The feces thesis: Using machine learning to detect diarrhea
Bibliographic record
Abstract
Cholera is a bacterial disease which induces extremely liquid diarrhea. It affects millions of people, resulting in up to about 150,000 deaths per year. In this study, a sensor is developed which can non-invasively determine if an outbreak may occur in an area, acting as an early detection method so that resources can be employed to stop the rapid spread of the disease. The sensor uses a microphone to collect audio samples of various excretion events. The collected acoustic data are pre-processed to produce mel spectrograms which capture the distinct temporal frequency characteristics of each excretion event. These mel spectrograms are input into a pre-trained convolutional neural network to classify the event as either diarrheal or non-diarrheal with up to 98.1% accuracy. The algorithm is also capable of classifying other excretion events such as urination, flatulence, and defecation. The sensor developed here could be applied to identify other use cases such as tracking bowl movements for hospice patients or for those with inflammatory bowel diseases like Crohn’s disease.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".