  In large population-based studies and in clinical routine, tasks like disease
diagnosis and progression prediction are inherently based on a rich set of
multi-modal data, including imaging and other sensor data, clinical scores,
phenotypes, labels and demographics. However, missing features, rater bias and
inaccurate measurements are typical ailments of real-life medical datasets.
Recently, it has been shown that deep learning with graph convolution neural
networks (GCN) can outperform traditional machine learning in disease
classification, but missing features remain an open problem. In this work, we
follow up on the idea of modeling multi-modal disease classification as a
matrix completion problem, with simultaneous classification and non-linear
imputation of features. Compared to methods before, we arrange subjects in a
graph-structure and solve classification through geometric matrix completion,
which simulates a heat diffusion process that is learned and solved with a
recurrent neural network. We demonstrate the potential of this method on the
ADNI-based TADPOLE dataset and on the task of predicting the transition from
MCI to Alzheimer's disease. With an AUC of 0.950 and classification accuracy of
87%, our approach outperforms standard linear and non-linear classifiers, as
well as several state-of-the-art results in related literature, including a
recently proposed GCN-based approach.
