Fine-grained Certainty Level Annotations Used for Coarser-grained E-health Scenarios Certainty Classification of Diagnostic Statements in Swedish Clinical Text

Abstract. An important task in information access methods is distinguishing factual information from speculative or negated information. Fine-grained certainty levels of diagnostic statements in Swedish clinical text are annotated in a corpus from a medical university hospital. The annotation model has two polarities (positive and negative) and three certainty levels. However, there are many e-health scenarios where such fine-grained certainty levels are not practical for information extraction. Instead, more coarse-grained groups are needed. We present three scenarios: adverse event surveillance, decision support alerts and automatic summaries and collapse the fine-grained certainty level classifications into coarser-grained groups. We build automatic classifiers for each scenario and analyze the results quantitatively. Annotation discrepancies are analyzed qualitatively through manual corpus analysis. Our main findings are that it is feasible to use a corpus of fine-grained certainty level annotations to build classifiers for coarser-grained real-world scenarios: 0.89, 0.91 and 0.8 F-score (overall average)