Contrastive Learning for Clinical Sentence Similarity Estimation in Medical Question Answering Systems

Abstract

This paper explores the application of contrastive learning to estimate clinical sentence similarity in the domain of medical question answering systems. The goal is to improve the accuracy and reliability of automated tools that respond to complex questions posed by healthcare professionals, clinical researchers, and patients. By focusing on sentence-level embeddings within clinical text corpora, our approach emphasizes the subtle linguistic cues and domain-specific contextual factors that determine semantic similarity in medical dialogues. We present a robust framework that leverages a contrastive objective to maximize the alignment between semantically related sentences while preserving essential distinctions among dissimilar examples. Additionally, we incorporate advanced representation learning techniques and rigorous optimization strategies to enhance the encoding of nuanced medical terminology. This paper addresses several core challenges: capturing long-range dependencies in clinical discourse, handling synonyms and abbreviations common to the healthcare domain, and mitigating the impact of noisy electronic health records on model performance. Our results show that a carefully designed contrastive learning pipeline yields significantly higher similarity estimation accuracy than standard sentence embedding baselines, with notable improvements in precision for semantically complex queries. We also provide a theoretical perspective on the relationship between contrastive objectives and the underlying geometry of sentence embeddings. Finally, we discuss the implications of our findings for broader clinical text mining applications.