Five-way smoking status classification using text hot-spot identification and error-correcting output codes.
We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, error-correcting output codes, and post-processing rules. We evaluated our approaches using the same methods as the i2b2 task organizers, using [...]
Author(s): Cohen, Aaron M
DOI: 10.1197/jamia.M2434