Machine learning for predicting acute hypotension: A systematic review

Figures

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  Figure 1. Flow chart of the methodology used to screen the articles. Thirty-five articles published between 2008 and 2021 were included in the review.

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  Figure 2. Number of publications by year.

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  Figure 3. Prediction accuracy based on the type of algorithm. LSH is the most accurate algorithm, although only a few studies used it. SVM is the algorithm that was most often studied, and RF is the least accurate algorithm. RF, random forest; GP, genetic programming; DT, decision tree; KNN, K-nearest neighbors; SVM, support vector machine; LR, logistic regression; ANN, artificial neural network; LSH, locality sensitive hashing.

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  Figure 4. (Left) The methodology used to extract the features was very diverse; no single methodology accounts for more than half of the studies. Statistics (e.g., maximum, minimum, mean values of the raw data) are the extracted features most often studied, followed by clinical equations (apply raw data to the equation to calculate some of the derived information, e.g., cardiac output, MAP to HR ratio). (Right) Graph showing how many features were extracted to predict AHE. The number varies greatly among the studies, with a single feature extraction being the most common.

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  Figure 5. Evaluation metrics. The way in which the performance of machine learning algorithms is evaluated varies from study to study. Accuracy is the most common metric; it was adopted by almost 80% of the studies. AC, accuracy; SE, sensitivity; SP, specificity; PPV, positive predictive value; AUC, area under the ROC curve; F1, F1-score; NPV, negative predictive value; MCC, Matthews correlation coefficient.