ezDI: A Hybrid CRF and SVM based Model for Detecting and Encoding Disorder Mentions in Clinical Notes

This paper describes the system used in Task-7 (Analysis of Clinical Text) of SemEval-2014 for detecting disorder mentions and associating them with their related CUI of UMLS1. For Task-A, a CRF based sequencing algorithm was used to find different medical entities and a binary SVM classifier was used to find relationship between entities. For Task-B, a dictionary look-up algorithm on a customized UMLS-2012 dictionary was used to find relative CUI for a given disorder mention. The system achieved F-score of 0.714 for Task A & accuracy of 0.599 for Task B when trained only on training data set, and it achieved F-score of 0.755 for Task A & accuracy of 0.646 for Task B when trained on both training as well as development data set. Our system was placed 3rd for both task A and B.