Analysis of polarity information in medical text.

Abstract

Knowing the polarity of clinical outcomes is important in answering questions posed by clinicians in patient treatment. We treat analysis of this information as a classification problem. Natural language processing and machine learning techniques are applied to detect four possibilities in medical text: no outcome, positive outcome, negative outcome, and neutral outcome. A supervised learning method is used to perform the classification at the sentence level. Five feature sets are constructed: unigrams, bigrams, change phrases, negations, and categories. The performance of different combinations of feature sets is compared. The results show that generalization using the category information in the domain knowledge base Unified Medical Language System is effective in the task. The effect of context information is significant. Combining linguistic features and domain knowledge leads to the highest accuracy.

Keywords

BigramComputer scienceArtificial intelligenceNatural language processingOutcome (game theory)SentenceNegationPolarity (international relations)Natural languageContext (archaeology)Knowledge baseGeneralizationFeature (linguistics)Machine learningQuestion answeringLinguisticsMathematicsTrigram

Affiliated Institutions

University of Toronto CA

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Publication Info

Year: 2005
Type: article
Pages: 570-4
Citations: 83
Access: Closed

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APA Style

                            
                                    Yun Niu, 
                                
                                    Xiaodan Zhu, 
                                
                                    Jianhua Li
                                
                                et al.
                            
                            (2005). 
                            Analysis of polarity information in medical text.. 
                            PubMed
                            
                            , 570-4.