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We propose an approach for adapting the DeBERTa model for electronic healthrecord (EHR) tasks using domain adaptation. We pretrain a small DeBERTa modelon a dataset consisting of MIMIC-III discharge summaries, clinical notes,radiology reports, and PubMed abstracts. We compare this model's performancewith a DeBERTa model pre-trained on clinical texts from our institutional EHR(MeDeBERTa) and an XGBoost model. We evaluate performance on three benchmarktasks for emergency department outcomes using the MIMIC-IV-ED dataset. Wepreprocess the data to convert it into text format and generate four versionsof the original datasets to compare data processing and data inclusion. Theresults show that our proposed approach outperforms the alternative models ontwo of three tasks (p<0.001) and matches performance on the third task, withthe use of descriptive columns improving performance over the original columnnames.

Adapting Pretrained Language Models for Solving Tabular Prediction Problems in the Electronic Health Record