  While machine learning (ML) includes a valuable array of tools for analyzing
biomedical data, significant time and expertise is required to assemble
effective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seek
to facilitate ML application by automating a subset of analysis pipeline
elements. In this study we develop and validate a Simple, Transparent,
End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it to
investigate the added utility of photography-based phenotypes for predicting
obstructive sleep apnea (OSA); a common and underdiagnosed condition associated
with a variety of health, economic, and safety consequences. STREAMLINE is
designed to tackle biomedical binary classification tasks while adhering to
best practices and accommodating complexity, scalability, reproducibility,
customization, and model interpretation. Benchmarking analyses validated the
efficacy of STREAMLINE across data simulations with increasingly complex
patterns of association. Then we applied STREAMLINE to evaluate the utility of
demographics (DEM), self-reported comorbidities (DX), symptoms (SYM), and
photography-based craniofacial (CF) and intraoral (IO) anatomy measures in
predicting any OSA or moderate/severe OSA using 3,111 participants from Sleep
Apnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified a
significant increase in ROC-AUC when adding CF to DEM+DX+SYM to predict
moderate/severe OSA. A consistent but non-significant increase in PRC-AUC was
observed with the addition of each subsequent feature set to predict any OSA,
with CF and IO yielding minimal improvements. Application of STREAMLINE to OSA
data suggests that CF features provide additional value in predicting
moderate/severe OSA, but neither CF nor IO features meaningfully improved the
prediction of any OSA beyond established demographics, comorbidity and symptom
characteristics.
