Early Prediction of Neonatal Sepsis From Synthetic Clinical Data Using Machine Learning.

Abstract

Neonatal sepsis is one of the most serious complications in neonatal intensive care units. Due to the often immature immune system, sepsis-related comorbidities are the major contributors to increased neonatal mortality. The rapid progression of the disease makes early treatment critical for patient survival. However, early diagnosis of sepsis remains difficult due to its non-specific symptoms. In recent years, Machine Learning-based techniques have been used in various medical applications to predict diseases using clinical data. In this work, we optimized and evaluated four prediction models with different architectural concepts. Two public datasets containing clinical data from adults and neonates were used for training. The adult data were collected to pre-train the models. Since neonatal data with sepsis diagnosis are very limited, we propose an augmentation method to generate synthetic clinical data. For the final evaluation, the real data of neonatal patients were defined as a test set. An AUROC of 0.91 and an AUPRC of 0.38 were obtained. These results are promising for early prediction of neonatal sepsis using artificial data for augmentation.Clinical relevance- This work demonstrates the potential of Machine Learning-based prediction models for the detection of sepsis to improve the early diagnosis of life-threatening conditions in neonatal intensive care units.