Interpretable Machine Learning in Healthcare through Generalized Additive Model with Pairwise Interactions (GA2M): Predicting Severe Retinopathy of Prematurity
Küçük Resim Yok
Tarih
2019
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
We 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.
Açıklama
2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019 -- 26 August 2019 through 28 August 2019 -- Istanbul -- 153122
Anahtar Kelimeler
GA2M; GAM; generalized additive model; interpretability of machine learning in healthcare; logistic regression; neonatology; Retinopathy of Prematurity (RoP)
Kaynak
Proceedings - 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications, Deep-ML 2019
WoS Q Değeri
Scopus Q Değeri
N/A
