Artificial Intelligence in Diagnostics: Innovations for Resource-Limited Healthcare in Malawi
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2013
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| _version_ | 1866901724243951616 |
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| author | Chinyarama, Chirwa |
| author_facet | Chinyarama, Chirwa |
| contents | <p>This study addresses a current research gap in Computer Science concerning AI Applications for Disease Diagnosis in Resource-Limited Healthcare Settings in Malawi in Malawi. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. AI Applications for Disease Diagnosis in Resource-Limited Healthcare Settings in Malawi, Malawi, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19005945 |
| institution | Zenodo |
| language | eng |
| publishDate | 2013 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Artificial Intelligence in Diagnostics: Innovations for Resource-Limited Healthcare in Malawi Chinyarama, Chirwa African healthcare Machine learning Data analytics Diagnostic algorithms Resource allocation Precision medicine Geographic information systems <p>This study addresses a current research gap in Computer Science concerning AI Applications for Disease Diagnosis in Resource-Limited Healthcare Settings in Malawi in Malawi. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. AI Applications for Disease Diagnosis in Resource-Limited Healthcare Settings in Malawi, Malawi, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.</p> |
| title | Artificial Intelligence in Diagnostics: Innovations for Resource-Limited Healthcare in Malawi |
| topic | African healthcare Machine learning Data analytics Diagnostic algorithms Resource allocation Precision medicine Geographic information systems |
| url | https://doi.org/10.5281/zenodo.19005945 |