Injury narrative text classification using factorization model

BMC Medical Informatics and Decision Making

Research Article

Kirsten Vallmuur¹  Lin Chen²  Richi Nayak² 

[1] Centre for Accident Research and Road Safety - Queensland, School of Psychology and Counselling, Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Brisbane, Queensland, Australia;Faculty of Science & Technology, Queensland University of Technology, 2 George St, Brisbane, Queensland, Australia;

关键词: Narrative Text;    Classification;    Pre-processing;    Matrix Factorization;    Learning Enhancement;   

DOI  :  10.1186/1472-6947-15-S1-S5

来源: Springer

【 摘 要 】

Narrative text is a useful way of identifying injury circumstances from the routine emergency department data collections. Automatically classifying narratives based on machine learning techniques is a promising technique, which can consequently reduce the tedious manual classification process. Existing works focus on using Naive Bayes which does not always offer the best performance. This paper proposes the Matrix Factorization approaches along with a learning enhancement process for this task. The results are compared with the performance of various other classification approaches. The impact on the classification results from the parameters setting during the classification of a medical text dataset is discussed. With the selection of right dimension k, Non Negative Matrix Factorization-model method achieves 10 CV accuracy of 0.93.

【 授权许可】

Unknown   
© Chen et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.