Publications by authors named "Ehsan Masoumi"
Article Synopsis
- Hypotension is common in ICUs, and early prediction is crucial for improving patient outcomes.
- Machine-learning techniques were evaluated to predict hypotensive events using noninvasive physiological signals, given the discomfort of invasive blood pressure measurements.
- The study demonstrates that simulating noninvasive mean arterial pressure (NIMAP) from invasive measurements can effectively aid predictive algorithms, achieving an 84% sensitivity and improved performance with more frequent blood pressure sampling.
Supervised Machine-Learning Algorithms in Real-time Prediction of Hypotensive Events.
Annu Int Conf IEEE Eng Med Biol Soc
July 2020
Hypotension is common in critically ill patients. Early prediction of hypotensive events in the Intensive Care Units (ICUs) allows clinicians to pre-emptively treat the patient and avoid possible organ damage. In this study, we investigate the performance of various supervised machine-learning classification algorithms along with a real-time labeling technique to predict acute hypotensive events in the ICU.
View Article and Find Full Text PDF