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

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 5; pp. 1717 - 1725
• Main Authors: Olson, Joseph D., Somarajan, Suseela, Muszynski, Nicole D., Comstock, Andrew H., Hendrickson, Kyra E., Scott, Lauren, Russell, Alexandra, Acra, Sari A., Walker, Lynn, Bradshaw, Leonard A.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automated machine learning; Automation; Child; Classification; clinical decision support; Decision making; Diagnosis; electrogastrogram; Electromyography; Factorial design; High resolution; Humans; Kernel functions; Learning algorithms; Linear programming; Machine Learning; Medical diagnostic imaging; Nausea; Nausea - diagnosis; Noise reduction; Objective function; Optimization; Patients; pediatric functional nausea; Pediatrics; Physics; Pipelines; Radial basis function; Support Vector Machine; Support vector machines
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3129175

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 3x8 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 kernel 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.