A hierarchical network model for the estimate of the energy expenditure in individuals with type 1 diabetes
Bibliographic record
Abstract
Total daily energy expenditure (TDEE) is impacted by many medical conditions, such as diabetes. In the case of type 1 diabetes (T1D), individuals need to have an accurate assessment of the energy expenditure in real-time to avoid dietary imbalance, and improve glycemic control. This work proposes a hierarchical Long Short-Term Memory (LSTM)-based modeling approach to predict real-time continuous energy expenditure, expressed as metabolic equivalents (METs), for individuals with T1D on a 24-hour basis by leveraging the step count and heart rate data from a wrist-band smartwatch. To deal with the inter- and intra-individual variability, the proposed model uses three different LSTMs to capture population, activity-type and subject scale information. To evaluate the impact of the components of the hierarchy, the performance of the proposed hierarchical model was assessed at each level. The results show that the combination of population data, such as heart rate and step counts, with individual data in a hierarchical architecture helps to achieve superior prediction performance, than using only individual heart rate and step counts data.Additionally, compared to non-hierarchical modeling, the hierarchical modeling can provide precise and individualized prediction of the METs categories, as it allows the integration of the variation at different levels of the hierarchy. This model can be used to augment current automated insulin delivery (AID) systems to adapt insulin infusion according to the predicted activity intensity and compensate for glycemic perturbations due to exercise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".