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Midwifery learning and forecasting: Predicting content demand with user-generated logs()
Every day, 800 women and 6700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help improve thei...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Science Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102717/ https://www.ncbi.nlm.nih.gov/pubmed/36990589 http://dx.doi.org/10.1016/j.artmed.2023.102511 |
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author | Guitart, Anna del Río, Ana Fernández Periáñez, África Bellhouse, Lauren |
author_facet | Guitart, Anna del Río, Ana Fernández Periáñez, África Bellhouse, Lauren |
author_sort | Guitart, Anna |
collection | PubMed |
description | Every day, 800 women and 6700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help improve their learning competencies. In this work, we evaluate various forecasting methods to determine the future interest of users for the different types of content available in the Safe Delivery App, a digital training tool for skilled birth attendants, broken down by profession and region. This first attempt at health content demand forecasting for midwifery learning shows that DeepAR can accurately anticipate content demand in operational settings, and could therefore be used to offer users personalized content and to provide an adaptive learning journey. |
format | Online Article Text |
id | pubmed-10102717 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier Science Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-101027172023-04-15 Midwifery learning and forecasting: Predicting content demand with user-generated logs() Guitart, Anna del Río, Ana Fernández Periáñez, África Bellhouse, Lauren Artif Intell Med Article Every day, 800 women and 6700 newborns die from complications related to pregnancy or childbirth. A well-trained midwife can prevent most of these maternal and newborn deaths. Data science models together with logs generated by users of online learning applications for midwives can help improve their learning competencies. In this work, we evaluate various forecasting methods to determine the future interest of users for the different types of content available in the Safe Delivery App, a digital training tool for skilled birth attendants, broken down by profession and region. This first attempt at health content demand forecasting for midwifery learning shows that DeepAR can accurately anticipate content demand in operational settings, and could therefore be used to offer users personalized content and to provide an adaptive learning journey. Elsevier Science Publishing 2023-04 /pmc/articles/PMC10102717/ /pubmed/36990589 http://dx.doi.org/10.1016/j.artmed.2023.102511 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Guitart, Anna del Río, Ana Fernández Periáñez, África Bellhouse, Lauren Midwifery learning and forecasting: Predicting content demand with user-generated logs() |
title | Midwifery learning and forecasting: Predicting content demand with user-generated logs() |
title_full | Midwifery learning and forecasting: Predicting content demand with user-generated logs() |
title_fullStr | Midwifery learning and forecasting: Predicting content demand with user-generated logs() |
title_full_unstemmed | Midwifery learning and forecasting: Predicting content demand with user-generated logs() |
title_short | Midwifery learning and forecasting: Predicting content demand with user-generated logs() |
title_sort | midwifery learning and forecasting: predicting content demand with user-generated logs() |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102717/ https://www.ncbi.nlm.nih.gov/pubmed/36990589 http://dx.doi.org/10.1016/j.artmed.2023.102511 |
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