One of these infants has clinical deterioration requiring intubation the next day, at which point antibiotics are started. An artificial intelligence (AI) system for early detection of neonatal sepsis has been implemented in a level IV NICU. An automated algorithm incorporates analysis of heart ...

January 22, 2026 - Neonatal sepsis is an infection involving the bloodstream in infants younger than 28 days old and remains a leading cause of morbidity and mortality among neonates, especially in middle and lower-income countries.[1][2] Neonatal sepsis is divided into 2 groups based on the time of presentation ...

August 8, 2023 - For example, a recent study using ML algorithms to analyze vital signs and laboratory values in a cohort of preterm neonates achieved a sensitivity of 88% and a specificity of 91% for predicting sepsis within 24 h of onset.

Conclusion: RALIS has a promising potential as an easy to implement noninvasive early indicator of LOS, especially for ruling out LOS in VLBW high-risk infants. ... Vital signs analysis algorithm detects inflammatory response in premature infants ...

Experimental results show that adaptive boosting, light gradient boosting and random forest with Synthetic Minority Oversampling Technique give the highest area under the receiver operating characteristic (AUROC) of 0.9248, 0.9245, and 0.9238, respectively, among all the algorithms evaluated using 10-fold stratified cross-validation. The soft voting classifier trained on an ensemble of the top three models predicted the onset of neonatal sepsis with an AUROC of 0.9266, accuracy of 0.8553, F1 score of 0.7829, and Matthew's correlation coefficient of 0.6995.

Signatures of illness are present in physiologic time series data derived from heart rate (HR) and oxygen saturation (SpO2) monitoring in the early stages of sepsis in premature infants8–10. We developed and validated algorithms to detect abnormal patterns in continuous HR9 and SpO211 data ...

Aim: To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non-invasive parameters for sepsis prediction in neonates.

August 1, 2024 - CONTEXT. Accurate identification of possible sepsis in young infants is needed to effectively manage and reduce sepsis-related morbidity and mortality.OBJECTIVE. Synthesize evidence on the diagnostic accuracy of clinical sign algorithms to identify young infants (aged 0–59 days) with suspected ...

August 8, 2023 - In order to prevent mortality and morbidity, the early detection of LOS is of the utmost importance. Recent works have found that the use of machine learning techniques might help detect LOS at an early stage.

New research shows that continuous automated detection of abnormal variability of vital signs, through mathematical algorithms incorporating measures such as entropy, can alert clinicians to impending clinical deterioration and allow earlier intervention [7■]. This review will highlight research into use of continuous physiologic monitoring to identify ICU patients in the early stages of life-threatening illnesses, with a particular focus on cardiorespiratory predictive monitoring for sepsis in preterm infants.

January 7, 2025 - This study proposes a machine learning algorithm utilizing electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) to enhance early detection and treatment of neonatal sepsis.

June 15, 2023 - The clinically relevant algorithm, based on routinely collected data, can potentially accelerate clinical decisions in the early detection of LOS, even with limited inputs. ... Neonatal sepsis is a severe infectious disease and a significant cause of neonatal morbidity and mortality worldwide [1]. Particularly, preterm infants and very-low-birth-weight infants (VLBW) are prone to neonatal sepsis [[2], [3], [4]]. Neonatal sepsis is often categorized based on the age of onset.

We have applied principles of statistical signal processing and non-linear dynamics to analyze heart rate time series from premature newborn infants in order to assist in the early diagnosis of sepsis, a common and potentially deadly bacterial ...

This research compares several machine learning algorithms that may use vital signs, laboratory measures, and observations · taken within 24 hours of arrival to predict when newborn sepsis would start.

September 20, 2022 - Artificial intelligence may have a role in the early detection of sepsis in neonates. Machine learning can identify patterns that predict high or increasing risk for clinical deterioration from a sepsis-like illness.

Improving early recognition of sepsis in the Neonatal Intensive Care Unit using machine learning models and electronic health record data.

Neonatal complications such as NEC(27) and bronchopulmonary dysplasia (BPD)(28) were identified according to parent study protocol and widely used benchmarks. Specifically, an episode of NEC was defined as clinical and radiographic evidence of NEC (pneumatosis intestinalis) treated with antibiotics and bowel rest. Based on NICHD definitions for premature infants, episodes of culture-proven sepsis occurring <72 hours of life were considered early onset and excluded from analysis.

June 1, 2026 - Twenty-five maternal and neonatal ... Mechanical ventilation) were used to for the learning procedure with the Levenberg-Marquardt optimization algorithm and sepsis estimation as the output of the model....

Two recent studies used hourly vital sign and demographic variables in a proprietary algorithm (InSight) to predict adult sepsis 4 hours prior to clinician suspicion [31]. Although these works utilize similar methods, significant physiological and immunological differences between adults and ...

The best-performing algorithm was selected to build the final model, and decision curve analysis (DCA), calibration curves, and learning curves were plotted. SHAP was used to interpret the model results. A comparison of baseline data between the neonatal EOS group and the non-EOS group in the training set revealed significant differences in RR, pulse, YS, NC, fever, AFT, ATF, FDP, PCT, WBC, CRP.