Classifying Supporting, Refuting, or Uncertain Evidence for Pneumonia Case Review

Introduction Characterizing mentions found in clinical texts that support, refute, or represent uncertainty for suspected pneumonia is one area where automated Natural Language Processing (NLP) screening algorithms could be improved. Mentions of uncertainty and negation commonly occur in clinical texts, and opportunities exist to extend existing algorithms [1] and taxonomies [2]. In general there are three main sources of uncertainty found in healthcare: 1) probability or risk; 2) ambiguity – lack of reliability, credibility or adequacy of the information; and, 3) complexity – aspects of the phenomenon that make it difficult to comprehend [3].