Ahmed, W;
Veluthandath, AV;
Madsen, J;
Clark, HW;
Dushianthan, A;
Postle, AD;
Wilkinson, JS;
(2024)
Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.
Talanta
, 275
, Article 126062. 10.1016/j.talanta.2024.126062.
Preview |
Text
ComplexMixturesPaper_ElSevier_Reviewer_Reply_CLEAN.pdf - Accepted Version Download (2MB) | Preview |
Abstract
Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomarkers, lecithin (L) and sphingomyelin (S) with the potential for point of care (POC) diagnosis and monitoring. The effects of varying other lipid species present in lung surfactant on the mid IR spectra used to train machine learning models are explored. This study presents a lung lipid model of five lipids present in lung surfactant and varies each in a systematic approach to evaluate the ability of machine learning models to predict the lipid concentrations, the L/S ratio and to quantify the uncertainty in the predictions using the jackknife + -after-bootstrap and variant bootstrap methods. We establish the L/S ratio can be determined with an uncertainty of approximately ±0.3 mol/mol and we further identify the 5 most prominent wavenumbers associated with each machine learning model.
| Type: | Article |
|---|---|
| Title: | Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.talanta.2024.126062 |
| Publisher version: | http://dx.doi.org/10.1016/j.talanta.2024.126062 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Physical Sciences, Chemistry, Analytical, Chemistry, ATR-FTIR, Machine, Learning, PLSR, Lipid, SHAP values, nRDS, FETAL LUNG MATURITY, PULMONARY SURFACTANT, AMNIOTIC-FLUID, PHOSPHATIDYLGLYCEROL, PHOSPHOLIPIDS, RATIO |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Neonatology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10195388 |
Archive Staff Only
 |
View Item |
