Machine learning for predicting acute hypotension: A systematic review

Abstract

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 (six articles) and Artificial Neural Network (six 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 (3,825 subjects). Recommendations for future work are also discussed in this review.