A hybrid approach for DocTime classification

On reviewing clinical documents, doctors, researchers and caregivers all expect to know the time when a patient's disorders appear (in the past, present, and future or from the past until now...) in comparison with the time when the documents are written. The information about this period of time is very useful in building a treatment regimen and an inquiry system for the patient, and summarizing the relevant documents. This paper proposes a hybrid approach between the rules and the machine learning to classify the relationship between a patient's disorders and the time of writing clinical documents. The hybrid approach has achieved a result of accuracy 0.5194, which is higher than the best ranking system (0.328) in the ShARE/CLEF eHealth 2014 Evaluation Lab.