Clinical text analysis using machine learning methods
SemEval (Semantic Evaluation) is an annual workshop where attendees participate in a series of evaluations (competitions) of computational semantic analysis for natural language processing (NLP) The series evaluations include 10 20 tasks each year. In this paper we present our entry to the SemEval 2014 Task 7 on the Analysis of Clinical Text evaluation. The main aim of this task is to analyze large amounts of clinical data and to find the mentions of clinical disorders. This task consists of two sub tasks: a) named entity recognition i.e, identifying disorder concepts that belong to Unified Medical Language System (UMLS) semantic group; and b) normalization, i.e, mapping mentions of disorders to UMLS Concept Unique Identifier (CUI) In this paper, we present a supervised machine learning system for prediction of disorder named entities based on the conditional random field model. The data set provided by the Task 7 organizer was used to evaluate our model.
2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS)