Neonatal Jaundice Requiring Phototherapy Risk Factors in a Newborn Nursery: Machine Learning Approach.

Area of Science:

• Neonatal Medicine
• Data Science
• Predictive Analytics

Background:

• Neonatal jaundice is a prevalent condition that can lead to severe hyperbilirubinemia if not managed promptly.
• Early identification of neonates at high risk for jaundice remains a clinical challenge despite current guidelines.
• Existing diagnostic methods may not capture the full spectrum of risk factors for severe neonatal jaundice.

Purpose of the Study:

• To pinpoint critical maternal and neonatal risk factors associated with neonatal jaundice requiring phototherapy.
• To develop and validate a machine learning model for predicting the need for phototherapy in newborns.
• To enhance early clinical decision-making for managing neonatal hyperbilirubinemia.

Main Methods:

• Retrospective analysis of electronic medical records for 8242 neonates from 2017-2022.
• Application of machine learning algorithms, including XGBoost, to predict phototherapy requirements.
• Utilized SHAP values for interpreting the predictive model and identifying key risk factors.

Main Results:

• Mode of delivery, neonatal feeding indicators (formula intake, breastfeeding frequency), maternal BMI, and maternal white blood cell count were identified as significant predictors.
• Cesarean delivery and lower birth weight were associated with an increased likelihood of requiring phototherapy.
• The XGBoost model achieved a high predictive performance with an AUROC of 0.911.

Conclusions:

• Machine learning models trained on perinatal data can accurately predict the risk of neonatal jaundice requiring phototherapy.
• These predictive models offer a valuable tool for early clinical intervention, potentially improving infant health outcomes.
• Integrating machine learning into clinical practice can support timely decisions regarding phototherapy for neonatal jaundice.