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International Joint Conference on Biomedical Engineering Systems and Technologies

BIOSTEC 2020: Biomedical Engineering Systems and Technologies pp 366–384Cite as

Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1400))

Abstract

The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.

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References

  1. Afzal, A., et al.: The cognitive internet of things: a unified perspective. Mob. Netw. Appl. 20(1), 72–85 (2015)

    Article  Google Scholar 

  2. Alam, M.U., Henriksson, A., Karlsson Valik, J., Ward, L., Naucler, P., Dalianis, H.: Deep learning from heterogeneous sequences of sparse medical data for early prediction of sepsis. In: 13th International Joint Conference on Biomedical Engineering Systems and Technologies, Valletta, Malta, 24–26 February 2020, vol. 5, pp. 45–55. SciTePress (2020)

    Google Scholar 

  3. Bahga, A., Madisetti, V.K.: Healthcare data integration and informatics in the cloud. Computer 48(2), 50–57 (2015)

    Article  Google Scholar 

  4. Behera, R.K., Bala, P.K., Dhir, A.: The emerging role of cognitive computing in healthcare: a systematic literature review. Int. J. Med. Inform. 129, 154–166 (2019)

    Article  Google Scholar 

  5. Coccoli, M., Maresca, P.: Adopting cognitive computing solutions in healthcare. J. e-Learn. Knowl. Soc. 14(1) (2018)

    Google Scholar 

  6. Delahanty, R.J., Alvarez, J., Flynn, L.M., Sherwin, R.L., Jones, S.S.: Development and evaluation of a machine learning model for the early identification of patients at risk for sepsis. Ann. Emerg. Med. 73, 334–344 (2019)

    Article  Google Scholar 

  7. Dimitrov, D.V.: Medical internet of things and big data in healthcare. Healthcare Inform. Res. 22(3), 156–163 (2016)

    Article  Google Scholar 

  8. Durga, S., Nag, R., Daniel, E.: Survey on machine learning and deep learning algorithms used in internet of things (IoT) healthcare. In: 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 1018–1022. IEEE (2019)

    Google Scholar 

  9. Estrela, V.V., Monteiro, A.C.B., França, R.P., Iano, Y., Khelassi, A., Razmjooy, N.: Health 4.0: applications, management, technologies and review. Med. Technol. J. 2(4), 262–276 (2018)

    Google Scholar 

  10. Ferrer, R., et al.: Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit. Care Med. 42(8), 1749–1755 (2014)

    Article  Google Scholar 

  11. Futoma, J., Hariharan, S., Heller, K.: Learning to detect sepsis with a multitask Gaussian process RNN classifier. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1174–1182. JMLR.org (2017)

    Google Scholar 

  12. Futoma, J., et al.: An improved multi-output gaussian process RNN with real-time validation for early sepsis detection. In: Doshi-Velez, F., Fackler, J., Kale, D., Ranganath, R., Wallace, B., Wiens, J. (eds.) Proceedings of the 2nd Machine Learning for Healthcare Conference. Proceedings of Machine Learning Research, Boston, Massachusetts, vol. 68, pp. 243–254. PMLR, 18–19 August 2017. http://proceedings.mlr.press/v68/futoma17a.html

  13. Gatouillat, A., Badr, Y., Massot, B., Sejdić, E.: Internet of medical things: a review of recent contributions dealing with cyber-physical systems in medicine. IEEE Internet of Things J. 5(5), 3810–3822 (2018)

    Article  Google Scholar 

  14. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  15. Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  16. Habibzadeh, H., Dinesh, K., Shishvan, O.R., Boggio-Dandry, A., Sharma, G., Soyata, T.: A survey of healthcare internet-of-things (HIoT): a clinical perspective. IEEE Internet of Things J. 7, 53–71 (2019)

    Article  Google Scholar 

  17. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  18. Irfan, M., Ahmad, N.: Internet of medical things: architectural model, motivational factors and impediments. In: 2018 15th Learning and Technology Conference (L&T), pp. 6–13. IEEE (2018)

