UTH-CCB: The Participation of the SemEval 2015 Challenge - Task 14

This paper describes the system developed by the University of Texas Health Science Center at Houston (UTHealth), for the 2015 SemEval shared task on “Analysis of Clinical Text” (Task 14). We participated in both sub-tasks: Task 1 for “Disorder Identification”, which aims to detect disorder entities and encode them to UMLS (Unified Medial Language System) CUI (Concept Unique Identifier) and Task 2 for Disorder Slot Filling, where the task is to identify normalized value for modifiers of disorders. For Task 1, we developed an ensemble approach that combined machine learning based named entity recognition classifiers with MetaMap, an existing symbolic biomedical NLP system, to recognize disorder entities, and we used a general Vector Space Model-based approach for disorder encoding to UMLS CUIs. To identify modifiers of disorders (Task 2), we developed Support Vector Machines-based classifiers for each type of modifier, by exploring various types of features. Our system was ranked 3^rd for Task 1 and 1^st for the Task 2 (both 2A and 2B), demonstrating the effectiveness of machine learning-based approaches for extracting clinical entities and their modifiers from clinical narratives.