Medical language processing for patient diagnosis using text classification and negation labelling
Document Type Conference Paper
In Proceedings of the Second i2b2 Shared-Task Workshop on Challenges in Natural Language Processing for Clinical Data, American Medical Informatics Association Annual Conference (AMIA'08)
This paper describes the approach of the DIT AIGroup to the i2b2 Obesity Challenge to build a system to diagnose obesity and related co-morbidities from narrative, unstructured patient records. Based on experimental results a system was developed which used knowledge-light text classification using decision trees and negation labelling.