Since hedge cues cause systems to predict false positive labels, our idea here was to train Maximum Entropy Models for the false positive classifications of our ICD-9-CM coding system using the vector space representation of radiology reports.
we classified every sentence that contained a medical term (disease or symptom name) and caused the automated ICD-9 coder 8 to predict a false positive code was treated as a speculative sentence and all the rest were treated as non-speculative sentences.