Injury narrative text classification using factorization model
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.
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|Item Type:||Journal Article|
|Keywords:||narrative text, classification, pre-processing, matrix factorization, learning enhancement|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Health Information Systems (incl. Surveillance) (111711)|
|Divisions:||Current > Research Centres > Centre for Accident Research & Road Safety - Qld (CARRS-Q)
Current > QUT Faculties and Divisions > Faculty of Health
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Psychology & Counselling
|Copyright Owner:||Copyright 2015 The Author(s); licensee BioMed Central.|
|Deposited On:||24 Mar 2015 23:58|
|Last Modified:||26 May 2015 03:08|
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