AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine
Jie Lian Xufang Luo Caihua Shan Dongqi Han Varut Vardhanabhuti Dongsheng Li
Precision medicine tailored to individual patients has gained significant
attention in recent times. Machine learning techniques are now employed to
process personalized data from various sources, including images, genetics, and
assessments. These techniques have demonstrated good outcomes in many clinical
prediction tasks. Notably, the approach of constructing graphs by linking
similar patients and then applying graph neural networks (GNNs) stands out,
because related information from analogous patients are aggregated and
considered for prediction. However, selecting the appropriate edge feature to
define patient similarity and construct the graph is challenging, given that
each patient is depicted by high-dimensional features from diverse sources.
Previous studies rely on human expertise to select the edge feature, which is
neither scalable nor efficient in pinpointing crucial edge features for complex
diseases. In this paper, we propose a novel algorithm named \ours, which can
automatically select important features to construct multiple patient
similarity graphs, and train GNNs based on these graphs as weak learners in
adaptive boosting. \ours{} is evaluated on two real-world medical scenarios and
shows superiors performance.