A Predictive Model Based on Machine Learning for the Early Detection of Late-Onset Neonatal Sepsis: Development and Observational Study.

Wongeun Song,Young Hwa Jung,Hyunyoung Baek,Se Young Jung,Chang Won Choi,Sooyoung Yoo

doi:10.2196/15965

Abstract

BackgroundNeonatal sepsis is associated with most cases of mortalities and morbidities in the neonatal intensive care unit (NICU). Many studies have developed prediction models for the early diagnosis of bloodstream infections in newborns, but there are limitations to data collection and management because these models are based on high-resolution waveform data.ObjectiveThe aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology.MethodsWe used electronic medical record data in intensive care units published in the Medical Information Mart for Intensive Care III clinical database. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models.ResultsThe performance of the LONS prediction model was found to be comparable to that of the prediction models that use invasive data such as high-resolution vital sign data, blood gas estimations, blood cell counts, and pH levels. The area under the receiver operating characteristic curve of the 48-hour prediction model was 0.861 and that of the onset detection model was 0.868. The main features that could be vital candidate markers for clinical neonatal sepsis were blood pressure, oxygen saturation, and body temperature. Feature generation using kurtosis and skewness of the features showed the highest performance.ConclusionsThe findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model.

Highlights

With the developments in the care system of neonate intensive care units (NICUs), the survival rates of very low birth weight infants have greatly increased
This study showed that when the biosignals recorded in electronic medical record (EMR) are used to select and learn features based on the presented algorithm, it is possible to produce a model that can predict late-onset neonatal sepsis (LONS) 48 hours earlier
The vital sign–based prediction model, which was based on EMR, showed a model performance that exceeded the model that learned based on the laboratory test, which was presented by Mani et al [14]

Summary

Introduction

With the developments in the care system of neonate intensive care units (NICUs), the survival rates of very low birth weight infants have greatly increased. Even if a negative finding is reported for blood culture, antibiotic therapy is prolonged when the clinical symptoms of LONS are manifested because of the possibility of false-negative blood culture results [5,6]. The late-onset neonatal sepsis (LONS) prediction algorithm using our proposed forward feature selection technique was based on NICU inpatient data and was designed to detect clinical sepsis 48 hours before occurrence. The performance of this prediction model was evaluated using various feature selection algorithms and machine learning models. Conclusions: The findings of our study confirmed that the LONS prediction model based on machine learning can be developed using vital sign data that are regularly measured in clinical settings. Future studies should conduct external validation by using different types of data sets and actual clinical verification of the developed model

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Conclusion