Pretrained patient trajectories for adverse drug event prediction using common data model-based electronic health records
Abstract Background Pretraining electronic health record (EHR) data using language models has enhanced performance across various medical tasks. Despite the potential of EHR pretraining models, predicting adverse drug events (ADEs) using EHR pretraining models has not been explored. Methods We used...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-00914-7 |
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| Summary: | Abstract Background Pretraining electronic health record (EHR) data using language models has enhanced performance across various medical tasks. Despite the potential of EHR pretraining models, predicting adverse drug events (ADEs) using EHR pretraining models has not been explored. Methods We used observational medical outcomes partnership common data model (CDM)-based EHR data from Seoul National University Hospital (SNUH) between January 2001 and December 2023 and Ajou University Medical Center (AUMC) between January 2004 and December 2023. In total 510,879 and 419,505 adult inpatients from SNUH and AUMC are included in internal and external datasets. For pretraining, the model was trained to infer randomly masked tokens using preceding and following history. In this process, we introduced domain embedding (DE) to provide information about the domain of masked tokens, preventing the model from finding codes from irrelevant domains. For qualitative analysis, we identified important features using the attention matrix from each finetuned model. Results Here we show that EHR pretraining models with DE outperform the models without pretraining and DE in predicting various ADEs, with the average area under the receiver operating characteristic curve (AUROC) of 0.958 and 0.964 in internal and external validations, respectively. For feature importance analysis, we demonstrate that the results are consistent with priorly reported background clinical knowledge. In addition to cohort-level interpretation, patient-level interpretation is also available. Conclusions The CDM-based EHR pretraining model with DE can improve prediction performance for various ADEs and can provide proper explanation at cohort and patient level. Our model has the potential to serve as a foundation model due to its strong prediction performance, interpretability, and compatibility. |
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| ISSN: | 2730-664X |