<p> In this original study, we investigate the performances of machine learning algorithms on a neonatal sepsis detection task. We consider this work to be of great interest to both engineers and clinicians, as it uses non-invasive, already existing, vital signs monitoring signals in a population of very low birth weight infants at high risk of sepsis. Vital sign variability may indeed represent a general indicator of health and wellbeing and be helpful in the early detection of systematic inflammation such as sepsis. We used state of the art feature extraction technics and evaluate a large variety of binary classification models among which a neural network based generative model. The models were chosen from two main families: discriminative and generative. This enables a comprehensive study of different kinds of traditional and advanced binary classification algorithms.</p> <p> Our study reveals that advanced machine learning models are more robust to changes in the feature extraction pipeline, although linear classifiers have a comparable performance when the feature extraction is tuned. The advanced model performing the best is a neural network based generative model which is a hybrid generative and discriminative model. A large window length when computing the features is beneficial to almost all algorithms, indicating the relevance of frequency domain related features for the neonatal sepsis detection task.</p> <p> Overall we obtain a classification AUROC above 0.85, which makes our prediction models potentially relevant in clinical practice. This will enable earlier therapeutic interventions and thereby reduce morbidity and mortality in infants.</p>

<p> In this original study, we investigate the performances of machine learning algorithms on a neonatal sepsis detection task. We consider this work to be of great interest to both engineers and clinicians, as it uses non-invasive, already existing, vital signs monitoring signals in a population of very low birth weight infants at high risk of sepsis. Vital sign variability may indeed represent a general indicator of health and wellbeing and be helpful in the early detection of systematic inflammation such as sepsis. We used state of the art feature extraction technics and evaluate a large variety of binary classification models among which a neural network based generative model. The models were chosen from two main families: discriminative and generative. This enables a comprehensive study of different kinds of traditional and advanced binary classification algorithms.</p> <p> Our study reveals that advanced machine learning models are more robust to changes in the feature extraction pipeline, although linear classifiers have a comparable performance when the feature extraction is tuned. The advanced model performing the best is a neural network based generative model which is a hybrid generative and discriminative model. A large window length when computing the features is beneficial to almost all algorithms, indicating the relevance of frequency domain related features for the neonatal sepsis detection task.</p> <p> Overall we obtain a classification AUROC above 0.85, which makes our prediction models potentially relevant in clinical practice. This will enable earlier therapeutic interventions and thereby reduce morbidity and mortality in infants.</p>

Aim:To investigate the relation between autonomic regulation, measured using heart rate variability (HRV), body weight and degree of prematurity in infants. Further to assess utility to include body weight in a machine learning-based sepsis prediction algorithm.Methods: Longitudinal cohort study including 378 infants hospitalised in two neonatal intensive care units. Continuous vital sign data collection was performed prospectively from the time of NICU admission to discharge. Clinically relevant events were annotated retrospectively. HRV described using sample entropy of inter-beat intervals and assessed for its correlation with body weight measurements and age.Weight values were then added to a machine learning-based algorithm for neonatal sepsis detection.Results: Sample entropy showed a positive correlation with increasing body weight and postconceptual age. Very low birth weight infants exhibited significantly lower HRV compared to infants with a birth weight >1500 g. This persisted when reaching similar weight and at the same postconceptual age. Adding body weight measures improved the algorithm's ability to predict sepsis in the overall population. Conclusion:We revealed a positive correlation of HRV with increasing body weight and maturation in infants. Restricted HRV, proven helpful in detecting acute events such as neonatal sepsis, might reflect prolonged impaired development of autonomic control.