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Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data

Author

Listed:
  • Akram Farhadi

    (Department of Computer Science, University of Georgia, Athens, GA 30602, USA)

  • Joshua J. Chern

    (Children’s Healthcare of Atlanta, Atlanta, GA 30329, USA
    These authors contributed equally to this work.)

  • Daniel Hirsh

    (Children’s Healthcare of Atlanta, Atlanta, GA 30329, USA
    These authors contributed equally to this work.)

  • Tod Davis

    (Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
    These authors contributed equally to this work.)

  • Mingyoung Jo

    (Children’s Healthcare of Atlanta, Atlanta, GA 30329, USA
    These authors contributed equally to this work.)

  • Frederick Maier

    (Institute for Artificial Intelligence, University of Georgia, Athens, GA 30602, USA
    These authors contributed equally to this work.)

  • Khaled Rasheed

    (Department of Computer Science, University of Georgia, Athens, GA 30602, USA
    Institute for Artificial Intelligence, University of Georgia, Athens, GA 30602, USA
    These authors contributed equally to this work.)

Abstract

Increased Intracranial Pressure (ICP) is a serious and often life-threatening condition. If the increased pressure pushes on critical brain structures and blood vessels, it can lead to serious permanent problems or even death. In this study, we propose a novel regression model to forecast ICP episodes in children, 30 min in advance, by using the dynamic characteristics of continuous intracranial pressure, vitals and medications during the last two hours. The correlation between physiological parameters, including blood pressure, respiratory rate, heart rate and the ICP, is analyzed. Linear regression, Lasso regression, support vector machine and random forest algorithms are used to forecast the next 30 min of the recorded ICP. Finally, dynamic features are created based on vitals, medications and the ICP. The weak correlation between blood pressure and the ICP (0.2) is reported. The Root-Mean-Square Error (RMSE) of the random forest model decreased from 1.6 to 0.89% by using the given medication variables in the last two hours. The random forest regression gave an accurate model for the ICP forecast with 0.99 correlation between the forecast and experimental values.

Suggested Citation

  • Akram Farhadi & Joshua J. Chern & Daniel Hirsh & Tod Davis & Mingyoung Jo & Frederick Maier & Khaled Rasheed, 2018. "Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data," Forecasting, MDPI, vol. 1(1), pages 1-12, August.
    • Handle: RePEc:gam:jforec:v:1:y:2018:i:1:p:4-58:d:162229
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    References listed on IDEAS

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    1. Tsair-Fwu Lee & Pei-Ju Chao & Hui-Min Ting & Liyun Chang & Yu-Jie Huang & Jia-Ming Wu & Hung-Yu Wang & Mong-Fong Horng & Chun-Ming Chang & Jen-Hong Lan & Ya-Yu Huang & Fu-Min Fang & Stephen Wan Leung, 2014. "Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    2. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
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    1. Vidhya V. & Anjan Gudigar & U. Raghavendra & Ajay Hegde & Girish R. Menon & Filippo Molinari & Edward J. Ciaccio & U. Rajendra Acharya, 2021. "Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives," IJERPH, MDPI, vol. 18(12), pages 1-29, June.

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