Text classification for medical informatics: a comparison of models for data mining radiological medical records

Abstract

In this study we analyze 1024 free text digital records from pediatric patients who underwent CT scanning. The free text reports are from the digital records of patients who underwent CT scanning in a one-year period in 2004 at the Nagasaki University Medical Hospital in Japan. We use text mining algorithms to model the records. Each scan was evaluated by an expert in the field and classified as to whether the CT scan was necessary or not. A model was built that predicts this classification. The results show that models developed on raw text could contribute significantly to the physician’s decision to order a CT scan. Practically this is important because radiation at levels ordinarily used for CT scanning may pose significant health risks especially to children and thus the modeling of unnecessary scanning may lead to less exposure to radiation.