A Contrastive Learning and Region-Guided Approach for Long Clinical Text Classification

Delirium is an acute and reversible neuropsychiatric syndrome, with its diagnosis relying on clinicians’ dynamic assessments of patients. This process requires the integration of multidimensional clinical information, for which electronic medical records (EMRs) serve as a crucial source. However, Chinese EMRs are often lengthy, unevenly structured, and exhibit considerable variability in symptom descriptions, leading to key diagnostic information being obscured by redundant narratives. These characteristics pose significant challenges for traditional methods to consistently extract relevant diagnostic features. This paper proposes a deep learning framework, guided by medical knowledge, to improve the classification performance and interpretability of delirium in Chinese long-form EMRs. The framework employs a region-guided chunking mechanism to segment records into semantically coherent units and extract key features. It combines differentiated data augmentation and contrastive learning methods to strengthen semantic discriminability, and utilizes adaptive feature fusion to achieve cross-chunk collaborative decision-making. Experimental results demonstrate that this method effectively enhances the accuracy and reliability of delirium diagnosis in Chinese medical texts, showing strong clinical applicability.