ArticleArtificial intelligence in medicine2023
Midwifery learning and forecasting: Predicting content demand with user-generated logs.
Article in Artificial intelligence in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- The Revolution in Midwifery Education: How AI and Deep Learning are Transforming Outcome-Based Assessments?Advances in medical education and practice · 2025Review
- Use of digital technologies for staff education and training programmes on newborn resuscitation and complication management: a scoping review.BMJ paediatrics open · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.