Development and Validation of the Lafiya Clinical Knowledge Graph for Decision Support in Nigerian Primary Health Care (Preprint)

BACKGROUND Nigeria's primary healthcare (PHC) system serves over 200 million people through approximately 30,000 facilities staffed predominantly by Community Health Officers (CHOs) and Community Health Extension Workers (CHEWs) who operate without physician oversight. With only 0.4 physicians per 1,000 population, diagnostic accuracy at frontline facilities averages 65%, contributing to the country's disproportionate disease burden — including 25% of global malaria cases and an under-5 mortality rate of 114 per 1,000 live births. Existing clinical decision support tools (CDSTs) require stable internet connectivity or rely on generic algorithms unsuited to Nigeria's epidemiology and clinical guidelines. OBJECTIVE To describe the development, architecture, and validation of a Nigeria-specific clinical knowledge graph (KG) designed to power an offline-capable CDST for CHOs and CHEWs in Northern Nigerian PHC settings. METHODS The KG was constructed using a three-layer architecture: a multilingual semantic normalization layer (3,299 synonym mappings across English, Hausa, and Nigerian Pidgin) feeds into Layer A (medical ontology — diseases, symptoms, treatments), Layer B (guideline logic — diagnostic rules, 7-tier probabilistic weighting, age/sex gating, and deficit-gated differential rules), and Layer C (operational safety — drug interactions, age/sex gating, prescribing guardrails, and post-test confirmation overlay). Source content was derived from National Standing Orders for Community Health Practitioner, WHO Integrated Management of Childhood Illness (IMCI) guidelines, and 13 additional verified clinical guidelines. Validation comprised four independent assessments: (1) an internal benchmark of 381 generated clinical scenarios covering all 136 diagnoses; (2) a WHO-aligned CDSS safety validation suite of 203 adversarial scenarios across 7 safety categories; (3) adversarial paraphrase testing using novel Nigerian English, Hausa, and Pidgin symptom expressions; and (4) a free-text benchmark of 322 simulated patient presentations spanning all 136 diagnoses across 15 clinical domains. RESULTS The final KG contains 772 nodes and 2,052 edges encoding 136 diagnoses across 30 clinical categories and 6 age groups. Internal benchmarking demonstrated 90.0% top-1 diagnostic accuracy (clinical equivalence), 87.4% strict top-1 accuracy, 98.2% top-3 accuracy, and 97.5% emergency detection rate. The WHO-aligned validation achieved 97.5% overall accuracy (198/203 scenarios) with zero dangerous misses across 25 must-not-miss conditions (e.g., eclampsia, postpartum hemorrhage, acute kidney injury, poisoning). Adversarial paraphrase testing confirmed 96.3% accuracy on expressions never encountered during development. Free-text benchmarking — evaluating the system's end-to-end pipeline from unstructured patient language to differential diagnosis — achieved 68.3% top-1 accuracy, 91.9% top-3 accuracy, and 94.4% top-5 accuracy across 322 cases spanning all 136 diagnoses, with 99.3% emergency detection rate using clinical equivalence. CONCLUSIONS A rigorously validated, Nigeria-specific clinical KG can achieve diagnostic accuracy and safety performance comparable to international benchmarks while operating within the linguistic and infrastructural constraints of low-resource PHC settings. The three-layer architecture and deterministic reasoning approach offer a reproducible framework for developing context-specific CDSTs in other low- and middle-income countries. CLINICALTRIAL NA