Investigating a domain adaptation approach for integrating different
  measurement instruments in a longitudinal clinical registry
Maren Hackenberg Michelle Pfaffenlehner Max Behrens Astrid Pechmann Janbernd Kirschner Harald Binder
In a longitudinal clinical registry, different measurement instruments might
have been used for assessing individuals at different time points. To combine
them, we investigate deep learning techniques for obtaining a joint latent
representation, to which the items of different measurement instruments are
mapped. This corresponds to domain adaptation, an established concept in
computer science for image data. Using the proposed approach as an example, we
evaluate the potential of domain adaptation in a longitudinal cohort setting
with a rather small number of time points, motivated by an application with
different motor function measurement instruments in a registry of spinal
muscular atrophy (SMA) patients. There, we model trajectories in the latent
representation by ordinary differential equations (ODEs), where person-specific
ODE parameters are inferred from baseline characteristics. The goodness of fit
and complexity of the ODE solutions then allows to judge the measurement
instrument mappings. We subsequently explore how alignment can be improved by
incorporating corresponding penalty terms into model fitting. To systematically
investigate the effect of differences between measurement instruments, we
consider several scenarios based on modified SMA data, including scenarios
where a mapping should be feasible in principle and scenarios where no perfect
mapping is available. While misalignment increases in more complex scenarios,
some structure is still recovered, even if the availability of measurement
instruments depends on patient state. A reasonable mapping is feasible also in
the more complex real SMA dataset. These results indicate that domain
adaptation might be more generally useful in statistical modeling for
longitudinal registry data.