Elevating Clinical Semantics: Contrastive Pre-Training Beyond Cross-Entropy in Discharge Summaries

Despite remarkable advances in neural language models, a substantial gap remains in precisely interpreting the complex semantics of Electronic Medical Records (EMR). We propose Contrastive Representations Pre-Training (CRPT) to address this gap, replacing the conventional Next Sentence Prediction task’s cross-entropy loss with contrastive loss and incorporating whole-word masking to capture multi-token domain-specific terms better. We also introduce a carefully designed negative sampling strategy that balances intra-document and cross-document sentences, enhancing the model’s discriminative power. Implemented atop a BERT-based architecture and evaluated on the Biomedical Language Understanding Evaluation (BLUE) benchmark, our Discharge Summary CRPT model achieves significant performance gains, including a natural language inference precision of 0.825 and a sentence similarity score of 0.775. We further extend our approach through Bio+Discharge Summary CRPT, combining biomedical and clinical corpora to boost downstream performance across tasks. Our framework demonstrates robust interpretive capacity in clinical texts by emphasizing sentence-level semantics and domain-aware masking. These findings underscore CRPT’s potential for advancing semantic accuracy in healthcare applications and open new avenues for integrating larger negative sample sets, domain-specific masking techniques, and multi-task learning paradigms.