Abstract
Appropriate treatment regimens play a vital role in improving patient health status. Although some achievements have been made, few of the recent studies of learning treatment regimens have exploited different kinds of patient information due to the difficulty in adopting heterogeneous data to many data mining methods. Moreover, current studies seem too rigid with fixed intervals of treatment periods corresponding to the varying lengths of hospital stay. To this end, this work proposes a generic data-driven framework which can derive group-treatment regimens from electronic medical records by utilizing a mixed-variate restricted Boltzmann machine and incorporating medical domain knowledge. We conducted experiments on coronary artery disease as a case study. The obtained results show that the framework is promising and capable of assisting physicians in making clinical decisions.
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References
Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucl. Acids Res. 32(suppl_1), D267–D270 (2004)
Huang, Z., Dong, W., Bath, P., Ji, L., Duan, H.: On mining latent treatment patterns from electronic medical records. Data Min. Knowl. Discov. 29(4), 914–949 (2015)
Huang, Z., Lu, X., Duan, H.: On mining clinical pathway patterns from medical behaviors. Artif. Intell. Med. 56(1), 35–50 (2012)
Johnson, A.E., Pollard, T.J., Shen, L., Lehman, L.-W.H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L.A., Mark, R.G.: MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)
Johnston, R.L., Carius, H.J., Skelly, A., Ferreira, A., Milnes, F., Mitchell, P.: A retrospective study of Ranibizumab treatment regimens for neovascular age-related macular degeneration (nAMD) in Australia and the United Kingdom. Adv. Ther. 34(3), 703–712 (2017)
Merhej, E., Schockaert, S., McKelvey, T.G., De Cock, M.: Recommending treatments for comorbid patients using word-based and phrase-based alignment methods. In: 29th Benelux Conference on Artificial Intelligence (BNAIC 2017) (2017)
Savova, G.K., Masanz, J.J., Ogren, P.V., Zheng, J., Sohn, S., Kipper-Schuler, K.C., Chute, C.G.: Mayo clinical text analysis and knowledge extraction system (cTAKES): architecture, component evaluation and applications. J. Am. Med. Inform. Assoc. 17(5), 507–513 (2010)
Song, R., Wang, W., Zeng, D., Kosorok, M.R.: Penalized Q-learning for dynamic treatment regimens. Stat. Sin. 25(3), 901 (2015)
Sun, L., Liu, C., Guo, C., Xiong, H., Xie, Y.: Data-driven automatic treatment regimen development and recommendation. In: KDD, pp. 1865–1874 (2016)
Tamasauskas, D., Sakalauskas, V., Kriksciuniene, D.: Evaluation framework of hierarchical clustering methods for binary data. In: 2012 12th International Conference on Hybrid Intelligent Systems (HIS), pp. 421–426. IEEE (2012)
Tran, T., Phung, D., Venkatesh, S.: Mixed-variate restricted Boltzmann machines. arXiv preprint arXiv:1408.1160 (2014)
Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., Hassanali, M.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucl. Acids Res. 36(suppl_1), D901–D906 (2007)
Acknowledgement
This work is partially sponsored Asian Office of Aerospace R& D under agreement number FA2386-17-1-4094 and Vietnam National University at Ho Chi Minh City under the grant number B2015-42-02. We wish to thank Tu Dinh Nguyen for providing the implementation of the MV.RBM model.
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Hoang, K.H., Ho, T.B. (2018). Learning Treatment Regimens from Electronic Medical Records. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10937. Springer, Cham. https://doi.org/10.1007/978-3-319-93034-3_33
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DOI: https://doi.org/10.1007/978-3-319-93034-3_33
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