  Joint medical relation extraction refers to extracting triples, composed of
entities and relations, from the medical text with a single model. One of the
solutions is to convert this task into a sequential tagging task. However, in
the existing works, the methods of representing and tagging the triples in a
linear way failed to the overlapping triples, and the methods of organizing the
triples as a graph faced the challenge of large computational effort. In this
paper, inspired by the tree-like relation structures in the medical text, we
propose a novel scheme called Bidirectional Tree Tagging (BiTT) to form the
medical relation triples into two two binary trees and convert the trees into a
word-level tags sequence. Based on BiTT scheme, we develop a joint relation
extraction model to predict the BiTT tags and further extract medical triples
efficiently. Our model outperforms the best baselines by 2.0\% and 2.5\% in F1
score on two medical datasets. What's more, the models with our BiTT scheme
also obtain promising results in three public datasets of other domains.
