Medical decision support using machine learning for early detection of late-onset neonatal sepsis

Abstract

Objective: The objective was to develop non-invasive predictive models for late-onset neonatal sepsis from off-the-shelf medical data and electronic medical records (EMR).

Design: The data used in this study are from 299 infants admitted to the neonatal intensive care unit in the Monroe Carell Jr. Children's Hospital at Vanderbilt and evaluated for late-onset sepsis. Gold standard diagnostic labels (sepsis negative, culture positive sepsis, culture negative/clinical sepsis) were assigned based on all the laboratory, clinical and microbiology data available in EMR. Only data that were available up to 12 h after phlebotomy for blood culture testing were used to build predictive models using machine learning (ML) algorithms.

Measurement: We compared sensitivity, specificity, positive predictive value and negative predictive value of sepsis treatment of physicians with the predictions of models generated by ML algorithms.

Results: The treatment sensitivity of all the nine ML algorithms and specificity of eight out of the nine ML algorithms tested exceeded that of the physician when culture-negative sepsis was included. When culture-negative sepsis was excluded both sensitivity and specificity exceeded that of the physician for all the ML algorithms. The top three predictive variables were the hematocrit or packed cell volume, chorioamnionitis and respiratory rate.

Conclusions: Predictive models developed from off-the-shelf and EMR data using ML algorithms exceeded the treatment sensitivity and treatment specificity of clinicians. A prospective study is warranted to assess the clinical utility of the ML algorithms in improving the accuracy of antibiotic use in the management of neonatal sepsis.

Keywords: Decision Support; Early Detection; Electronic Medical Records; Machine Learning; Neonatal Sepsis; Predictive Models.

Figure 1

Study dataset generation (MRN, medical record number). All the datasets provided structured data.

Figure 2

Histogram showing the number of variables for different missing value percentage intervals.

Figure 3

Sepsis diagnostic algorithm (modified from Gladstone et al).

Figure 4

Schema for predictive model building, evaluation and clinical validation. NICU, neonatal intensive care unit.

Figure 5

Nested cross-validation procedure for performance estimation in the outer loop and parameter optimization in the inner loop.

Figure 6

Receiver operator characteristic (ROC) curve for naive Bayes with area under the ROC curve 0.78 (n=185). The curve in the middle is the actual ROC curve; the upper and lower curves show the upper error bound and lower error bound for the ROC curve, respectively.