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Master of Public Health

Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-resolution Electrogastrogram

AUTHORS

Olson JD , Somarajan S , Muszynski N , Comstock AH , Hendrickson KE , Scott L , Acra SA , Russell A , Bradshaw LA , . IEEE transactions on bio-medical engineering. 2021 11 18; PP().

ABSTRACT

OBJECTIVE: Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysiology of pediatric functional nausea from healthy controls would be an invaluable aid to support clinical decision-making in diagnosis and management of patient treatment methodology. The purpose of this paper is to present an innovative approach for objectively classifying pediatric functional nausea using cutaneous high-resolution electrogastrogram data.

METHODS: We present an Automated Electrogastrogram Data Analytics Pipeline framework and demonstrate its use in a 3×8 factorial design to identify an optimal classification model according to a defined objective function. Low-fidelity synthetic high-resolution electrogastrogram data were generated to validate outputs and determine SOBI-ICA noise reduction effectiveness.

RESULTS: A 10 parameter support vector machine binary classifier with a radial basis function was selected as the overall top-performing model from a pool of over 1000 alternatives via maximization of an objective function. This resulted in a 91.6% test ROC AUC score.

CONCLUSION: Using an automated machine learning pipeline approach to process high-resolution electrogastrogram data allows for clinically significant objective classification of pediatric functional nausea.

SIGNIFICANCE: To our knowledge, this is the first study to demonstrate clinically significant performance in the objective classification of pediatric nausea patients from healthy control subjects using experimental high-resolution electrogastrogram data. These results indicate a promising potential for high-resolution electrogastrography to serve as a data-driven screening tool for the objective diagnosis of pediatric functional nausea.

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