Semi-automated text mining strategies for identifying rare causes of injuries from emergency room triage data

References

• Aggarwal, C. C., and Zhai, C. (2012) An introduction to text mining. Pp. 1–10 in Mining Text Data, C. C. Aggarwal and C. Zhai (Eds.). Retrieved from http://link.springer.com/chapter/10.1007/978-1-4614-3223-4_1
   Google Scholar
• Batista, G. E. A. P. A., Prati, R. C., and Monard, M. C. (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter, 6(1), 20. doi:10.1145/1007730.1007735
   Google Scholar
• Bertke, S. J., Meyers, A. R., Wurzelbacher, S. J., Bell, J., Lampl, M. L., and Robins, D. (2012) Development and evaluation of a Naïve Bayesian model for coding causation of workers’ compensation claims. Journal of Safety Research, 43(5–6), 327–332. doi:10.1016/j.jsr.2012.10.012
   PubMed Web of Science ®Google Scholar
• Bertke, S. J., Meyers, A. R., Wurzelbacher, S. J., Measure, A., Lampl, M. P., and Robins, D. (2016) Comparison of methods for auto-coding causation of injury narratives. Accident Analysis & Prevention, 88, 117–123. doi:10.1016/j.aap.2015.12.006
   Web of Science ®Google Scholar
• Chawla, N. V., Japkowicz, N., and Kotcz, A. (2004) Editorial: Special issue on learning from imbalanced data sets. ACM Sigkdd Explorations Newsletter, 6(1), 1–6. Retrieved from http://dl.acm.org/citation.cfm?id=1007733
   Google Scholar
• Chen, L., Vallmuur, K., and Nayak, R. (2015) Injury narrative text classification using factorization model. BMC Medical Informatics and Decision Making, 15(Suppl 1), S5. doi:10.1186/1472-6947-15-S1-S5
   Google Scholar
• Corns, H. L., Marucci, H. R., and Lehto, M. R. (2007) Development of an approach for optimizing the accuracy of classifying claims narratives using a machine learning tool (TEXTMINER[4]). Pp. 411–416 in Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design. Human Interface 2007. Lecture Notes in Computer Science, M. J. Smith and G. Salvendy (Eds.). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-540-73345-4_47
   Google Scholar
• David W. Hosmer, Jr. (2004) Applied Logistic Regression. John Wiley & Sons.Retrieved from http://books.google.com/books?id=Po0RLQ7USIMC
   Google Scholar
• Fan, R., Chang, K., Hsieh, C., Wang, X., and Lin, C. (2008) LIBLINEAR: A library for large linear classification. Retrieved from http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.140.9959
   Google Scholar
• Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., and Trigg, L. (2005) Weka. Pp. 1305–1314 in Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach (Eds.). Retrieved from http://link.springer.com/chapter/10.1007/0-387-25465-X_62
   Google Scholar
• Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009) The WEKA data mining software: An update. SIGKDD Explor. Newsl., 11(1), 10–18. doi:10.1145/1656274.1656278
   Google Scholar
• Japkowicz, N. (2000) The class imbalance problem: Significance and strategies. In Proceedings of the International Conference on Artificial Intelligence. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.35.1693&rep=rep1&type=pdf
   Google Scholar
• Lauría, E. J. M., and March, A. D. (2011) Combining Bayesian text classification and shrinkage to automate healthcare coding: A data quality analysis. J. Data and Information Quality, 2(3), 13:1–13:22. doi:10.1145/2063504.2063506
   Google Scholar
• Lehto, M., Marucci-Wellman, H., and Corns, H. (2009) Bayesian methods: A useful tool for classifying injury narratives into cause groups. Injury Prevention: Journal of the International Society for Child and Adolescent Injury Prevention, 15(4), 259–265. doi:10.1136/ip.2008.021337
   Web of Science ®Google Scholar
• Manning, C. D., Raghavan, P., and Schütze, H. (2008) Introduction to Information Retrieval. Cambridge University Press. Retrieved from http://books.google.com/books?id=t1PoSh4uwVcC
   Google Scholar
• Marucci-Wellman, H. R., Corns, H. L., and Lehto, M. R. (2017) Classifying injury narratives of large administrative databases for surveillance—A practical approach combining machine learning ensembles and human review. Accident Analysis & Prevention, 98, 359–371. doi:10.1016/j.aap.2016.10.014
   PubMed Web of Science ®Google Scholar
• Marucci-Wellman, H. R., Lehto, M. R., and Corns, H. L. (2015) A practical tool for public health surveillance: Semi-automated coding of short injury narratives from large administrative databases using Naïve Bayes algorithms. Accident Analysis & Prevention, 84, 165–176. doi:10.1016/j.aap.2015.06.014
   Web of Science ®Google Scholar
