CASSTIMP: Cascaded Architecture for Symptom Status Tracking with Inquiry-Aware Attention and Multi-Perception Pooling

Symptom status tracking poses a significant challenge due to the intricate nature of symptom identification and inference from medical doctor-patient dialogues. Numerous prior studies in this domain have relied on approaches involving multi-label classification and multi-task learning. Multi-label classification methods typically consider symptoms and statuses within a unified label space. Nevertheless, this approach frequently results in sparse predictions, eroding semantic relationships among labels and causing instability in prediction outcomes. In contrast, multi-task learning segregates symptom prediction and status prediction into separate tasks, thereby improving performance relative to conventional multi-label classification methods. Nonetheless, despite these advancements, the imbalance in training task weights persists, leading to suboptimal performance. To tackle these challenges, we employ a cascaded model structure rooted in the Question-Answering (QA) paradigm in this study. Our approach utilizes dialogue content to create context-inquiry pairs and introduces two novel modules: inquiry-aware attention and multi-perception pooling. Inquiry-aware attention enhances the contextual relationship between inquiries and dialogues, while multi-perception pooling extracts diverse semantics from the dialogue. The experimental results unequivocally demonstrate our method's efficiency, surpassing state-of-the-art techniques in symptom status tracking and indicating its superior effectiveness.