Data science and artificial intelligence for maternal, newborn and child health
Scoping review and thematic analysis
Bibliographic Data
The limited application of data science and artificial intelligence in MNCH at the national level highlights a significant gap in Africa. Our review found that 28 studies (53.8%) were research projects compared to operational implementations integrated into routine systems to inform policy. Main barriers include inadequate infrastructure, limited data stewardship capacity, and insufficient government commitment. Opportunities exist through Africa's youthful demographics, expanding mobile technology, and latecomer advantage in digital innovation
Big data · Biostatistics · Data collection · Government (linguistics · Implementation · Public health · Stewardship (theology · Thematic analysis · Global Maternal and Child Health · ICT in Developing Communities · Mobile Health and mHealth Applications · Health Informatics
National, regional, and global estimates of preterm birth in 2020, with trends from 2010
Every Newborn
The Fair Guiding Principles for scientific data management and stewardship
Enabling data sharing and utilization for African population health data using OHDSI tools with an Omop-common data model
Performance of predictive algorithms in estimating the risk of being a zero-dose child in India, Mali and Nigeria
Implementing the routine immunisation data module and dashboard of DHIS2 in Nigeria, 2014–2019
Global strategies and local implementation of health and health-related SDGs
Machine learning predictive modelling for identification of predictors of acute respiratory infection and diarrhoea in Uganda’s rural and urban settings
Machine learning prediction of gestational age from metabolic screening markers resistant to ambient temperature transportation
Harnessing the potential of African youth for transforming health research in Africa
Weeping in silence
Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia
Associations in the continuum of care for maternal, newborn and child health
Big Data Needs and Challenges to Advance Research on Racial and Ethnic Inequities in Maternal and Child Health
Machine learning algorithms for predicting undernutrition among under-five children in Ethiopia
Newborn survival
Scoping studies
| Citation velocity | historical |
|---|---|
| Highly cited | No |