Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity

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dc.authorscopusid57215288222
dc.authorscopusid56814875900
dc.authorscopusid55318646300
dc.authorscopusid36115433400
dc.authorscopusid55901258100
dc.authorscopusid15026635000
dc.authorscopusid56295278200
dc.contributor.authorKaratekin, Tamer
dc.contributor.authorSancak, Selim
dc.contributor.authorÇelik, Gökhan
dc.contributor.authorTopçuo?lu, Sevilay
dc.contributor.authorKaratekin, Güner
dc.contributor.authorKirci, Pinar
dc.contributor.authorOkatan, Ali
dc.date.accessioned2024-09-11T19:58:57Z
dc.date.available2024-09-11T19:58:57Z
dc.date.issued2019
dc.departmentİstanbul Gelişim Üniversitesien_US
dc.description2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019 -- 26 August 2019 through 28 August 2019 -- Istanbul -- 153122en_US
dc.description.abstractWe have investigated the risk factors that lead to severe retinopathy of prematurity using statistical analysis and logistic regression as a form of generalized additive model (GAM) with pairwise interaction terms (GA2M). In this process, we discuss the trade-off between accuracy and interpretability of these machine learning techniques on clinical data. We also confirm the intuition of expert neonatologists on a few risk factors, such as gender, that were previously deemed as clinically not significant in RoP prediction. © 2019 IEEE.en_US
dc.identifier.doi10.1109/Deep-ML.2019.00020
dc.identifier.endpage66en_US
dc.identifier.isbn978-172812914-3en_US
dc.identifier.scopus2-s2.0-85074884539en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage61en_US
dc.identifier.urihttps://doi.org/10.1109/Deep-ML.2019.00020
dc.identifier.urihttps://hdl.handle.net/11363/8596
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240903_Gen_US
dc.subjectGA2M; GAM; generalized additive model; interpretability of machine learning in healthcare; logistic regression; neonatology; Retinopathy of Prematurity (RoP)en_US
dc.titleInterpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurityen_US
dc.typeConference Objecten_US

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