Abstract
An adequate model for predicting bacteraemia has not yet been developed. This study aimed to evaluate the performance of an artificial neural network (ANN)-based prediction model in comparison with previous statistical models. The performance of multi-layer perceptron (MLP), a representative ANN model, was verified via comparison with a non-neural network model. A total of 1260 bacteraemia episodes were identified in 13,402 patients. In MLP with 128 hidden layer nodes, the area under the receiver operating characteristic curve (AUC) of the prediction performance was 0.729 (95% confidence interval [CI]; 0.712–0.728), while in MLP with 256 hidden layer nodes, it was 0.727 (95% CI; 0.713–0.727). In a conventional Bayesian statistical method, the AUC was 0.7. The aforementioned two MLP models exhibited the highest sensitivity (0.810). The ranking of clinical variables was used to describe the influential power of the prediction. Serum alkaline phosphatase was one of the most influential clinical variables, and one-out search was the best ranking method for measuring the influence of the clinical variables. Furthermore, adding variables beyond the 10 top-ranking ones did not significantly affect the prediction of bacteraemia. The ANN model is not inferior to conventional statistical approaches. Bacteraemia can be predicted using only the top 10 clinical variables determined by a ranking method, and the model can be used in clinical practice by applying real-time monitoring.
Highlights
Evidence-based medicine, which is the basis of modern medicine, involves making medical judgments based on scientific evidence and clinical experience [1]
We presented a model for predicting bacteraemia using a Bayesian statistical approach [18], in which bacteraemia was predicted on the basis of the statistical stratification of clinical variables
We aimed to evaluate the performance of an artificial neural network (ANN)-based prediction model in comparison with previous traditional statistical models and use artificial intelligence as a starting point for the analysis and prediction of progress in acute severe infectious diseases
Summary
Evidence-based medicine, which is the basis of modern medicine, involves making medical judgments based on scientific evidence and clinical experience [1]. Most analyses using artificial intelligence techniques focus on the diagnosis of chronic diseases, medical imaging, or health care control [4,5,6]. By applying machine learning-based algorithms using artificial intelligence techniques to massive amounts of medical data, we attempt to build a real-time monitoring system for the prediction of diseases to support accurate, efficient, and timely clinical decision-making in any situation [12,13]. Diagnosis and active treatment can improve the outcome in such cases [14] Various biomarkers such as procalcitonin, presepsin, and CD64 have been studied in early-onset models of acute and severe infectious diseases; the application of such biomarkers in clinical practice is insufficient [15,16,17]. There was a limitation linked to the presence of structural and unstructured variables in the aforementioned model
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