Personalized non-invasive continuous glucose monitoring via multiparameter-informed machine learning

Non-invasive continuous glucose monitoring (NCGM) based on reverse iontophoresis (RI) holds considerable promise for diabetes management; however, its clinical translation is constrained by limited predictive accuracy. This limitation primarily arises from the inter-individual differences and the dynamic variability during continuous monitoring. Here we present a multiparameter-informed machine learning framework using physiological factors of glucose, Na⁺, skin surface pH, temperature, and impedance to composite the effect and enable accurate and personalized NCGM. An accompanying flexible multimodal sensing platform is designed to simultaneously quantify glucose, Na⁺, and skin surface pH, together with skin temperature and impedance. A physics-based model first generates a physiologically grounded glucose estimate calibrated by Na⁺ and pH, which is subsequently refined using a CNN-LSTM-Attention architecture. Transfer learning is implemented to enable cross-individual generalization and personalized calibration. In-vivo studies in six healthy volunteers over 14 days demonstrate that the proposed multiparameter biosensing-informed machine learning approach achieves closer agreement with reference glucose profiles, reducing the overall mean absolute relative difference (MARD) from 19.01% to 14.47% and further to 10.51%. Clarke error grid analysis indicates enhanced clinical accuracy, with the highest proportion of data points located within zones A and B (99.45%). Shapley Additive Explanation (SHAP) analysis further reveals pronounced inter-individual variability in feature contributions, highlighting the model's capacity for adaptive, personalized weighting of physiological inputs. These results establish a multiparameter-informed strategy that improves the accuracy of RI-based glucose monitoring, providing a viable pathway toward precise and personalized NCGM.