Predicting Future Occurrence of Acute Hypotensive Episodes Using Noninvasive and Invasive Features

Abstract

Introduction Early prediction of the acute hypotensive episode (AHE) in critically ill patients has the potential to improve outcomes. In this study, we apply different machine learning algorithms to the MIMIC III Physionet dataset, containing more than 60,000 real-world intensive care unit records, to test commonly used machine learning technologies and compare their performances. Materials and Methods Five classification methods including K-nearest neighbor, logistic regression, support vector machine, random forest, and a deep learning method called long short-term memory are applied to predict an AHE 30 minutes in advance. An analysis comparing model performance when including versus excluding invasive features was conducted. To further study the pattern of the underlying mean arterial pressure (MAP), we apply a regression method to predict the continuous MAP values using linear regression over the next 60 minutes. Results Support vector machine yields the best performance in terms of recall (84%). Including the invasive features in the classification improves the performance significantly with both recall and precision increasing by more than 20 percentage points. We were able to predict the MAP with a root mean square error (a frequently used measure of the differences between the predicted values and the observed values) of 10 mmHg 60 minutes in the future. After converting continuous MAP predictions into AHE binary predictions, we achieve a 91% recall and 68% precision. In addition to predicting AHE, the MAP predictions provide clinically useful information regarding the timing and severity of the AHE occurrence. Conclusion We were able to predict AHE with precision and recall above 80% 30 minutes in advance with the large real-world dataset. The prediction of regression model can provide a more fine-grained, interpretable signal to practitioners. Model performance is improved by the inclusion of invasive features in predicting AHE, when compared to predicting the AHE based on only the available, restricted set of noninvasive technologies. This demonstrates the importance of exploring more noninvasive technologies for AHE prediction.

Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis

“…Machine learning models are able to capture high-capacity relationships and they are amenable to more operational tasks rather than direct research questions; thus, more research gaps could be solved through the one-stop analysis [ 38 ]. Various medical data analyses used a machine learning approach to make decisions [ 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 ]. Biostatisticians are in a need of an updated methodology that uses a machine learning approach to conduct analysis on a variety of medical data [ 89 ].…”