Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran

Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran

Abstract Background The high number of COVID-19 deaths is a serious threat to the world. Demographic and clinical biomarkers are significantly associated with the mortality risk of this disease. This study aimed to implement Generalized Neural Additive Model (GNAM) as an interpretable machine learni...

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Bibliographic Details
Main Authors: Samad Moslehi, Hossein Mahjub, Maryam Farhadian, Ali Reza Soltanian, Mojgan Mamani
Format: Article
Language:English
Published: BMC 2022-12-01
Series:BMC Medical Research Methodology
Subjects:
COVID-19
Feature selection
Laboratory markers
Machine learning
Generalized neural additive
Prediction
Online Access:https://doi.org/10.1186/s12874-022-01827-y

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