Abstract
This paper proposes a method to enrich the semantics of before/after relations based on the closeness between two events and contexts surrounding two events identified by a key event in a period. The proposed method captures four types of before/after relations: continuous relation, discrete relation, same contextual relation and different contextual relation. We derive five embedding methods from the combination of the four relation types, and apply them to clinical data for the prediction of hospital length of stay where the events are treatments, the key event is surgery and the period is seven days after hospital admission. The experimental results showed that on the whole the embedding method employed all of the four relation types has higher scores of precision, recall, F1 score and accuracy than other embedding methods. This suggests that the potential for the elucidation of the candidates of medically meaningful temporal patterns increases by exploring the temporal patterns generated from the embedding method. A paired t-test indicated that significant differences are partially confirmed for discrete relation and same contextual relation but not confirmed for different contextual relation. We will apply the proposed method to a number of hospitals for performing the further analysis of the four relation types and elucidating medically meaningful temporal patterns.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Batal, I., Valizadegan, H., Cooper, G.F., Hauskrecht, M.: A temporal pattern mining approach for classifying electronic health record data. ACM Trans. Intell. Syst. Technol. 4(4), 1–22 (2013)
Dubois, D., Prade, H.: Processing fuzzy temporal knowledge. IEEE Trans. Syst. Man Cybern. 19(4), 729–744 (1989)
FDA: Statement from FDA Commissioner Scott Gottlieb, M.D., on FDA’s new strategic framework to advance use of real-world evidence to support development of drugs and biologics (2018). https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md-fdas-new-strategic-framework-advance-use-real-world
Giannoula, A., Gutierrez-Sacristán, A., Bravo, Á., Sanz, F., Furlong, L.I.: Identifying temporal patterns in patient disease trajectories using dynamic time warping: a population-based study. Sci. Rep. 8(1), 4216 (2018)
Matsuda, S., Fujimori, K., Kuwabara, K., Ishikawa, K.B., Fushimi, K.: Diagnosis procedure combination as an infrastructure for the clinical study. Asian Pac. J. Dis. Manag. 5(4), 81–87 (2011)
Mörchen, F.: Unsupervised pattern mining from symbolic temporal data. ACM SIGKDD Explor. Newslett. 9(1), 41–55 (2007)
Norén, G.N., Hopstadius, J., Bate, A., Star, K., Edwards, I.R.: Temporal pattern discovery in longitudinal electronic patient records. Data Min. Knowl. Disc. 20(3), 361–387 (2010). https://doi.org/10.1007/s10618-009-0152-3
Orphanou, K., Dagliati, A., Sacchi, L., Stassopoulou, A., Keravnou, E., Bellazzi, R.: Incorporating repeating temporal association rules in Naïve Bayes classifiers for coronary heart disease diagnosis. J. Biomed. Inform. 81, 74–82 (2018)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pustejovsky, J., et al.: TimeML: robust specification of event and temporal expressions in text. New Dir. Quest. Answ. 3, 28–34 (2003)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24(5), 513–523 (1988)
Star, K., Watson, S., Sandberg, L., Johansson, J., Edwards, I.R.: Longitudinal medical records as a complement to routine drug safety signal analysis. Pharmacoepidemiol. Drug Saf. 24(5), 486–494 (2015)
Yasunaga, H., Matsui, H., Horiguchi, H., Fushimi, K., Matsuda, S.: Application of the diagnosis procedure combination (DPC) data to clinical studies. J. UOEH 36(3), 191–197 (2014)
Yasunaga, H., Matsui, H., Horiguchi, H., Fushimi, K., Matsuda, S.: Clinical epidemiology and health services research using the diagnosis procedure combination database in Japan. Asian Pac. J. Dis. Manag. 7(1–2), 19–24 (2015)
Acknowledgment
This work was supported by JSPS KAKENHI Grant Number JP18K09948.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Matsuo, R., Yamazaki, T., Kushima, M., Araki, K. (2020). Enriching the Semantics of Temporal Relations for Temporal Pattern Mining. In: Fujita, H., Fournier-Viger, P., Ali, M., Sasaki, J. (eds) Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. IEA/AIE 2020. Lecture Notes in Computer Science(), vol 12144. Springer, Cham. https://doi.org/10.1007/978-3-030-55789-8_58
Download citation
DOI: https://doi.org/10.1007/978-3-030-55789-8_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-55788-1
Online ISBN: 978-3-030-55789-8
eBook Packages: Computer ScienceComputer Science (R0)