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Sankore

Named after the University of Sankore in Timbuktu — one of the world’s oldest centres of learning. Every figure here is computed from work-level records in our own graph, never copied from a source’s summary count.

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Evaluating How Explainable Ai-Driven Financial and Clinical Risk Models Improve Global Maternal, Neonatal and Mental Health Outcomes in Resource-limited Settings

2026Asian Journal of Advanced Research and ReportsJournalOpen access

10.9734/ajarr/2026/v20i21280 Full text0 citations

Authors

10 authors across 12 institutions in 3 countries.

Abayomi Ibrahim AdeagaFirst author
Felix Denkyi
Ugwumadu-Ihechi Jovita
Blessing Chinedu Ndubuisi
Ezekiel Oluwagbemileke Ilori
Adegoke Adebayo David
- University of Ibadan (NG)
Fatimah Abduljelil
Badmus Monsuru Owolabi
- Center for Clinical Care and Research in Nigeria (NG)
Godwin Pascal Madukwe
Confidence Adimchi, ChinonyeremSenior author
- Abia State University (NG)
- Delta State Polytechnic Ogwashi-Uku (NG)

Fields

Assignment is probabilistic — a work belongs to several fields in proportions.

Explainable Artificial Intelligence (XAI)level 3 · 0.11
Artificial Intelligence in Healthcare and Educationlevel 3 · 0.09
Machine Learning in Healthcarelevel 3 · 0.07

Sources

Several sources describing one work is the point — it means the record rests on more than one authority.

openalexseen September 9, 2026
2026 · Asian Journal of Advanced Research and Reports · 0 citations
https://openalex.org/W7128443911

Evaluating How Explainable Ai-Driven Financial and Clinical Risk Models Improve Global Maternal, Neonatal and Mental Health Outcomes in Resource-limited Settings · Sankore