• McKenzie, K., Scott, D. A., Campbell, M. A., and McClure, R. J. (2010) The use of narrative text for injury surveillance research: A systematic review. Accident Analysis & Prevention, 42(2), 354–363. doi:10.1016/j.aap.2009.09.020
   PubMed Web of Science ®Google Scholar
• Measure, A. C. (2014). Automated Coding of Worker Injury Narratives. Boston, MA: JSM 2014 - Government Statistics Section. Retrieved from http://www.bls.gov/osmr/pdf/st140040.pdf
   Google Scholar
• Nanda, G., Grattan, K. M., Chu, M. T., Davis, L. K., and Lehto, M. R. (2016) Bayesian decision support for coding occupational injury data. Journal of Safety Research, 57, 71–82. doi:10.1016/j.jsr.2016.03.001
   PubMed Web of Science ®Google Scholar
• Nanda, G., Vallmuur, K., and Lehto, M. R. (2018) Improving autocoding performance of rare categories in injury classification: Is more training data or filtering the solution? Accident Analysis & Prevention, 110, 115–127. doi:10.1016/j.aap.2017.10.020
   Google Scholar
• Ng, A. Y., and Jordan, M. I. (2002) On discriminative vs. generative classifiers: A comparison of Logistic Regression and Naive Bayes. Pp. 841–848 in Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic (NIPS'01), T. G. Dietterich, S. Becker, and Z. Ghahramani (Eds.). Cambridge, MA: MIT Press. Retrieved from http://papers.nips.cc/paper/2020-on-discriminative-vs-generative-classifiers-a-comparison-of-logistic-regression-and-naive-bayes.pdf
   Google Scholar
• Phua, C., Alahakoon, D., and Lee, V. (2004) Minority report in fraud detection. ACM SIGKDD Explorations Newsletter, 6(1), 50. doi:10.1145/1007730.1007738
   Google Scholar
• Prati, R. C., Batista, G. E. A. P. A., and Silva, D. F. (2015). Class imbalance revisited: A new experimental setup to assess the performance of treatment methods. Knowledge and Information Systems, 45(1), 247–270. doi:10.1007/s10115-014-0794-3
   Web of Science ®Google Scholar
• Provost, F. (2000). Machine learning from imbalanced data sets 101. Retrieved from http://www.aaai.org/Papers/Workshops/2000/WS-00-05/WS00-05-001.pdf
   Google Scholar
• QISU Guide to Collecting An Accurate Text Description of an Injury Event. (2011).South Brisbane, QLD: Queensland Injury Surveillance Unit.
   Google Scholar
• Queensland Injury Surveillance Unit. (n.d.) Retrieved from http://www.qisu.org.au/ModCoreFrontEnd/index.asp?pageid=109
   Google Scholar
• Rizzo, S. G., Montesi, D., Fabbri, A., and Marchesini, G. (2015) ICD code retrieval: Novel approach for assisted disease classification. Pp. 147–161 in Data Integration in the Life Sciences. DILS 2015. Lecture Notes in Computer Science, N. Ashish and J.-L. Ambite (Eds.), . Springer International Publishing. Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-21843-4_12
   Google Scholar
• Smith, G. S., Timmons, R. A., Lombardi, D. A., Mamidi, D. K., Matz, S., Courtney, T. K., and Perry, M. J. (2006) Work-related ladder fall fractures: Identification and diagnosis validation using narrative text. Accident Analysis & Prevention, 38(5), 973–980. doi:10.1016/j.aap.2006.04.008
   PubMed Web of Science ®Google Scholar
• Sun, A., Lim, E.-P., and Liu, Y. (2009) On strategies for imbalanced text classification using SVM: A comparative study. Decision Support Systems, 48(1), 191–201. doi:10.1016/j.dss.2009.07.011
   Web of Science ®Google Scholar
• Vallmuur, K. (2015) Machine learning approaches to analysing textual injury surveillance data: A systematic review. Accident Analysis & Prevention, 79, 41–49. doi:10.1016/j.aap.2015.03.018
   Web of Science ®Google Scholar
• Vallmuur, K., Marucci-Wellman, H. R., Taylor, J. A., Lehto, M., Corns, H. L., and Smith, G. S. (2016) Harnessing information from injury narratives in the “big data” era: Understanding and applying machine learning for injury surveillance. Injury Prevention, 22(Suppl 1), i34–i42. doi:10.1136/injuryprev-2015-041813
   Google Scholar
• Van Hulse, J., Khoshgoftaar, T. M., and Napolitano, A. (2007) Experimental Perspectives on Learning from Imbalanced Data (pp. 935–942). ACM, New York, NY. doi:10.1145/1273496.1273614
   Google Scholar
• Wellman, H. M., Lehto, M. R., Sorock, G. S., and Smith, G. S. (2004) Computerized coding of injury narrative data from the National Health Interview Survey. Accident Analysis & Prevention, 36(2), 165–171. doi:10.1016/S0001-4575(02)00146-X
   Web of Science ®Google Scholar
• Zhu, X. (2007) Advanced NLP: Text categorization with Logistic Regression. Retrieved from http://pages.cs.wisc.edu/∼jerryzhu/cs838/LR.pdf
   Google Scholar