A Hybrid Approach to Extracting Disorder Mentions from Clinical Notes

Crucial information on a patient's physical or mental conditions is provided by mentions of disorders, such as disease, syndrome, injury, and abnormality. Identifying disorder mentions is one of the most significant steps in clinical text analysis. However, there are many surface forms of the same concept documented in clinical notes. Some are even recorded disjointedly, briefly, or intuitively. Such difficulties have challenged the information extraction systems that focus on identifying explicit mentions. In this study, we proposed a hybrid approach to disorder extraction, which leverages supervised machine learning, rule-based annotation, and an unsupervised NLP system. To identify different surface forms, we exploited rich features, especially the semantic, syntactic, and sequential features, for better capturing implicit relationships among words. We evaluated our method on the CLEF 2013 eHealth dataset. The experiments showed that our hybrid approach achieves a 0.776 F-score under strict evaluation standards, outperforming any participating systems in the Challenge.