Injury narrative text classification using factorization model

Lin Chen, Kirsten Vallmuur, Richi Nayak
<span title="2015-05-20">2015</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bnylrk2y7bfnrn7u2f2vjkx7ta" style="color: black;">BMC Medical Informatics and Decision Making</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1472-6947-15-s1-s5">doi:10.1186/1472-6947-15-s1-s5</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26043671">pmid:26043671</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4460654/">pmcid:PMC4460654</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4j25q6uckna5ldyptr3fzcfiv4">fatcat:4j25q6uckna5ldyptr3fzcfiv4</a> </span>
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