  Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019)
to infer clinical entities (e.g., symptoms) and their properties (e.g.,
duration). It tackles the challenge of large label space and limited training
data using a hierarchical two-stage approach that identifies the span of
interest in a tagging step and assigns labels to the span in a classification
step.
  We extend the SAT model to jointly infer not only entities and their
properties but also relations between them. Most relation extraction models
restrict inferring relations between tokens within a few neighboring sentences,
mainly to avoid high computational complexity. In contrast, our proposed
Relation-SAT (R-SAT) model is computationally efficient and can infer relations
over the entire conversation, spanning an average duration of 10 minutes.
  We evaluate our model on a corpus of clinical conversations. When the
entities are given, the R-SAT outperforms baselines in identifying relations
between symptoms and their properties by about 32% (0.82 vs 0.62 F-score) and
by about 50% (0.60 vs 0.41 F-score) on medications and their properties. On the
more difficult task of jointly inferring entities and relations, the R-SAT
model achieves a performance of 0.34 and 0.45 for symptoms and medications
respectively, which is significantly better than 0.18 and 0.35 for the baseline
model. The contributions of different components of the model are quantified
using ablation analysis.
