AI-Informed Development for a Lactate Measurement Tool

Lactate 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.