We study and compare two different approaches
to the task of automatic assignment
of predefined classes to clinical free text
narratives. In the first approach this
is treated as a traditional mention-level
named-entity recognition task, while the
second approach treats it as a sentence level
multi-label classification task. Performance
comparison across these two
approaches is conducted in the form of
sentence-level evaluation and state-of-the-art
methods for both approaches are evaluated.
The experiments are done on
two data sets consisting of Finnish clinical
text, manually annotated with respect
to the topics pain and acute confusion.
Our results suggest that the mention level
named-entity recognition approach
outperforms sentence-level classification
overall, but the latter approach still manages
to achieve the best prediction scores
on several annotation classes.