Machine learning for predicting acute hypotension: A systematic review - Zhao, Anxing; Elgendi, Mohamed; Menon, Carlo (Frontiers in Cardiovascular Medicine, 2022)
An acute hypotensive episode (AHE) can lead to severe consequences and complications that threaten patients' lives within a short period of time. How to accurately and non-invasively predict AHE in advance has become a hot clinical topic that has attracted a lot of attention in the medical and engineering communities. In the last 20 years, with rapid advancements in machine learning methodology, this topic has been viewed from a different perspective. This review paper examines studies published from 2008 to 2021 that evaluated the performance of various machine learning algorithms developed to predict AHE. A total of 437 articles were found in four databases that were searched, and 35 full-text articles were included in this review. Fourteen machine learning algorithms were assessed in these 35 articles; the Support Vector Machine algorithm was studied in 12 articles, followed by Logistic Regression (6 articles) and Artificial Neural Network (6 articles). The accuracy of the algorithms ranged from 70% to 96%. The size of the study sample varied from small (12 subjects) to very large (3825 subjects). Recommendations for future work are also discussed in this review.