ShARe/CLEF eHealth 2013 Normalization of Acronyms/Abbreviations Challenge

Objective: Abbreviations and acronyms are widely used in the clinical documents. This paper describes using of a machine learner to automatically extract spans of abbreviations and acronyms from clinical notes and map them to the UMLS (Unified Medical Language System) CUI (Concept Unique Identifier). Tasks: A Conditional Random Field (CRF) machine learner was used to identify abbreviations and acronyms. Firstly, the training data was converted to the CRF format. The different feature sets were applied with 10-fold cross validation to find the best feature set to create the machine learning model. Secondly, the identified spans for abbreviation/acronyms were mapped to the UMLS (Unified Medical Language System) CUIs. Thirdly, a rule based engine was applied for disambiguation of terms with multiple abbreviations or acronyms. Approach: A novel supervised learning model was developed that incorporates a machine learning algorithm and a rule-based engine. Evaluation of each step included precision, recall and F-score metrics for span detection and accuracy for CUI mapping. Resources: Several tools which were created in our laboratory were used, including a Text to SNOMED CT (TTSCT) service, Lexical Management System (LMS) and Ring-fencing approach. Also a set of gazetteers which had been created from the training data was employed. Results: A 10-fold cross validation on the training data showed 0.911 of precision, 0.887 of recall and a F-score of 0.899 for detecting the boundary of abbreviation/acronyms and an accuracy of 0.760 for CUI mapping while the official results on the test data showed strict accuracy of 0.447 and relaxed accuracy of 0.488 which is the third team out of the five participating teams. A supervised machine learning method with mixed computational strategies and rule based method for disambiguation of expansions seems to provide a nearoptimal strategy for automated extraction of abbreviation/acronyms.