Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning

• Published in: Pediatric research 2019-07, Vol.86 (1), p.122-127
• Main Authors: Daunhawer, Imant, Kasser, Severin, Koch, Gilbert, Sieber, Lea, Cakal, Hatice, Tütsch, Janina, Pfister, Marc, Wellmann, Sven, Vogt, Julia E.
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Clinical Research Article; Jaundice; Light therapy; Machine learning; Medicine; Medicine & Public Health; Pediatric Surgery; Pediatrics
• ISSN: 0031-3998; 1530-0447
• DOI: 10.1038/s41390-019-0384-x

Background Machine learning models may enhance the early detection of clinically relevant hyperbilirubinemia based on patient information available in every hospital. Methods We conducted a longitudinal study on preterm and term born neonates with serial measurements of total serum bilirubin in the first two weeks of life. An ensemble, that combines a logistic regression with a random forest classifier, was trained to discriminate between the two classes phototherapy treatment vs. no treatment. Results Of 362 neonates included in this study, 98 had a phototherapy treatment, which our model was able to predict up to 48 h in advance with an area under the ROC-curve of 95.20%. From a set of 44 variables, including potential laboratory and clinical confounders, a subset of just four (bilirubin, weight, gestational age, hours since birth) suffices for a strong predictive performance. The resulting early phototherapy prediction tool (EPPT) is provided as an open web application. Conclusion Early detection of clinically relevant hyperbilirubinemia can be enhanced by the application of machine learning. Existing guidelines can be further improved to optimize timing of bilirubin measurements to avoid toxic hyperbilirubinemia in high-risk patients while minimizing unneeded measurements in neonates who are at low risk.