Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction
Rui Lin, Jing Fan, Haifeng Wu · ACM Transactions on Intelligent Systems and Technology · 2023
Medical dialogue information extraction is an important but challenging task for Electronic Medical Records. Existing medical information extraction methods ignore the crucial information of sentence and multi-level dependency in dialogue, which limits their effectiveness for capturing essential medical information. To address these issues, we present a novel Multi-aspect Understanding with Cooperative Graph Attention Networks for Medical Dialogue Information Extraction to capture multi-aspect sentence information and multi-level dependency information from the dialogue. First, we propose the multi-aspect sentence encoder to capture various features from different perspectives. Second, we propose double graph attention networks to model the dependency features from intra-window and inter-window, respectively. Extensive experiments on a benchmark dataset have well-validated the effectiveness of the proposed method.