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This paper is in the following e-collection/theme issue:

Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (104) Applications of AI (345) Advanced Data Analytics in eHealth (237) Machine Learning (2234) Pediatrics (174) Descriptive Epidemiology and Population Size Estimates (150) Public Health Policy (115) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (748)

Published on in Vol 17 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66798, first published .
Forecasting Neonatal Mortality in Ethiopia to Assess Progress Toward National and International Reduction Targets Using Classical Techniques and Deep Learning: Time-Series Forecasting Study

Forecasting Neonatal Mortality in Ethiopia to Assess Progress Toward National and International Reduction Targets Using Classical Techniques and Deep Learning: Time-Series Forecasting Study

Forecasting Neonatal Mortality in Ethiopia to Assess Progress Toward National and International Reduction Targets Using Classical Techniques and Deep Learning: Time-Series Forecasting Study

Authors of this article:

Shimels Derso Kebede1 Author Orcid Image
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  1. Moretti C, Gizzi C, Trevisanuto D, Lista G, Carnielli V, Saugstad O, Gagliardi L, Vertecchi G, Jira L, Demtse A, Metaferia G, Gatta L, Manenti F, Carraro D, Bogale W. Strengthening Neonatology in Ethiopia: From Survey Data to System Improvement. Neonatology 2026:1 View