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Classifying tumor event attributes in radiology reports
Author(s) -
Yim Wenwai,
Kwan Sharon W.,
Yetisgen Meliha
Publication year - 2017
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23937
Subject(s) - negation , temporality , computer science , malignancy , natural language processing , artificial intelligence , event (particle physics) , identification (biology) , radiology , medicine , information retrieval , pathology , physics , quantum mechanics , philosophy , botany , epistemology , biology , programming language
Radiology reports contain vital diagnostic information that characterizes patient disease progression. However, information from reports is represented in free text, which is difficult to query against for secondary use. Automatic extraction of important information, such as tumor events using natural language processing, offers possibilities in improved clinical decision support, cohort identification, and retrospective evidence‐based research for cancer patients. The goal of this work was to classify tumor event attributes: negation, temporality, and malignancy, using biomedical ontology and linguistically enriched features. We report our results on an annotated corpus of 101 hepatocellular carcinoma patient radiology reports, and show that the improved classification improves overall template structuring. Classification performances for negation identification, past temporality classification, and malignancy classification were at 0.94, 0.62, and 0.77 F1, respectively. Incorporating the attributes into full templates led to an improvement of 0.72 F1 for tumor‐related events over a baseline of 0.65 F1. Improvement of negation, malignancy, and temporality classifications led to significant improvements in template extraction for the majority of categories. We present our machine‐learning approach to identifying these several tumor event attributes from radiology reports, as well as highlight challenges and areas for improvement.