  Observational data in medicine arise as a result of the complex interaction
between patients and the healthcare system. The sampling process is often
highly irregular and itself constitutes an informative process. When using such
data to develop prediction models, this phenomenon is often ignored, leading to
sub-optimal performance and generalisability of models when practices evolve.
We propose a multi-task recurrent neural network which models three clinical
presence dimensions -- namely the longitudinal, the inter-observation and the
missingness processes -- in parallel to the survival outcome. On a prediction
task using MIMIC III laboratory tests, explicit modelling of these three
processes showed improved performance in comparison to state-of-the-art
predictive models (C-index at 1 day horizon: 0.878). More importantly, the
proposed approach was more robust to change in the clinical presence setting,
demonstrated by performance comparison between patients admitted on weekdays
and weekends. This analysis demonstrates the importance of studying and
leveraging clinical presence to improve performance and create more
transportable clinical models.
