Papers › Early hospital mortality prediction using vital signals

Early hospital mortality prediction using vital signals

18 Mar 2018arXiv:1803.06589archive 2025-07-28

Reza Sadeghi, Tanvi Banerjee, William Romine

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.

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Tasks

ICU AdmissionMortality PredictionPrediction

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Mortality Prediction MIMIC-III Random Forest F1 score 0.97 #1 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Random Forest Precision 0.97 #1 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Random Forest Recall 0.97 #1 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Gaussian SVM F1 score 0.96 #2 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Gaussian SVM Precision 0.95 #2 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Gaussian SVM Recall 0.96 #2 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Decision Tree F1 score 0.91 #3 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Decision Tree Precision 0.90 #3 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Decision Tree Recall 0.92 #3 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Boosted Trees F1 score 0.87 #4 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Boosted Trees Precision 0.91 #4 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Boosted Trees Recall 0.83 #4 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III K-NN F1 score 0.82 #5 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III K-NN Precision 0.80 #5 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III K-NN Recall 0.85 #5 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Logistic regression F1 score 0.72 #6 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Logistic regression Precision 0.77 #6 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Logistic regression Recall 0.67 #6 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Linear Discriminant F1 score 0.71 #7 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Linear Discriminant Precision 0.78 #7 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Linear Discriminant Recall 0.66 #7 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Linear SVM F1 score 0.70 #8 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Linear SVM Precision 0.80 #8 of 13 Archive leaderboard report
Mortality Prediction MIMIC-III Linear SVM Recall 0.63 #8 of 13 Archive leaderboard report

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Methods

InterpretabilitySVM

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