadly and difficult to predict. Transformer models pretrained on large datasets have recently produced state of the art performance on diverse prediction tasks, but the optimal pretraining strategies for diagnostic predictions are not known. Here we report the development and external validation of Pretrained and Adapted BERT for Longitudinal Outcomes (PABLO), a transformer-based deep learning model with multitask clinical pretraining, to identify patients who will receive a diagnosis of NAT in the next year. We develop a clinical interface to visualize patient trajectories, model predictions, and individual risk factors. In two comprehensive statewide databases, approximately 1% of patients experience NAT within one year of prediction. PABLO predicts NAT events with area under the receiver operating characteristic curve (AUROC) of 0.844 (95% CI 0.838-0.851) in the California test set, and 0.849 (95% CI 0.846-0.851) on external validation in Florida, outperforming comparator models. Multitask pretraining significantly improves model performance. Attribution analysis shows substance use, psychiatric, and injury diagnoses, in the context of age and racial demographics, as influential predictors of NAT. As a clinical decision support system, PABLO can identify high-risk patients and patient-specific risk factors, which can be used to target secondary screening and preventive interventions at the point-of-care.
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Registered trials
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