Abstract
Sepsis is a major risk to patient in the Intensive Care Unit (ICU) and is associated with substantial treatment expenditure. As most cases of sepsis are acquired during the ICU stay, timely identification and intervention play a crucial role in enhancing the survival rate of septic patients and reducing the financial burden of treatment. Prior research has established that machine learning approaches surpass conventional scoring systems in predicting sepsis. However, these existing machine learning methodologies exhibit limitations when predicting sepsis with flexible window settings. Their performance is heavily reliant on the selection of prediction and feature windows, which restricts their practical applicability in clinical settings. This paper aims to overcome this challenge by introducing a model selection approach for sepsis prediction, utilizing a window-controlled strategy. Experimental results demonstrate that our proposed model outperforms existing models and exhibits enhanced stability across various prediction and feature windows.
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
Similar content being viewed by others
References
Angus, D.C., Linde-Zwirble, W.T., Lidicker, J., Clermont, G., Carcillo, J., Pinsky, M.R.: Epidemiology of severe sepsis in the united states: analysis of incidence, outcome, and associated costs of care. Crit. Care Med. 29(7), 1303–1310 (2001)
Baral, S., Alsadoon, A., Prasad, P., Al Aloussi, S., Alsadoon, O.H.: A novel solution of using deep learning for early prediction cardiac arrest in sepsis patient: enhanced bidirectional long short-term memory (lstm). Multimedia Tools Appl. 80, 32639–32664 (2021)
Bone, R.C., et al.: Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Chest 101(6), 1644–1655 (1992)
Brink, A., et al.: Predicting mortality in patients with suspected sepsis at the emergency department; a retrospective cohort study comparing qsofa, sirs and national early warning score. PLoS ONE 14(1), e0211133 (2019)
Calvert, J.S., et al.: A computational approach to early sepsis detection. Comput. Biol. Med. 74, 69–73 (2016)
Çıldır, E., Bulut, M., Akalın, H., Kocabaş, E., Ocakoğlu, G., Aydın, ŞA.: Evaluation of the modified meds, mews score and charlson comorbidity index in patients with community acquired sepsis in the emergency department. Intern. Emerg. Med. 8, 255–260 (2013)
Dykes, L.A., Heintz, S.J., Heintz, B.H., Livorsi, D.J., Egge, J.A., Lund, B.C.: Contrasting qSOFA and sirs criteria for early sepsis identification in a veteran population. Fed. Pract. 36(Suppl 2), S21 (2019)
Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(1), 1997–2017 (2019)
Esteban, C., Staeck, O., Baier, S., Yang, Y., Tresp, V.: Predicting clinical events by combining static and dynamic information using recurrent neural networks. In: 2016 IEEE International Conference on Healthcare Informatics (ICHI), pp. 93–101. IEEE (2016)
Evans, T.: Diagnosis and management of sepsis. Clin. Med. 18(2), 146 (2018)
Fleuren, L.M., et al.: Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med. 46, 383–400 (2020)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Islam, M.M., Nasrin, T., Walther, B.A., Wu, C.C., Yang, H.C., Li, Y.C.: Prediction of sepsis patients using machine learning approach: a meta-analysis. Comput. Methods Programs Biomed. 170, 1–9 (2019)
Kaji, D.A., Zech, J.R., Kim, J.S., Cho, S.K., Dangayach, N.S., Costa, A.B., Oermann, E.K.: An attention based deep learning model of clinical events in the intensive care unit. PLoS ONE 14(2), e0211057 (2019)
Khoshnevisan, F., Ivy, J., Capan, M., Arnold, R., Huddleston, J., Chi, M.: Recent temporal pattern mining for septic shock early prediction. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 229–240. IEEE (2018)
Levy, M.M., et al.: 2001 SCCM/ESICM/ACCP/ATS/sis international sepsis definitions conference. Intensive Care Med. 29, 530–538 (2003)
Lin, C., et al.: Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 219–228. IEEE (2018)
Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015)
Liu, V.X., et al.: The timing of early antibiotics and hospital mortality in sepsis. Am. J. Respir. Crit. Care Med. 196(7), 856–863 (2017)
Marik, P.E., Taeb, A.M.: Sirs, qSOFA and new sepsis definition. J. Thorac. Dis. 9(4), 943 (2017)
Martin, G.S., Mannino, D.M., Eaton, S., Moss, M.: The epidemiology of sepsis in the united states from 1979 through 2000. N. Engl. J. Med. 348(16), 1546–1554 (2003)
Moor, M., Rieck, B., Horn, M., Jutzeler, C.R., Borgwardt, K.: Early prediction of sepsis in the ICU using machine learning: a systematic review. Front. Med. 8, 607952 (2021)
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(1), 5485–5551 (2020)
Ren, P., et al.: A comprehensive survey of neural architecture search: challenges and solutions. ACM Comput. Surv. (CSUR) 54(4), 1–34 (2021)
Rhodes, A., et al.: Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med. 43, 304–377 (2017)
Sakr, Y., et al.: Sepsis in intensive care unit patients: worldwide data from the intensive care over nations audit. In: Open Forum Infectious Diseases, vol. 5, p. ofy313. Oxford University Press US (2018)
Scherpf, M., Gräßer, F., Malberg, H., Zaunseder, S.: Predicting sepsis with a recurrent neural network using the mimic iii database. Comput. Biol. Med. 113, 103395 (2019)
Singer, M., et al.: The third international consensus definitions for sepsis and septic shock (sepsis-3). JAMA 315(8), 801–810 (2016)
Van der Woude, S., Van Doormaal, F., Hutten, B., Nellen, F., Holleman, F.: Classifying sepsis patients in the emergency department using sirs, qSOFA or mews. Neth. J. Med. 76(4), 158–166 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Su, S., Lan, S., Zhang, Z., Zhu, A. (2023). Window-Controlled Sepsis Prediction Using a Model Selection Approach. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14180. Springer, Cham. https://doi.org/10.1007/978-3-031-46677-9_31
Download citation
DOI: https://doi.org/10.1007/978-3-031-46677-9_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-46676-2
Online ISBN: 978-3-031-46677-9
eBook Packages: Computer ScienceComputer Science (R0)