Understanding human functioning using graphical models

Background: Functioning and disability are universal human
experiences. However, our current understanding of functioning from
a comprehensive perspective is limited. The development of the
International Classification of Functioning, Disability and Health
(ICF) on the one hand and recent developments in graphical modeling
on the other hand might be combined and open the door to a more
comprehensive understanding of human functioning. The objective of
our paper therefore is to explore how graphical models can be used
in the study of ICF data for a range of applications. Methods: We
show the applicability of graphical models on ICF data for
different tasks: Visualization of the dependence structure of the
data set, dimension reduction and comparison of subpopulations.
Moreover, we further developed and applied recent findings in
causal inference using graphical models to estimate bounds on
intervention effects in an observational study with many variables
and without knowing the underlying causal structure. Results: In
each field, graphical models could be applied giving results of
high face-validity. In particular, graphical models could be used
for visualization of functioning in patients with spinal cord
injury. The resulting graph consisted of several connected
components which can be used for dimension reduction. Moreover, we
found that the differences in the dependence structures between
subpopulations were relevant and could be systematically analyzed
using graphical models. Finally, when estimating bounds on causal
effects of ICF categories on general health perceptions among
patients with chronic health conditions, we found that the five ICF
categories that showed the strongest effect were plausible.
Conclusions: Graphical Models are a flexible tool and lend
themselves for a wide range of applications. In particular, studies
involving ICF data seem to be suited for analysis using graphical
models.