  A health outcome is a measurement or an observation used to capture and
assess the effect of a treatment. Automatic detection of health outcomes from
text would undoubtedly speed up access to evidence necessary in healthcare
decision making. Prior work on outcome detection has modelled this task as
either (a) a sequence labelling task, where the goal is to detect which text
spans describe health outcomes or (b) a classification task, where the goal is
to classify a text into a pre-defined set of categories depending on an outcome
that is mentioned somewhere in that text. However, this decoupling of span
detection and classification is problematic from a modelling perspective and
ignores global structural correspondences between sentence-level and word-level
information present in a given text. We propose a method that uses both
word-level and sentence-level information to simultaneously perform outcome
span detection and outcome type classification. In addition to injecting
contextual information to hidden vectors, we use label attention to
appropriately weight both word-level and sentence-level information.
Experimental results on several benchmark datasets for health outcome detection
show that our model consistently outperforms decoupled methods, reporting
competitive results.
