Multi-label Classification of Anemia Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work examines the application of machine learning to an important area of medicine which aims to diagnose paediatric patients with β-thalassemia minor, iron deficiency anemia or the co-occurrence of these ailments. Iron deficiency anemia is a major cause of microcytic anemia and is considered an important task in global health. Whilst existing methods, based on linear equations, are proficient at distinguishing between the two classes of anemia, they fail to identify the co-occurrence of this issues. Machine learning algorithms, however, can induce non-linear decision boundaries that enable accurate classification within complex domains. Through a multi-label classification technique, known as problem transformations, we convert the learning task to one that is appropriate for machine learning and examine the effectiveness of machine learning algorithms on this domain. Our results show that machine learning classifiers produce good overall accuracy and are able to identify instances of the co-occurrence class unlike the existing methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it