Determining whether a condition is historical or recent is important for accurate results in biomedicine. In this paper, we investigate four types of information found in clinical text that might be used to make this distinction. We conducted a descriptive, exploratory study using annotation on clinical reports to determine whether this temporal information is useful for classifying conditions as historical or recent. Our initial results suggest that few of these feature values can be used to predict temporal classification.
This paper introduces ONYX, a sentence-level text analyzer that
implements a number of innovative ideas in syntactic and semantic analysis.
ONYX is being developed as part of a project that seeks to translate spoken
dental examinations directly into chartable findings.
ONYX integrates syntax
and semantics to a high degree. It interprets sentences using a combination
of probabilistic classifiers, graphical unification, and semantically
annotated grammar rules. In this preliminary evaluation, ONYX shows
inter-annotator agreement scores with humans of 86% for assigning semantic
types to relevant words, 80% for inferring relevant concepts from words, and
76% for identifying relations between concepts.