Development and external validation of a pretrained deep learning model for the prediction of non-accidental trauma.

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 ev

SUBMITTER: Huang D 

PROVIDER: S-EPMC10356774 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature