Prospects of Big Data Analytics in Africa Healthcare System
Bibliographic record
Abstract
Big data is having a positive impact in almost every sphere of life, such as in military intelligence, space science, aviation, banking, and health. Big data is a growing force in healthcare. Even though healthcare systems in the developed world are recording some breakthroughs due to the application of big data, it is important to research the impact of big data in developing regions of the world, such as Africa. Healthcare systems in Africa are, in relative terms, behind the rest of the world. Platforms and technologies used to amass big data such as the Internet and mobile phones are already in use in Africa, thereby making big data applications to be emerging. Hence, the key research question we address is whether big data applications can improve healthcare in Africa especially during epidemics and through the public health system. In this study, a literature review is carried out, firstly to present cases of big data applications in healthcare in Africa, and secondly, to explore potential ethical challenges of such applications. This review will provide an update on the application of big data in the health sector in Africa that can be useful for future researchers and health care practitioners in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".