Machine learning techniques to predict diabetic ketoacidosis and HbA1c above 7% among individuals with type 1 diabetes — A large multi-centre study in Australia and New Zealand
Bibliographic record
Abstract
BACKGROUND AND AIMS: Type 1 diabetes and diabetic ketoacidosis (DKA) have a significant impact on individuals and society across a wide spectrum. Our objective was to utilize machine learning techniques to predict DKA and HbA1c>7 %. METHODS AND RESULTS: Nine different models were implemented and model performance evaluated via the Area under the Curve (AUC). These models were applied to a large multi-centre dataset of 13761 type 1 diabetes individuals prospectively recruited from Australia and New Zealand. Predictive features included a number of clinical demographic and socio-economic measures collected at previous visits. In our study, 2.9 % reported at least one episode of DKA since their last clinic visit. A number of features were significantly associated with DKA. Our results showed that Deep Learning (DL) model performed well in predicting DKA with an AUC of 0.887. The DL also provided the lowest classification error rate of 0.9 %, highest sensitivity of 99.9 % and F-measure of 99.6 %. As for HbA1c >7 %, the optimal Support Vector Machine provided a good AUC of 0.884. CONCLUSION: Machine learning models can be effectively implemented on real-life large clinical datasets and they perform well in terms of identifying individuals with type 1 diabetes at risk of adverse outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".