A framework for cell phone based diagnosis and management of priority tropical diseases
Bibliographic record
Abstract
Malaria, pneumonia, tuberculosis, typhoid fever, amebiasis, and diarrheal diseases are considered existing global health priorities. This is because of their global prevalence, especially in most developing (tropical) countries. These conditions pose a lot of challenges to global health and wellbeing due to their increasing morbidity and mortality rates; a challenge that has been attributed to poor medical infrastructure, poor diagnosis and management of these diseases. These conditions are known to present with similar symptoms at different stages of their pathogenesis and thus can become “confusable” with each other. Medical practitioners attempting to diagnose and manage these conditions are therefore expected to manage large amounts of information (which can sometimes become unwieldy and time wasting) in order to arrive at an accurate and timely diagnosis. Medical facilities can be freed up through the adoption of mobile devices for early diagnosis of some of the tropical conditions. In this paper, we present a framework for a cell phone based intelligent system (based on fuzzy logic and AHP engines) for the diagnosis of some tropical global health priorities. Fuzzy logic and the analytic hierarchy process (AHP) are known to resolve the conflicts arising from ambiguity, uncertainty, and imprecision of information, and thus can be harnessed in the analysis of information supplied by patients in the cell phone-based diagnosis of confusing tropical diseases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".