scholarly journals Structuring unstructured clinical narratives in OpenMRS with medical concept extraction

2015 ◽  
Author(s):  
Ryan M Eshleman ◽  
Hui Yang ◽  
Barry Levine
Keyword(s):  
Concept Extraction ◽  
Medical Concept

scholarly journals Active learning: a step towards automating medical concept extraction

2015 ◽  
Vol 23 (2) ◽  
pp. 289-296 ◽  
Author(s):  
Mahnoosh Kholghi ◽  
Laurianne Sitbon ◽  
Guido Zuccon ◽  
Anthony Nguyen
Keyword(s):  
Active Learning ◽  
Language Processing ◽  
Selection Criteria ◽  
Conditional Random Fields ◽  
Free Text ◽  
Data Sets ◽  
Comparative Performance ◽  
Concept Extraction ◽  
Learning Framework ◽  
Medical Concept

Abstract Objective This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined. Materials and methods The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab. Results The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled. Conclusion Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation.

scholarly journals MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7

2014 ◽  
Author(s):  
Alejandro Riveros ◽  
Maria De Arteaga ◽  
Fabio González ◽  
Sergio Jimenez ◽  
Henning Müller
Keyword(s):  
Concept Extraction ◽  
Medical Concept
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