Medical Decision Tree Extraction: A Prompt Based Dual Contrastive Learning Method

The extraction of decision-making knowledge in the form of decision trees from unstructured textual knowledge sources is a novel research area within the field of information extraction. In this paper, we present an approach to extract medical decision trees from medical texts (aka. Text2DT) in the 8th China Health Information Processing Conference (CHIP 2022) Open Shared Task1. Text2DT task involves the construction of tree nodes using relation triples, which extends upon the foundation of the named entity recognition and relation extraction tasks. Compared to the fixed event schema typically defined in the event extraction task, the tree structure allows a more flexible and variable approach to representing information. To achieve this novel task, we propose a prompt based dual contrastive learning method. The experimental results demonstrate that the decision tree constructed by our model can achieve an accuracy of 55% (65% using the relaxed metric). 1(http://www.cips-chip.org.cn/2022/eval3)