Segun Fatumo
Biographic Data
| ID | 415933 |
|---|---|
| NAME | Segun Fatumo |
| GIVEN NAMES | Segun |
| FAMILY NAME | Fatumo |
| SIGNATURE | FATUMO S |
| AFFILIATIONS | London School of Hygiene & Tropical Medicine |
| ORCID | 0000-0003-4525-3362 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 1 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Predicting suicidality in people living with HIV in Uganda: A machine learning approach
A cost-sensitive AdaBoost model developed using the sociodemographic, psychosocial, and clinical data of PLWH in Uganda can predict suicidality risk, albeit with modest PPV. Incorporating suicidality PRS improved the overall predictive performance of the model. However, larger studies involving more diverse participants are needed to evaluate the potential of PRS in enhancing risk stratification and the clinical utility of the prediction model
Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis
Retrospective studies on artificial intelligence (AI) in screening for diabetic retinopathy (DR) have shown promising results in addressing the mismatch between the capacity to implement DR screening and increasing DR incidence. This review sought to evaluate the diagnostic test accuracy (DTA) of AI in screening for referable diabetic retinopathy (RDR) in real-world settings. We searched CENTRAL, PubMed, CINAHL, Scopus, and Web of Science on 9 Fe…
African genomes hold the key to accurate genetic risk prediction
No prominent works on this page.
Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis
Retrospective studies on artificial intelligence (AI) in screening for diabetic retinopathy (DR) have shown promising results in addressing the mismatch between the capacity to implement DR screening and increasing DR incidence. This review sought to evaluate the diagnostic test accuracy (DTA) of AI in screening for referable diabetic retinopathy (RDR) in real-world settings. We searched CENTRAL, PubMed, CINAHL, Scopus, and Web of Science on 9 Fe…
African genomes hold the key to accurate genetic risk prediction
Predicting suicidality in people living with HIV in Uganda: A machine learning approach
A cost-sensitive AdaBoost model developed using the sociodemographic, psychosocial, and clinical data of PLWH in Uganda can predict suicidality risk, albeit with modest PPV. Incorporating suicidality PRS improved the overall predictive performance of the model. However, larger studies involving more diverse participants are needed to evaluate the potential of PRS in enhancing risk stratification and the clinical utility of the prediction model
Computer Science (2 works) · AdaBoost (1 works) · Artificial Intelligence (1 works) · Biology (1 works) · Computational biology (1 works) · Computer security (1 works) · Decision tree (1 works) · Evolutionary biology (1 works) · Generalizability theory (1 works) · Genetic Associations and Epidemiology (1 works)