Classifying Medical Questions based on an Evidence Taxonomy

Hong Yu, Carl Sable, and Hai Ran Zhu

We present supervised machine-learning approaches to automatically classify medical questions based on a hierarchical evidence taxonomy created by physicians. We show that SVMs is the best classifier for this task and that a ladder approach, which incorporates the knowledge representation of the hierarchical evidence taxonomy, leads to the highest performance. We have explored the use of features from a large, robust biomedical knowledge resource, namely, the Unified Medical Language System (UMLS), and we have found that performance is generally enhanced by including these features in addition to bag-ofwords.


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