  Clinical data from electronic medical records, registries or trials provide a
large source of information to apply machine learning methods in order to
foster precision medicine, e.g. by finding new disease phenotypes or performing
individual disease prediction. However, to take full advantage of deep learning
methods on clinical data, architectures are necessary that 1) are robust with
respect to missing and wrong values, and 2) can deal with highly variable-sized
lists and long-term dependencies of individual diagnosis, procedures,
measurements and medication prescriptions. In this work, we elaborate
limitations of fully-connected neural networks and classical machine learning
methods in this context and propose AdaptiveNet, a novel recurrent neural
network architecture, which can deal with multiple lists of different events,
alleviating the aforementioned limitations. We employ the architecture to the
problem of disease progression prediction in rheumatoid arthritis using the
Swiss Clinical Quality Management registry, which contains over 10.000 patients
and more than 65.000 patient visits. Our proposed approach leads to more
compact representations and outperforms the classical baselines.
