ArticleBMC medical informatics and decision making2015
Injury narrative text classification using factorization model.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Application of a Machine Learning-Based Decision Support Tool to Improve an Injury Surveillance System Workflow.Applied clinical informatics · 2022Article
- Detecting molecular subtypes from multi-omics datasets using SUMO.Cell reports methods · 2022Article
- Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2016Article
- A hierarchical method to automatically encode Chinese diagnoses through semantic similarity estimation.BMC medical informatics and decision making · 2016Article
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3 authors.
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No grant is acknowledged in the PubMed record.
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.
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