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Journal of Clinical Monitoring and Computing

, Volume 33, Issue 1, pp 95–105 | Cite as

Predicting delayed cerebral ischemia after subarachnoid hemorrhage using physiological time series data

  • Soojin ParkEmail author
  • Murad Megjhani
  • Hans-Peter Frey
  • Edouard Grave
  • Chris Wiggins
  • Kalijah L. Terilli
  • David J. Roh
  • Angela Velazquez
  • Sachin Agarwal
  • E. Sander ConnollyJr.
  • J. Michael Schmidt
  • Jan Claassen
  • Noemie Elhadad
Original Research

Abstract

To develop and validate a prediction model for delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) using a temporal unsupervised feature engineering approach, demonstrating improved precision over standard features. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Baseline information and standard grading scales were evaluated: age, sex, Hunt Hess grade, modified Fisher Scale (mFS), and Glasgow Coma Scale (GCS). An unsupervised approach applying random kernels was used to extract features from physiological time series (systolic and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (Partial Least Squares, linear and kernel Support Vector Machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.58. Combined demographics and grading scales: AUC 0.60. Random kernel derived physiologic features: AUC 0.74. Combined baseline and physiologic features with redundant feature reduction: AUC 0.77. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that our models achieve higher classification accuracy.

Keywords

Subarachnoid hemorrhage Random kernels Time series Machine learning Critical care 

Notes

Acknowledgements

Data Collection (SP, HF, JC, SA, DJR, ESC, JMS, AV, KT), Analysis (SP, HF, EG, MM, CW, NE), Writing (SP, HF, MM), Editing (All).

Funding

National Institutes of Health K01-ES026833-02 (SP), National Institutes of Health U54-CA193313-01 (CW). National Science Foundation 1305023 (CW). National Science Foundation 1344668 (CW, NE).

Compliance with ethical standards

Conflict of interest

Authors do not have any other disclosures of potential conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study has been approved by the Columbia University Medical Center Institutional Review Board.

Informed consent

Informed consent was obtained from all individual participants or their surrogates included in the study.

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Soojin Park
    • 1
    Email author
  • Murad Megjhani
    • 1
  • Hans-Peter Frey
    • 1
  • Edouard Grave
    • 2
  • Chris Wiggins
    • 3
  • Kalijah L. Terilli
    • 1
  • David J. Roh
    • 1
  • Angela Velazquez
    • 1
  • Sachin Agarwal
    • 1
  • E. Sander ConnollyJr.
    • 4
  • J. Michael Schmidt
    • 1
  • Jan Claassen
    • 1
  • Noemie Elhadad
    • 2
  1. 1.Department of NeurologyColumbia UniversityNew YorkUSA
  2. 2.Department of Biomedical InformaticsColumbia UniversityNew YorkUSA
  3. 3.Department of Applied Physics and Applied MathematicsColumbia UniversityNew YorkUSA
  4. 4.Department of NeurosurgeryColumbia UniversityNew YorkUSA

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