AI-informed development for a Lactate measurement tool

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dc.contributor.authorKiely, Cianen
dc.contributor.authorRossberg, Nicolaen
dc.contributor.authorKrishnamoorthy, Shreeen
dc.contributor.authorVisentin, Andreaen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-01-22T10:29:33Z
dc.date.available2025-01-22T10:29:33Z
dc.date.issued2024en
dc.description.abstractLactate has been identified as a key biomarker, with spikes co-occurring with high-risk medical conditions including sepsis and hypoxia. Despite its high medical value, current methods of Lactate measurement require repeated blood sampling from the patient, which is both costly and invasive, and consequently tends to be limited to intensive care units. Spectroscopy, a non-invasive light-based system, presents a cost-effective alternative to these traditional methods, which permits continuous measurement and improved patient monitoring. Through the use of machine learning, spectroscopic measurements can be used to estimate blood Lactate levels in an accessible and low-cost manner. In this study, machine learning models were trained on Near-infrared (NIR) spectroscopy data, to identify the best set-up for high-precision estimation of Lactate levels. The results of the analysis are used to determine the best path length for spectroscopic measurements. Feature selection is implemented to establish the most important wavelengths for prediction and inform on the most relevant spectral regions for the given task. Explainability is implemented to analyse feature contributions and allow inference of potentially interfering components that should be considered for further testing. The results showed that by using a random forest, R2 values of 0.9986 can be achieved. Feature selection increased predictive performance considerably with R2 values as high as 0.9996 and the implementation of explainability allowed the identification of important wavelength ranges.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationKiely, C., Rossberg, N., Krishnamoorth, S. and Visentin, A. (2024) 'AI-informed development for a Lactate measurement tool', 32nd Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, December 9-10, 2024.en
dc.identifier.endpage12en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16872
dc.language.isoenen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Maternity/Adoptive Leave Allowance/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Centres for Research Training Programme::Data and ICT Skills for the Future/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/HE::HORIZON-IA/101092989/EU/DATA Monetization, Interoperability, Trading & Exchange/DATAMITEen
dc.relation.urihttps://aics2024.ucd.ie/programme.htmlen
dc.rights© 2022, the Authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectLactate measurementen
dc.subjectArtificial Intelligenceen
dc.subjectExplainabilityen
dc.subjectChemometricsen
dc.titleAI-informed development for a Lactate measurement toolen
dc.typeConference itemen
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