DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

  • Trang Pham
  • Truyen Tran
  • Dinh Phung
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9652)


Personalized predictive medicine necessitates modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, a deep dynamic neural network that reads medical records and predicts future medical outcomes. At the data level, DeepCare models patient health state trajectories with explicit memory of illness. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timing by moderating the forgetting and consolidation of illness memory. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling and readmission prediction in diabetes, a chronic disease with large economic burden. The results show improved modeling and risk prediction accuracy.


  1. 1.
    Arandjelović, O.: Discovering hospital admission patterns using models learnt from electronic hospital records. Bioinformatics. btv508 (2015)Google Scholar
  2. 2.
    Corbin, J.M., Strauss, A.: A nursing model for chronic illness management based upon the trajectory framework. Res. Theory Nurs. Pract. 5(3), 155–174 (1991)Google Scholar
  3. 3.
    Futoma, J., Morris, J., Lucas, J.: A comparison of models for predicting early hospital readmissions. J. Biomed. Inform. 56, 229–238 (2015)CrossRefGoogle Scholar
  4. 4.
    Granger, B.B., Moser, D., Germino, B., Harrell, J., Ekman, I.: Caring for patients with chronic heart failure: the trajectory model. Eur. J. Cardiovasc. Nurs. 5(3), 222–227 (2006)CrossRefGoogle Scholar
  5. 5.
    Graves, A.: Generating sequences with recurrent neural networks (2013). arXiv preprint arXiv:1308.0850
  6. 6.
    Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for unconstrained handwriting recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 855–868 (2009)CrossRefGoogle Scholar
  7. 7.
    Henly, S.J., Wyman, J.F., Findorff, M.J.: Health and illness over time: the trajectory perspective in nursing science. Nurs. Res. 60(3 Suppl), S5 (2011)CrossRefGoogle Scholar
  8. 8.
    Henriques, R., Antunes, C., Madeira, S.C.: Generative modeling of repositories of health records for predictive tasks. Data Min. Knowl. Discov. 29(4), 999–1032 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRefGoogle Scholar
  10. 10.
    Jensen, A.B., Moseley, P.L., Oprea, T.I., Ellesøe, S.G., Eriksson, R., Schmock, H., Jensen, P.B., Jensen, L.J., Brunak, S.: Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5, 10 (2014)Google Scholar
  11. 11.
    Jensen, P.B., Jensen, L.J., Brunak, S.: Mining electronic health records: towards better research applications and clinical care. Nat. Rev. Genet. 13(6), 395–405 (2012)CrossRefGoogle Scholar
  12. 12.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  13. 13.
    Liang, Z., Zhang, G., Huang, J.X., Hu, Q.V.: Deep learning for healthcare decision making with EMRs. In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 556–559. IEEE (2014)Google Scholar
  14. 14.
    Liu, C., Wang, F., Hu, J., Xiong, H.: Temporal phenotyping from longitudinal electronic health records: a graph based framework. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 705–714. ACM (2015)Google Scholar
  15. 15.
    Mathias, J.S., Agrawal, A., Feinglass, J., Cooper, A.J., Baker, D.W., Choudhary, A.: Development of a 5 year life expectancy index in older adults using predictive mining of electronic health record data. J. Am. Med. Inf. Assoc. 20(e1), e118–e124 (2013)CrossRefGoogle Scholar
  16. 16.
    Orphanou, K., Stassopoulou, A., Keravnou, E.: Temporal abstraction and temporal Bayesian networks in clinical domains: a survey. Artif. Intell. Med. 60(3), 133–149 (2014)CrossRefGoogle Scholar
  17. 17.
    Srivastava, N., Mansimov, E., Salakhutdinov, R.: Unsupervised learning of video representations using LSTMS (2015). arXiv preprint arXiv:1502.04681
  18. 18.
    Sutskever, I., Vinyals, O., Le, Q.V.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)Google Scholar
  19. 19.
    Tran, T., Phung, D., Luo, W., Harvey, R., Berk, M., Venkatesh, S.: An integrated framework for suicide risk prediction. In: KDD 2013 (2013)Google Scholar
  20. 20.
    Tran, T., Luo, W., Phung, D., Gupta, S., Rana, S., Kennedy, R.L., Larkins, A., Venkatesh, S.: A framework for feature extraction from hospital medical data with applications in risk prediction. BMC Bioinform. 15(1), 6596 (2014)CrossRefGoogle Scholar
  21. 21.
    Tran, T., Nguyen, T.D., Phung, D., Venkatesh, S.: Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). J. Biomed. Inform. 54, 96–105 (2015)CrossRefGoogle Scholar
  22. 22.
    Wang, X., Sontag, D., Wang, F.: Unsupervised learning of disease progression models. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 85–94. ACM (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Trang Pham
    • 1
  • Truyen Tran
    • 1
  • Dinh Phung
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Center for Pattern Recognition and Data Analytics School of Information TechnologyDeakin UniversityGeelongAustralia

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