Towards generating a patients timeline: Extracting temporal relationships from clinical notes
We proposed a hybrid system (graph-based and machine-learning) for temporal relation extraction. Our system yields an f-measure of 0.63, precision of 0.76 and recall of 0.54. We ranked third in f-measure and first in precision in the 2012 i2b2 NLP TLink extraction task. Sentence simplification and then parsing improves temporal relation extraction. Generalizing sentences yields interesting extraction patterns.
temporal relation extraction clinical text mining automatic patient timeline natural language processing machine learning temporal graph