A Multitask Learning Framework for Multigranular Causality Detection and Extraction in Indonesian EMRs

Extracting causal relations from Electronic Medical Records (EMRs) is important for transforming unstructured clinical narratives into structured knowledge that supports decision making. However, most existing approaches split causality modeling into two subtasks: causality detection and causality extraction. This causes poorer cross-task learning, error propagation, and missing fine-grained markers. Therefore, we propose a Multitask Causality Learning for Causality Detection and Extraction (MTCL-CDE) framework that simultaneously identifies causal relationships at sentence and token levels. The main idea of our framework is a unified hierarchical multigranular architecture explicitly designed to maximize cross-task signal sharing and capture fine-grained markers at all levels. Our approach utilizes an enhanced hierarchical attention network (HAN) and integrates it into a multigranular architecture. First, each sentence was represented across four hierarchical layers: characters, words, subsentences, and sentences to retain fine-grained markers and contextual signals. Each hierarchical layer produces a specific representation that is fused via a multigranular fusion module into a unified feature representation. Finally, the unified features are fed into a sentence level classifier for detection and a token level classifier for extraction. We evaluated our approach using a real-world Indonesian electronic medical record (EMR) dataset collected from hospitals. The experimental results show that MTCL-CDE achieves an average F1-score of 0.9630 for detection and 0.9003 for extraction, outperforming strong state-of-the-art baseline models including fine-grained-based models, HAN-based models, and other combined multitask frameworks. These results confirm the effectiveness of multitask learning for accurate and comprehensive causality modeling in clinical texts.