Abstract
We formulate the task of treatment recommendation as a sequence-to-sequence prediction model that takes the time–ordered medical history as input, and predicts a sequence of future clinical procedures and medications. It is built on the premise that an effective treatment plan may have long–term dependencies from previous medical history. We approach the problem by using a memory–augmented neural network, in particular, by leveraging the recent differentiable neural computer that consists of a neural controller and an external memory module. Differing from the original model, we use dual controllers, one for encoding the history followed by another for decoding the treatment sequences. In the encoding phase, the memory is updated as new input is read; at the end of this phase, the memory holds not only the medical history but also the information about the current illness. During the decoding phase, the memory is write–protected. The decoding controller generates a treatment sequence, one treatment option at a time. The resulting dual controller write–protected memory–augmented neural network is demonstrated on the MIMIC-III dataset on two tasks: procedure prediction and medication prescription. The results show improved performance over both traditional bag-of-words and sequence-to-sequence methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
The metrics actually are at disadvantage to the proposed sequence-to-sequence model, but we use to make them easy to compare against non-sequential methods.
References
Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: ICLR (2015)
Bajor, J.M., Lasko, T.A.: Predicting medications from diagnostic codes with recurrent neural networks. In: ICLR (2017)
Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A., Stewart, W.: RETAIN: an interpretable predictive model for healthcare using reverse time attention mechanism. In: Advances in Neural Information Processing Systems, pp. 3504–3512 (2016)
Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_51
Graves, A., Wayne, G., Danihelka, I.: Neural turing machines. arXiv e-prints, October 2014
Graves, A., Wayne, G., Reynolds, M., Harley, T., Danihelka, I., Grabska-Barwińska, A., Colmenarejo, S.G., Grefenstette, E., Ramalho, T., Agapiou, J., et al.: Hybrid computing using a neural network with dynamic external memory. Nature 538(7626), 471–476 (2016)
Hasan, S.A., Zhao, S., Datla, V.V., Liu, J., Lee, K., Qadir, A., Prakash, A., Farri, O.: Clinical question answering using key-value memory networks and knowledge graph. In: TREC (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Johnson, A.E.W., Pollard, T.J., Shen, L., Lehman, L.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)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lipton, Z., Kale, D., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks. In: International Conference on Learning Representations (ICLR 2016) (2016)
Ma, C., Shen, C., Dick, A., van den Hengel, A.: Visual question answering with memory-augmented networks. arXiv preprint arXiv:1707.04968 (2017)
Ma, F., Chitta, R., Zhou, J., You, Q., Sun, T., Gao, J.: Dipole: diagnosis prediction in healthcare via attention-based bidirectional recurrent neural networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1903–1911. ACM (2017)
Nguyen, P., Tran, T., Wickramasinghe, N., Venkatesh, S.: Deepr: a convolutional net for medical records. J. Biomed. Health Inform. 21(1), 22–30 (2017)
Pham, T., Tran, T., Phung, D., Venkatesh, S.: Predicting healthcare trajectories from medical records: a deep learning approach. J. Biomed. Inform. 69, 218–229 (2017)
Prakash, A., Zhao, S., Hasan, S.A., Datla, V.V., Lee, K., Qadir, A., Liu, J., Farri, O.: Condensed memory networks for clinical diagnostic inferencing. In: AAAI, pp. 3274–3280 (2017)
Rifkin, R., Klautau, A.: In defense of one-vs-all classification. J. Mach. Learn. Res. 5(Jan), 101–141 (2004)
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International Conference on Machine Learning, pp. 1842–1850 (2016)
Sukhbaatar, S., Weston, J., Fergus, R., et al.: End-to-end memory networks. In: Advances in Neural Information Processing Systems, pp. 2440–2448 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: International Conference on Machine Learning (2014)
Tan, J., Ung, M., Cheng, C., Greene, C.S.: Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. In: Pacific Symposium on Biocomputing Co-Chairs, pp. 132–143. World Scientific (2014)
Weston, J., Chopra, S., Bordes, A.: Memory networks. arXiv preprint arXiv:1410.3916 (2014)
Zhang, Y., Chen, R., Tang, J., Stewart, W.F., Sun, J.: Leap: learning to prescribe effective and safe treatment combinations for multimorbidity. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1315–1324. ACM (2017)
Zurada, J.: End effector target position learning using feedforward with error back-propagation and recurrent neural networks. In: 1994 IEEE International Conference on IEEE World Congress on Computational Intelligence Neural Networks, vol. 4, pp. 2633–2638. IEEE (1994)
Acknowledgments
The paper is partly supported by the Telstra-Deakin CoE in Big Data and Machine Learning.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Le, H., Tran, T., Venkatesh, S. (2018). Dual Control Memory Augmented Neural Networks for Treatment Recommendations. 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 10939. Springer, Cham. https://doi.org/10.1007/978-3-319-93040-4_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-93040-4_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93039-8
Online ISBN: 978-3-319-93040-4
eBook Packages: Computer ScienceComputer Science (R0)