A Combined Approach for Disease/Disorder Template Filling

Disease/Disorder Template Filling is a complicated task of relation extraction, requiring a combination of several methods in order to solve it. The aim of this paper is to propose a combined approach for disorder template filling. The system combined three methods: rule-based, regular expression, and machine learning-based. This system added several features for the machine learning-based method in comparison with the our system that was proposed in Task 2: ShARe/CLEF eHealth Evaluation Lab 2014 [6]. This rule-based set is established on observation of instances of disease/disorder shown the dependency tree presentation. The regular expression used the rules in Heidel Time [2]. The machine learning method used the SVM algorithm to train the classification model based on the features that were added. This addition increased the result of the Doc Time Class attribute up to 6%. The system´s result obtained an overall accuracy of 0.833, F1-score of 0.445, a precision of 0.406, and a recall of 0.516.