  In many medical applications, interpretable models with high prediction
performance are sought. Often, those models are required to handle
semi-structured data like tabular and image data. We show how to apply deep
transformation models (DTMs) for distributional regression which fulfill these
requirements. DTMs allow the data analyst to specify (deep) neural networks for
different input modalities making them applicable to various research
questions. Like statistical models, DTMs can provide interpretable effect
estimates while achieving the state-of-the-art prediction performance of deep
neural networks. In addition, the construction of ensembles of DTMs that retain
model structure and interpretability allows quantifying epistemic and aleatoric
uncertainty. In this study, we compare several DTMs, including
baseline-adjusted models, trained on a semi-structured data set of 407 stroke
patients with the aim to predict ordinal functional outcome three months after
stroke. We follow statistical principles of model-building to achieve an
adequate trade-off between interpretability and flexibility while assessing the
relative importance of the involved data modalities. We evaluate the models for
an ordinal and dichotomized version of the outcome as used in clinical
practice. We show that both, tabular clinical and brain imaging data, are
useful for functional outcome prediction, while models based on tabular data
only outperform those based on imaging data only. There is no substantial
evidence for improved prediction when combining both data modalities. Overall,
we highlight that DTMs provide a powerful, interpretable approach to analyzing
semi-structured data and that they have the potential to support clinical
decision making.
