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A health outcome is a measurement or an observation used to capture andassess the effect of a treatment. Automatic detection of health outcomes fromtext would undoubtedly speed up access to evidence necessary in healthcaredecision making. Prior work on outcome detection has modelled this task aseither (a) a sequence labelling task, where the goal is to detect which textspans describe health outcomes, or (b) a classification task, where the goal isto classify a text into a pre-defined set of categories depending on an outcomethat is mentioned somewhere in that text. However, this decoupling of spandetection and classification is problematic from a modelling perspective andignores global structural correspondences between sentence-level and word-levelinformation present in a given text. To address this, we propose a method thatuses both word-level and sentence-level information to simultaneously performoutcome span detection and outcome type classification. In addition toinjecting contextual information to hidden vectors, we use label attention toappropriately weight both word and sentence level information. Experimentalresults on several benchmark datasets for health outcome detection show thatour proposed method consistently outperforms decoupled methods, reportingcompetitive results.

Detect and Classify -- Joint Span Detection and Classification for Health Outcomes