{"doi":"10.1109/tbme.2021.3129175","title":"Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-Resolution Electrogastrogram","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 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.","journal":"IEEE Transactions on Biomedical Engineering","year":2021,"id":201821,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":7,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.95,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":468939,"name":"Suseela Somarajan","orcid":"0000-0003-2453-6048","position":1,"is_corresponding":false},{"id":470335,"name":"Nicole D. Muszynski","orcid":null,"position":2,"is_corresponding":false},{"id":468940,"name":"Andrew H. Comstock","orcid":"0000-0002-7623-3332","position":3,"is_corresponding":false},{"id":781921,"name":"Kyra E. Hendrickson","orcid":null,"position":4,"is_corresponding":false},{"id":781241,"name":"Lauren Scott","orcid":"0000-0001-5301-7349","position":5,"is_corresponding":false},{"id":443203,"name":"Alexandra Russell","orcid":"0000-0003-0452-7662","position":6,"is_corresponding":false},{"id":781922,"name":"Sari A. Acra","orcid":null,"position":7,"is_corresponding":false},{"id":781923,"name":"Lynn Walker","orcid":null,"position":8,"is_corresponding":false},{"id":446019,"name":"Leonard A. Bradshaw","orcid":"0000-0003-4453-8606","position":9,"is_corresponding":false},{"id":781240,"name":"Joseph D. Olson","orcid":"0000-0002-5957-3001","position":0,"is_corresponding":true}],"reference_count":19,"raw_metadata":null,"created_at":"2026-07-18T23:51:05.955461Z","pmid":"34793297","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}