Next Article in Journal
Improved Brain Tumor Segmentation via Registration-Based Brain Extraction
Previous Article in Journal
A Multi-Stage Price Forecasting Model for Day-Ahead Electricity Markets
Article Menu

Export Article

Open AccessArticle
Forecasting 2018, 1(1), 47-58; https://doi.org/10.3390/forecast1010004

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

1
Department of Computer Science, University of Georgia, Athens, GA 30602, USA
2
Children’s Healthcare of Atlanta, Atlanta, GA 30329, USA
3
Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
4
Institute for Artificial Intelligence, University of Georgia, Athens, GA 30602, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 17 June 2018 / Revised: 17 July 2018 / Accepted: 31 July 2018 / Published: 6 August 2018
Full-Text   |   PDF [439 KB, uploaded 6 August 2018]   |  

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. View Full-Text
Keywords: time series; sliding window; forecasting; regression; intracranial pressure time series; sliding window; forecasting; regression; intracranial pressure
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Farhadi, A.; Chern, J.J.; Hirsh, D.; Davis, T.; Jo, M.; Maier, F.; Rasheed, K. Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data. Forecasting 2018, 1, 47-58.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Forecasting EISSN 2571-9394 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top