Imant Daunhawer1, Severin Kasser2, Gilbert Koch3, Lea Sieber2, Hatice Cakal2, Janina Tütsch2, Marc Pfister3, Sven Wellmann4,5, Julia E Vogt1,6. 1. Adaptive Systems and Medical Data Science, Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland. 2. Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland. 3. Division of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital (UKBB), Basel, Switzerland. 4. Division of Neonatology, University of Basel Children's Hospital (UKBB), Basel, Switzerland. sven.wellmann@ukbb.ch. 5. Division of Neonatology, University Children's Hospital Regensburg (KUNO), University of Regensburg, Regensburg, Germany. sven.wellmann@ukbb.ch. 6. SIB Swiss Institute of Bioinformatics, Basel, Switzerland.
Abstract
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.
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.
Authors: D Müller; D Haschtmann; T F Fekete; F Kleinstück; R Reitmeir; M Loibl; D O'Riordan; F Porchet; D Jeszenszky; A F Mannion Journal: Eur Spine J Date: 2022-07-14 Impact factor: 2.721
Authors: Davide De Francesco; Yair J Blumenfeld; Ivana Marić; Jonathan A Mayo; Alan L Chang; Ramin Fallahzadeh; Thanaphong Phongpreecha; Alex J Butwick; Maria Xenochristou; Ciaran S Phibbs; Neda H Bidoki; Martin Becker; Anthony Culos; Camilo Espinosa; Qun Liu; Karl G Sylvester; Brice Gaudilliere; Martin S Angst; David K Stevenson; Gary M Shaw; Nima Aghaeepour Journal: iScience Date: 2022-03-22