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

Abstract

Background Neonatal 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. Objective The aim of this study was to examine the feasibility of a prediction model by using noninvasive vital sign data and machine learning technology. Methods We 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. Results The 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. 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.

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

“…As mentioned above, sepsis prediction on the ICU is an important and timely problem and an active area of research. Under these circumstances, it is not surprising that some approaches have been developed in parallel to this work [33][34][35][36][37][38][39][40][41][42][43]. It will be an exciting and important avenue for future work to benchmark all these approaches (including ours) against each other and to compare their performances on a unified and realistic set of sepsis labels, for instance, the ones we propose in this work.…”

Predictive Scores for Late-Onset Neonatal Sepsis as an Early Diagnostic and Antimicrobial Stewardship Tool: What Have We Done So Far?

“…The main purpose of this approach is to introduce a more personalized basis for the diagnosis of neonatal sepsis, relying on precise and continuous information. A significant number of such studies reflect the interest on this new wrinkle, with impressive results in their diagnostic power [ 43 , 44 , 45 , 46 , 47 , 48 ]. Undoubtedly, this idea provides a pioneering perspective on the field, not only for the time being where NICUs count on continuous monitoring, but also for the near future when computing methodologies will play a crucial role in medical decision-making generally.…”

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

“…This is consistent with [44] where the same SINAN-TB data set was used and features selected by a specialist. We used the entire data set and applied k-fold cross validation, with k = 10 as per [71][72][73][74].…”