Neural networks for the prediction of bacterial and fungal infections: current evidence and implications.

Abstract

In the present narrative review, we discuss the use of artificial neural networks (ANNs) for predicting bacterial and fungal infections based on commonly available clinical and laboratory data, focusing on promises and challenges of these machine learning models. For predicting different bacterial or fungal infections from data commonly found in electronical medical records, ANN models may reach, based on current literature, an acceptable performance for discriminating between infected and non-infected patients, and outperformed other machine learning (ML)-based models in 38.3% of the retrieved studies evaluating at least another ML approach. In the near future, as for other ML models, the use of ANNs could be leveraged to provide real-time support to clinicians in clinical decision-making processes, although further research is needed in terms of quality of data and explainability of ANN model predictions to better understand whether and how these techniques can be safely adopted in everyday clinical practice.