Identifying Critical Phases for Disease Onset with Sparse Haematological Biomarkers
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Routinely collected clinical blood tests are an emerging molecular data
source for large-scale biomedical research but inherently feature irregular
sampling and informative observation. Traditional approaches rely on
imputation, which can distort learning signals and bias predictions while
lacking biological interpretability. We propose a novel methodology using Graph
Neural Additive Networks (GNAN) to model biomarker trajectories as
time-weighted directed graphs, where nodes represent sampling events and edges
encode the time delta between events. GNAN's additive structure enables the
explicit decomposition of feature and temporal contributions, allowing the
detection of critical disease-associated time points. Unlike conventional
imputation-based approaches, our method preserves the temporal structure of
sparse data without introducing artificial biases and provides inherently
interpretable predictions by decomposing contributions from each biomarker and
time interval. This makes our model clinically applicable, as well as allowing
it to discover biologically meaningful disease signatures.
