AI Foundation Models for Personalized Health Monitoring: Learning Meaningful Representations of Metabolic Profiles
Track: Tiny Paper Track
Keywords: AI Foundation Models, Personalized Health Monitoring, Metabolomics, NMR Spectroscopy, Deep Learning, Transformer Models, Precision Medicine, Biomarker Discovery, Disease Diagnosis
Abstract: Metabolism plays a crucial role in health, with metabolic processes linked to numerous diseases. Nuclear Magnetic Resonance (NMR) spectroscopy of blood provides detailed metabolic insights, but conventional analysis methods fail to fully utilize its rich data. This work proposes an AI foundation model leveraging Transformer architectures to learn meaningful representations directly from NMR spectra. Our approach involves self-supervised pre-training on large-scale unlabeled spectral data, followed by fine-tuning on labeled datasets for disease diagnosis and metabolic health assessment. By exploring hybrid architectures and attention mechanisms, we aim to enhance biomarker discovery and clinical decision-making. This AI-driven methodology is designed to improve diagnostic precision, enable personalized health monitoring, and advance the field of computational metabolomics.
Submission Number: 23
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