title: Injury narrative text classification using factorization model.
abstract: 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.
authors.forename: Lin
authors.initials: L
authors.lastname: Chen
authors.forename: Kirsten
authors.initials: K
authors.lastname: Vallmuur
authors.forename: Richi
authors.initials: R
authors.lastname: Nayak
mesh_terms: D057225:Data Mining / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008490:Medical Informatics / Q000379:methods*; D008499:Medical Records; D033262:Narration; D014947:Wounds and Injuries