  Several algorithms for learning the structure of dynamic Bayesian networks
(DBNs) require an a priori ordering of variables, which influences the
determined graph topology. However, it is often unclear how to determine this
order if feature importance is unknown, especially as an exhaustive search is
usually impractical. In this paper, we introduce Ranking Approaches for Unknown
Structures (RAUS), an automated framework to systematically inform variable
ordering and learn networks end-to-end. RAUS leverages existing statistical
methods (Cramers V, chi-squared test, and information gain) to compare variable
ordering, resultant generated network topologies, and DBN performance. RAUS
enables end-users with limited DBN expertise to implement models via command
line interface. We evaluate RAUS on the task of predicting impending acute
kidney injury (AKI) from inpatient clinical laboratory data. Longitudinal
observations from 67,460 patients were collected from our electronic health
record (EHR) and Kidney Disease Improving Global Outcomes (KDIGO) criteria were
then applied to define AKI events. RAUS learns multiple DBNs simultaneously to
predict a future AKI event at different time points (i.e., 24-, 48-, 72-hours
in advance of AKI). We also compared the results of the learned AKI prediction
models and variable orderings to baseline techniques (logistic regression,
random forests, and extreme gradient boosting). The DBNs generated by RAUS
achieved 73-83% area under the receiver operating characteristic curve (AUCROC)
within 24-hours before AKI; and 71-79% AUCROC within 48-hours before AKI of any
stage in a 7-day observation window. Insights from this automated framework can
help efficiently implement and interpret DBNs for clinical decision support.
The source code for RAUS is available in GitHub at
https://github.com/dgrdn08/RAUS .
