Fever etiology prediction in neurocritical care patients using Machine Learning

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Fever is harmful in critically ill patients with acute brain injury (ABI). It is vital to swiftly and accurately identify the source of the fever and start treatment. The aim of this study was to explore the application of AI to predict the etiology of a fever at onset. Fever episodes of included ABI patients were identified. Fever episodes with 100 hours of consecutive antibiotics were labelled as infectious, else non-infectious. Features were extracted over the three days before the fever. Eight traditional Machine Learning models were trained using different feature representation and sampling approaches. We identified 610 fever episodes in 423 of the 1056 included patients (40%) of which 120 (20%) were labelled infectious. The best performing models were Logistic Regression and SVM with rbf kernel, with an AUC of 0.64, which is 0.09 higher than the dummy classifier. The sampling techniques as well as the different approaches in feature engineering did not show a significant main effect on AUC performance. Based on our results, we conclude that the combination of features and labels in the created dataset do not carry sufficient predictive value for the distinction between infectious and non-infectious fever episodes.
Faculteit der Sociale Wetenschappen