Haemoglobin response modelling under erythropoietin treatment: Physiological model‐informed machine learning method
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Patients with renal anaemia are usually treated with recombinant human erythropoietin (EPO) because of insufficient renal EPO secretion. The establishment of a good haemoglobin (Hgb) response model is a necessary condition for dose optimization design. The purpose of this paper is to apply physics‐informed neural networks (PINN) to build the Hgb response model under EPO treatment. Neural network training is guided by a physiological model to avoid overfitting problems. During the training process, the parameters of the physiological model can be estimated simultaneously. To handle differential equations with impulse inputs and time delays, we propose approximate model equations for the pharmacokinetic (PK) model and the pharmacodynamic (PD) model, respectively. The modified PK/PD model was incorporated into PINN for training. Tests on simulated data and clinical data show that the proposed method has better performance than data‐driven modelling methods and the traditional physiological modelling based on the least squares method.
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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 | 0.000 |
| Open science | 0.000 | 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