Computer-Based Intelligent Solutions for the Diagnosis of Gastroesophageal Reflux Disease Phenotypes and Chicago Classification 3.0

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dc.authorscopusid57212629075
dc.authorscopusid7004013649
dc.contributor.authorDogan, Yunus
dc.contributor.authorBor, Serhat
dc.date.accessioned2024-08-25T18:53:35Z
dc.date.available2024-08-25T18:53:35Z
dc.date.issued2023
dc.departmentEge Üniversitesien_US
dc.description.abstractGastroesophageal reflux disease (GERD) is a multidisciplinary disease; therefore, when treating GERD, a large amount of data needs to be monitored and managed.The aim of our study was to develop a novel automation and decision support system for GERD, primarily to automatically determine GERD and its Chicago Classification 3.0 (CC 3.0) phenotypes. However, phenotyping is prone to errors and is not a strategy widely known by physicians, yet it is very important in patient treatment. In our study, the GERD phenotype algorithm was tested on a dataset with 2052 patients and the CC 3.0 algorithm was tested on a dataset with 133 patients. Based on these two algorithms, a system was developed with an artificial intelligence model for distinguishing four phenotypes per patient. When a physician makes a wrong phenotyping decision, the system warns them and provides the correct phenotype. An accuracy of 100% was obtained for both GERD phenotyping and CC 3.0 in these tests. Finally, since the transition to using this developed system in 2017, the annual number of cured patients, around 400 before, has increased to 800. Automatic phenotyping provides convenience in patient care, diagnosis, and treatment management. Thus, the developed system can substantially improve the performance of physicians.en_US
dc.identifier.doi10.3390/healthcare11121790
dc.identifier.issn2227-9032
dc.identifier.issue12en_US
dc.identifier.pmid37372907en_US
dc.identifier.scopus2-s2.0-85163738889en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/healthcare11121790
dc.identifier.urihttps://hdl.handle.net/11454/103171
dc.identifier.volume11en_US
dc.identifier.wosWOS:001014888300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.ispartofHealthcareen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240825_Gen_US
dc.subjectartificial intelligenceen_US
dc.subjecthealthcare systemsen_US
dc.subjectphenotypingen_US
dc.subjectSystemen_US
dc.titleComputer-Based Intelligent Solutions for the Diagnosis of Gastroesophageal Reflux Disease Phenotypes and Chicago Classification 3.0en_US
dc.typeArticleen_US

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