    Google Scholar 

  19. Johnson, A.E., Stone, D.J., Celi, L.A., Pollard, T.J.: The mimic code repository: enabling reproducibility in critical care research. J. Am. Med. Inform. Assoc. 25(1), 32–39 (2018)

    Article  Google Scholar 

  20. Jones, A.E., et al.: Lactate clearance vs central venous oxygen saturation as goals of early sepsis therapy: a randomized clinical trial. Jama 303(8), 739–746 (2010)

    Article  Google Scholar 

  21. Kumar, A., et al.: Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit. Care Med. 34(6), 1589–1596 (2006)

    Article  Google Scholar 

  22. Lipton, Z.C.: The doctor just won’t accept that! arXiv preprint arXiv:1711.08037 (2017)

  23. Mishra, N., Lin, C.C., Chang, H.T.: A cognitive adopted framework for IoT big-data management and knowledge discovery prospective. Int. J. Distrib. Sens. Netw. 11(10), 718390 (2015)

    Google Scholar 

  24. Modha, D.S., Ananthanarayanan, R., Esser, S.K., Ndirango, A., Sherbondy, A.J., Singh, R.: Cognitive computing. Commun. ACM 54(8), 62–71 (2011)

    Article  Google Scholar 

  25. Moor, M., Horn, M., Rieck, B., Roqueiro, D., Borgwardt, K.: Early recognition of sepsis with Gaussian process temporal convolutional networks and dynamic time warping. arXiv preprint arXiv:1902.01659 (2019)

  26. Pagola-Lorz, I., et al.: Epidemiological study and genetic characterization of inherited muscle diseases in a northern Spanish region. Orphanet J. Rare Dis. 14(1), 276 (2019)

    Article  Google Scholar 

  27. Seymour, C.W., et al.: Time to treatment and mortality during mandated emergency care for sepsis. N. Engl. J. Med. 376(23), 2235–2244 (2017)

    Article  Google Scholar 

  28. Seymour, C.W., et al.: Assessment of clinical criteria for sepsis: for the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 315(8), 762–774 (2016)

    Article  Google Scholar 

  29. Shi, W., Dustdar, S.: The promise of edge computing. Computer 49(5), 78–81 (2016)

    Article  Google Scholar 

  30. Singer, M., et al.: The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA 315(8), 801–810 (2016)

    Article  Google Scholar 

  31. Steele, A.J., Denaxas, S.C., Shah, A.D., Hemingway, H., Luscombe, N.M.: Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease. PloS One 13(8), e0202344 (2018)

    Article  Google Scholar 

  32. Vincent, J.L., et al.: The sofa (sepsis-related organ failure assessment) score to describe organ dysfunction/failure. Intensive Care Med. 22(7), 707–710 (1996)

    Article  Google Scholar 

  33. Wu, Q., Ding, G., Xu, Y., Feng, S., Du, Z., Wang, J., Long, K.: Cognitive internet of things: a new paradigm beyond connection. IEEE Internet of Things J. 1(2), 129–143 (2014)

    Article  Google Scholar 

  34. Xiao, C., Choi, E., Sun, J.: Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J. Am. Med. Inform. Assoc. 25(10), 1419–1428 (2018)

    Article  Google Scholar 

  35. Yoon, J., Jordon, J., Van Der Schaar, M.: Gain: missing data imputation using generative adversarial nets. arXiv preprint arXiv:1806.02920 (2018)

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Authors and Affiliations

  1. Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

    Mahbub Ul Alam & Rahim Rahmani

Authors
  1. Mahbub Ul Alam
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  2. Rahim Rahmani
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Correspondence to Mahbub Ul Alam .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xuesong Ye

  2. Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal

    Filipe Soares

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. Universidad Politécnica de Madrid, Madrid, Spain

    Pedro Gómez Vilda

  5. University of Milano-Bicocca, Milan, Italy

    Federico Cabitza

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Cite this paper

Alam, M.U., Rahmani, R. (2021). Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_18

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  • DOI: https://doi.org/10.1007/978-3-030-72379-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72378-1

  • Online ISBN: 978-3-030-72379-8

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