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Authors: Mahbub Alam 1 ; Aron Henriksson 1 ; John Valik 2 ; Logan Ward 3 ; Pontus Naucler 2 and Hercules Dalianis 1

Affiliations: 1 Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden ; 2 Division of Infectious Disease, Department of Medicine, Karolinska Institute, Stockholm, Sweden, Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden ; 3 Treat Systems ApS, Aalborg, Denmark

ISBN: 978-989-758-398-8

Keyword(s): Sepsis, Early Prediction, Machine Learning, Deep Learning, Health Informatics, Healthcare Analytics.

Abstract: Sepsis is a life-threatening complication to infections, and early treatment is key for survival. Symptoms of sepsis are difficult to recognize, but prediction models using data from electronic health records (EHRs) can facilitate early detection and intervention. Recently, deep learning architectures have been proposed for the early prediction of sepsis. However, most efforts rely on high-resolution data from intensive care units (ICUs). Prediction of sepsis in the non-ICU setting, where hospitalization periods vary greatly in length and data is more sparse, is not as well studied. It is also not clear how to learn effectively from longitudinal EHR data, which can be represented as a sequence of time windows. In this article, we evaluate the use of an LSTM network for early prediction of sepsis according to Sepsis-3 criteria in a general hospital population. An empirical investigation using six different time window sizes is conducted. The best model uses a two-hour window and assume s data is missing not at random, clearly outperforming scoring systems commonly used in healthcare today. It is concluded that the size of the time window has a considerable impact on predictive performance when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis. (More)

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Paper citation in several formats:
Alam, Mahbub Ul; Henriksson, A.; Valik, J.; Ward, L.; Naucler, P. and Dalianis, H. (2020). Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-398-8, pages 45-55. DOI: 10.5220/0008911400450055

@conference{healthinf20,
author={Alam, Mahbub Ul and Aron Henriksson. and John Karlsson Valik. and Logan Ward. and Pontus Naucler. and Hercules Dalianis.},
title={Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2020},
pages={45-55},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008911400450055},
isbn={978-989-758-398-8},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - Deep Learning from Heterogeneous Sequences of Sparse Medical Data for Early Prediction of Sepsis
SN - 978-989-758-398-8
AU - Alam, Mahbub Ul
AU - Henriksson, A.
AU - Valik, J.
AU - Ward, L.
AU - Naucler, P.
AU - Dalianis, H.
PY - 2020
SP - 45
EP - 55
DO - 10.5220/0008911400450055

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