Akram Farhadi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Akram Farhadi has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Daniel Hirsh
Children’s Healthcare of Atlanta, Atlanta, GA 30329, USA
1 shared publication - Frederick W. Maier
Institute for Artificial Intelligence, University of Georgia, Athens, GA 30602, USA
1 shared publication - Joshua J. Chern
Pediatric Neurosurgery Associates, Children's Healthcare of Atlanta
1 shared publication - Khaled Rasheed
The University of Georgia
1 shared publication - Mingyoung Jo
Children’s Healthcare of Atlanta, Atlanta, GA 30329, USA
1 shared publication - Tod Davis
Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
1 shared publication
Latest Publications
- Open Access
- 6 August 2018
- Published by MDPI AG in Forecasting
- Vol. 1 (1) , 47-58
Intracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data
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.
- 6 August 2018
- Published by MDPI AG in Forecasting
- Vol. 1 (1) , 47-58
Open AccessIntracranial Pressure Forecasting in Children Using Dynamic Averaging of Time Series Data
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.