Electronic health records (EHRs) are a rich source of clinical data, yet exploiting longitudinal signals for pulmonary nodule diagnosis remains challenging due to the administrative noise and high level of clinical abstraction present in these records. Because of this complexity, classification models are prone to overfitting when labeled data is scarce. This study explores masked representation learning (MRL) as a strategy to improve pulmonary nodule diagnosis by modeling longitudinal EHRs across multiple modalities: clinical conditions, procedures, and medications. We leverage a web-scale text embedding model to encode EHR event streams into semantically embedded sequences. We then pretrain a bidirectional transformer using MRL conditioned on time encodings on a large cohort of general pulmonary conditions from our home institution. Evaluation on a cohort of diagnosed pulmonary nodules demonstrates significant improvement in diagnosis accuracy with a model finetuned from MRL (0.781 AUC, 95% CI: [0.780, 0.782]) compared to a supervised model with the same architecture (0.768 AUC, 95% CI: [0.766, 0.770]) when integrating all three modalities. These findings suggest that language-embedded MRL can facilitate downstream clinical classification, offering potential advancements in the comprehensive analysis of longitudinal EHR modalities.

The authors have declared no competing interest.

This research was funded by the NIH through F30CA275020, 2U01CA152662, and R01CA253923-02, as well as NSF CAREER 1452485 and NSF 2040462. This study was also funded by the Vanderbilt Institute for Surgery and Engineering through T32EB021937-07, the Vanderbilt Institute for Clinical and Translational Research through UL1TR002243-06, and the Pierre Massion Directorship in Pulmonary Medicine.

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IRB of Vanderbilt University gave ehtical approval for this work.

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All data produced in the present study are available upon reasonable request to the authors