M2-DIA: Enhancing Diagnostic Capabilities in Imbalanced Disease Data using Multimodal Diagnostic Ensemble Framework
Bibliographic record
Abstract
The ADS (Automatic Diagnosis System) has garnered increasing attention for its potential to aid clinicians and enhance healthcare accessibility for patients. These systems are designed to identify a wide range of diseases, including those that may be overlooked by human doctors. However, the diagnostic data used in ADS suffer from disease imbalances due to statistical and medical rare diseases, limiting the scope of diagnosable conditions and finally reducing the accuracy of disease identification. Previous research attempted to address this imbalance using knowledge injection networks during the training phase. However, this approach relies on a training process makes it challenging to diagnose diseases absent from the training data, even with the use of knowledge data. To address this problem, we introduce Multimodal Diagnostic Ensemble Framework called M2-DIA which employs multiple representations—Text, Statistics, and One-hot vectors—derived from a patient’s medical condition. This multi-faceted approach improves its diagnostic capabilities, especially for diseases that are not present in training data. We evaluated our framework on the public dataset with multiple test datasets to verify diagnostic ability from various angles and showed our approach surpasses existing works.
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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 | 0.002 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".