A DeBERTa-GPLinker-Based Model for Relation Extraction from Medical Texts

Extracting causal relationships in medical texts is essential for improving clinical decision support systems and constructing comprehensive medical knowledge graphs. This paper presents a novel model for extracting causal, conditional, and hypernym relationships from Chinese medical texts, combining DeBERTa's advanced contextual encoding with GPLinker's efficient entity and relationship extraction mechanisms. Our approach computes the score matrix of entities and their causal relationships, followed by a decoding process to obtain the final predictions. On the CMedCausal dataset, comparative experiments highlight our model's superior performance in terms of precision, recall, and F1 score, demonstrating its robustness and effectiveness in managing overlapping and nested entities and accurately extracting causal relationships in Chinese medical texts.