  Pre-training has shown success in different areas of machine learning, such
as Computer Vision, Natural Language Processing (NLP), and medical imaging.
However, it has not been fully explored for clinical data analysis. An immense
amount of clinical records are recorded, but still, data and labels can be
scarce for data collected in small hospitals or dealing with rare diseases. In
such scenarios, pre-training on a larger set of unlabelled clinical data could
improve performance. In this paper, we propose novel unsupervised pre-training
techniques designed for heterogeneous, multi-modal clinical data for patient
outcome prediction inspired by masked language modeling (MLM), by leveraging
graph deep learning over population graphs. To this end, we further propose a
graph-transformer-based network, designed to handle heterogeneous clinical
data. By combining masking-based pre-training with a transformer-based network,
we translate the success of masking-based pre-training in other domains to
heterogeneous clinical data. We show the benefit of our pre-training method in
a self-supervised and a transfer learning setting, utilizing three medical
datasets TADPOLE, MIMIC-III, and a Sepsis Prediction Dataset. We find that our
proposed pre-training methods help in modeling the data at a patient and
population level and improve performance in different fine-tuning tasks on all
datasets.
