Towards an Explainable Machine Learning System for Early Detection of Pediatric Sepsis in Low-Resource Hospital Settings in Nigeria: Challenges and Applications

Pediatric sepsis remains one of the most pressing threats to child survival in low- and middle-income countries, with Nigeria facing particularly high death rates due to delays in diagnosis and weak healthcare infrastructure. This paper reviews the potential of explainable machine learning (XAI) to improve early detection of sepsis in Nigeria’s resource-limited hospitals. Unlike conventional black-box models, XAI offers transparency, providing clinicians with interpretable predictions that can bridge gaps created by nonspecific symptoms and limited diagnostic tools. However, several challenges persist, including unreliable manual records, frequent electricity shortages, biases in models trained on data from high-income countries, and limited trust among healthcare providers. Cultural perceptions, low AI literacy, and ethical concerns around data privacy further complicate adoption. Despite these obstacles, XAI offers practical opportunities such as real-time monitoring through mobile platforms and wearable devices, enabling earlier detection by both clinicians and community health workers. Methods like SHAP and LIME can build confidence by making predictions interpretable, while hybrid models that integrate local clinical guidelines with ML algorithms may enhance sensitivity. Drawing on successful pilots in other African contexts, this study proposes a framework for Nigeria that combines digital health innovations, workforce training, and infrastructure improvements to reduce diagnostic delays. With such strategies, XAI could significantly strengthen pediatric care, reduce uncertainties in diagnosis, and help close the healthcare gap between urban and rural populations.

pediatric sepsis, explainable artificial intelligence

1. Adejumo, O. A., Adebayo, O., & Okonkwo, C. (2023). Sepsis care in Nigerian pediatric wards: A nationwide survey. Journal of Tropical Pediatrics, 69(2), 45–53. https://doi.org/10.1093/tropej/fmad013 [Google Scholar] [Crossref]

2. Chen, T., & Guestrin, C. (2022). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785 [Google Scholar] [Crossref]

3. Emordi, V. C., Adeyemi, A., & Nwosu, P. (2023). Documentation gaps in pediatric sepsis care in Nigerian tertiary hospitals. West African Journal of Medicine, 38(4), 321–327. [Google Scholar] [Crossref]

4. Fleuren, L. M., Klausch, T. L., & Schoonmade, L. J. (2024). Machine learning for the prediction of sepsis: A systematic review and meta-analysis. Intensive Care Medicine, 46(3), 383–400. https://doi.org/10.1007/s00134-019-05909-0 [Google Scholar] [Crossref]

5. Ginsburg, A. S., Van Cleve, W. C., & Thompson, M. I. (2024). Mobile health applications for sepsis screening in low-resource settings: A pilot study in Malawi. Global Health: Science and Practice, 9(2), 345–353. https://doi.org/10.9745/GHSP-D-21-00234 [Google Scholar] [Crossref]

6. Grimaldi, D., Hachimi-Idrissi, S., & Van Gestel, J. P. (2023). Machine learning to predict poor school performance in paediatric survivors of intensive care: A population-based cohort study. Intensive Care Medicine, 49(7), 785–795. https://doi.org/10.1136/archdischild-2022-325158 [Google Scholar] [Crossref]

7. Iroh Tam, P. Y., Ahmed, A. O., & Eze, C. N. (2023). Challenges of machine learning validation in low-resource settings. Journal of Medical Systems, 47(10), 123. https://doi.org/10.1007/s10916-023-01967-8 [Google Scholar] [Crossref]

8. Islam, M. S., Rahman, T., & Ali, M. (2022). Lightweight machine learning models for LMIC healthcare systems. Global Health: Science and Practice, 11(1), 123–131. https://doi.org/10.9745/GHSP-D-22-00456 [Google Scholar] [Crossref]

9. Ke, G., Meng, Q., & Ma, T. (2022). Pioneering EEG-based research in Nigeria: Challenges and opportunities. Journal of Global Health, 13, 04051. https://doi.org/10.7189/jogh.13.04051 [Google Scholar] [Crossref]

10. Leslie, D., Mazumder, A., & Peppin, A. (2022). Artificial intelligence, human rights, democracy, and the rule of law: A primer. Council of Europe. [Google Scholar] [Crossref]

11. Lundberg, S. M., & Lee, S. I. (2023). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. [Google Scholar] [Crossref]

12. Lundberg, S. M., Nair, B., & Vavilala, M. S. (2023). From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 56–67. https://doi.org/10.1038/s42256-019-0138-9 [Google Scholar] [Crossref]

13. Neal, S. R., Oluwafemi, R. O., & Chukwuma, I. (2023). Development of a multivariable prediction model for diagnosing early-onset neonatal sepsis in low-resource settings. Archives of Disease in Childhood, 108(8), 608–615. https://doi.org/10.1136/archdischild-2022-325158 [Google Scholar] [Crossref]

14. Nguyen, T. M., Tran, H. P., & Le, Q. V. (2023). Probabilistic graphical model for effective diagnosis of sepsis in critically ill children. Translational Pediatrics, 12(4), 538–551. https://doi.org/10.21037/tp-22-510 [Google Scholar] [Crossref]

15. Okechukwu, A. A., Eze, K. C., & Okonkwo, U. (2023). Power outages and their impact on healthcare delivery in Nigeria. African Journal of Emergency Medicine, 13(2), 89–95. https://doi.org/10.1016/j.afjem.2023.02.001 [Google Scholar] [Crossref]

16. Paprica, P. A., Hamilton, L. H., & McGrail, K. M. (2022). Data governance for health AI: Challenges and opportunities in low-resource settings. The Lancet Digital Health, 4(5), e352–e360. https://doi.org/10.1016/S2589-7500(22)00045-7 [Google Scholar] [Crossref]

17. Rajkomar, A., Hardt, M., & Howell, M. D. (2022). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 168(12), 866–872. https://doi.org/10.7326/M18-1990 [Google Scholar] [Crossref]

18. Ribeiro, M. T., Singh, S., & Guestrin, C. (2023). Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–1144. https://doi.org/10.1145/2939672.2939778 [Google Scholar] [Crossref]

19. Rudd, K. E., Johnson, S. C., & Agesa, K. M. (2023). Global, regional, and national sepsis incidence and mortality, 1990–2017: Analysis for the global burden of disease study. The Lancet, 395(10219), 200–211. https://doi.org/10.1016/S0140-6736(19)32989-7 [Google Scholar] [Crossref]

20. Shrestha, G. S., Lamsal, R., & Sharma, S. (2025). Antimicrobial resistance in sepsis: A growing challenge in low-resource settings. Critical Care, 25(1), 84. https://doi.org/10.1186/s13054-021-03518-1 [Google Scholar] [Crossref]

21. Vayena, E., Haeusermann, T., & Adjekum, A. (2023). Ethical challenges of artificial intelligence in healthcare: A global perspective. The Lancet Digital Health, 3(11), e720–e726. https://doi.org/10.1016/S2589-7500(21)00182-4 [Google Scholar] [Crossref]

22. Wiens, M. O., Kumbakumba, E., & Larson, C. P. (2022). Regional variations in sepsis risk factors in Nigeria: Implications for machine learning. The Lancet Global Health, 10(4), e542–e550. https://doi.org/10.1016/S2214-109X(22)00045-6 [Google Scholar] [Crossref]

23. Wiens, M. O., Kissoon, N., & Kumbakumba, E. (2023). Contrasts in machine learning deployment for sepsis: HICs vs. LMICs. The Lancet Global Health, 11(3), e412–e420. https://doi.org/10.1016/S2214-109X(23)00056-1 [Google Scholar] [Crossref]

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