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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases pp 472–487Cite as

Hypotensive Episode Prediction in ICUs via Observation Window Splitting

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11053)

Abstract

Hypotension, defined as dangerously low blood pressure, is a significant risk factor in intensive care units (ICUs), which requires a prompt therapeutic intervention. The goal of our research is to predict an impending Hypotensive Episode (HE) by time series analysis of continuously monitored physiological vital signs. Our prognostic model is based on the last Observation Window (OW) at the prediction time. Existing clinical episode prediction studies used a single OW of 5–120 min to extract predictive features, with no significant improvement reported when longer OWs were used. In this work we have developed the In-Window Segmentation (InWiSe) method for time series prediction, which splits a single OW into several sub-windows of equal size. The resulting feature set combines the features extracted from each observation sub-window and then this combined set is used by the Extreme Gradient Boosting (XGBoost) binary classifier to produce an episode prediction model. We evaluate the proposed approach on three retrospective ICU datasets (extracted from MIMIC II, Soroka and Hadassah databases) using cross-validation on each dataset separately, as well as by cross-dataset validation. The results show that InWiSe is superior to existing methods in terms of the area under the ROC curve (AUC).

Keywords

  • Time series analysis
  • Clinical episode prediction
  • Feature extraction
  • Intensive care
  • Patient monitoring

Partially supported by the Cincinnati Children’s Hospital Medical Center; In collaboration with Soroka Medical Center in Beer-Sheva and Hadassah University Hospital, Ein Karem, Jerusalem

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Notes

  1. 1.

    Recently, The MIMIC III waveform database Matched Subset, four times larger than the MIMIC II subset, was published

  2. 2.

    All improvement percentages are in terms of a ratio between the two measures

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Authors and Affiliations

  1. Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel

    Elad Tsur & Mark Last

  2. Division of Pediatric Surgery, MLC 2023, Children’s Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA

    Victor F. Garcia

  3. Pediatric Surgery Department, Hadassah University Hospital, Ein-Karem, 9112001, Jerusalem, Israel

    Raphael Udassin

  4. General Intensive Care Unit, Soroka Medical Center, Beer Sheva, Israel

    Moti Klein & Evgeni Brotfain

Authors
  1. Elad Tsur
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  2. Mark Last
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  3. Victor F. Garcia
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  4. Raphael Udassin
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  5. Moti Klein
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  6. Evgeni Brotfain
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Corresponding author

Correspondence to Mark Last .

Editor information

Editors and Affiliations

  1. Leuphana University, Lüneburg, Germany

    Prof. Dr. Ulf Brefeld

  2. National University of Ireland, Galway, Ireland

    Edward Curry

  3. IBM Research - Ireland, Dublin, Ireland

    Elizabeth Daly

  4. University College Dublin, Dublin, Ireland

    Dr. Brian MacNamee

  5. Nokia (Ireland), Dublin, Ireland

    Alice Marascu

  6. Vodafone, Milan, Italy

    Fabio Pinelli

  7. IBM Research - Ireland, Dublin, Ireland

    Michele Berlingerio

  8. University College Dublin, Dublin, Ireland

    Neil Hurley

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Tsur, E., Last, M., Garcia, V.F., Udassin, R., Klein, M., Brotfain, E. (2019). Hypotensive Episode Prediction in ICUs via Observation Window Splitting. In: , et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018. Lecture Notes in Computer Science(), vol 11053. Springer, Cham. https://doi.org/10.1007/978-3-030-10997-4_29

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  • DOI: https://doi.org/10.1007/978-3-030-10997-4_29

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