Medical Causality Extraction: A Two-Stage Based Nested Relation Extraction Model

The extraction of medical causality contributes to constructing medical causal knowledge graphs, and enhancing the interpretability of modern medical consultation process. In this paper, we present our approach to medical causal entity and relation extraction in the 8th China Health Information Processing Conference (CHIP 2022) Open Shared Task. Nested relations and overlapping relations with shared entities are two major challenges in this task. We propose a two-stage model to achieve nested relation extraction. In the first stage, we extract traditional non-nested relations and explore how to utilize causal relational signals in entity recognition module to alleviate the problem of overlapping relations. In the second stage, we identify entities in nested relations through the method of machine reading comprehension and design a span-based contrastive learning method (SpanCL) with under-sampling strategy to determine whether causality is nested. The experiment results show that the method we proposed can achieve 43.23% in terms of macro-averaged F1-score.