Abstract
Automatically detecting temporal relations among dates/times and events mentioned in patient records has much potential to help medical staff in understanding disease progression and patients response to treatments. It can also facilitate evidence-based medicine (EBM) research. In this paper, we propose a hybrid temporal relation extraction approach which combines patient-record-specific rules and the Conditional Random Fields (CRFs) model to process patient records. We evaluate our approach on i2b2 dataset, and the results show our approach achieves an F-score of 61%.
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References
Chang, Y.-C., Dai, H.-J., Wu, J.C.-Y., Chen, J.-M., Tsai, R.T.-H., Hsu, W.-L.: TEMPTING system: A hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries. Journal of Biomedical Informatics 46(suppl.), S54–S60 (2013), 2012 i2b2 NLP Challenge on Temporal Relations in Clinical Data
Kovaevi, A., Dehghan, A., Filannino, M., Keane, J.A., Nenadic, G.: Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives. Journal of the American Medical Informatics Association (2013)
Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 282–289. Morgan Kaufmann Publishers Inc., San Francisco (2001)
Pustejovsky, J., Hanks, P., Sauri, R., See, A., Gaizauskas, R., Setzer, A., Radev, D., Sundheim, B., Day, D., Ferro, L., Lazo, M.: The TIMEBANK corpus. In: Proceedings of Corpus Linguistics 2003, Lancaster, pp. 647–656 (March 2003)
Sha, F., Pereira, F.: Shallow parsing with conditional random fields. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, pp. 134–141. Association for Computational Linguistics, Stroudsburg (2003)
Sohn, S., Wagholikar, K.B., Li, D., Jonnalagadda, S.R., Tao, C., Elayavilli, R.K., Liu, H.: Comprehensive temporal information detection from clinical text: medical events, time, and tlink identification. Journal of the American Medical Informatics Association 20(5), 836–842 (2013)
Sun, W., Rumshisky, A., Uzuner, O.: Evaluating temporal relations in clinical text: 2012 i2b2 challenge. Journal of the American Medical Informatics Association (2013), doi:10.1136/amiajnl-2013-001628
Tang, B., Wu, Y., Jiang, M., Chen, Y., Denny, J.C., Xu, H.: A hybrid system for temporal information extraction from clinical text. Journal of the American Medical Informatics Association 20(5), 828–835 (2013)
Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theor. 13(2), 260–269 (2006)
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Yang, YL., Lai, PT., Tsai, R.TH. (2014). A Hybrid System for Temporal Relation Extraction from Discharge Summaries. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_35
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DOI: https://doi.org/10.1007/978-3-319-13987-6_35
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
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