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While machine learning (ML) includes a valuable array of tools for analyzingbiomedical data, significant time and expertise is required to assembleeffective, rigorous, and unbiased pipelines. Automated ML (AutoML) tools seekto facilitate ML application by automating a subset of analysis pipelineelements. In this study we develop and validate a Simple, Transparent,End-to-end Automated Machine Learning Pipeline (STREAMLINE) and apply it toinvestigate the added utility of photography-based phenotypes for predictingobstructive sleep apnea (OSA); a common and underdiagnosed condition associatedwith a variety of health, economic, and safety consequences. STREAMLINE isdesigned to tackle biomedical binary classification tasks while adhering tobest practices and accommodating complexity, scalability, reproducibility,customization, and model interpretation. Benchmarking analyses validated theefficacy of STREAMLINE across data simulations with increasingly complexpatterns of association. Then we applied STREAMLINE to evaluate the utility ofdemographics (DEM), self-reported comorbidities (DX), symptoms (SYM), andphotography-based craniofacial (CF) and intraoral (IO) anatomy measures inpredicting any OSA or moderate/severe OSA using 3,111 participants from SleepApnea Global Interdisciplinary Consortium (SAGIC). OSA analyses identified asignificant increase in ROC-AUC when adding CF to DEM+DX+SYM to predictmoderate/severe OSA. A consistent but non-significant increase in PRC-AUC wasobserved with the addition of each subsequent feature set to predict any OSA,with CF and IO yielding minimal improvements. Application of STREAMLINE to OSAdata suggests that CF features provide additional value in predictingmoderate/severe OSA, but neither CF nor IO features meaningfully improved theprediction of any OSA beyond established demographics, comorbidity and symptomcharacteristics.

STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine  Applied to Examine the Utility of Photography-Based Phenotypes for OSA  Prediction Across International Sleep Centers