Model Predictive Control for Renal Anemia Treatment through Physics-informed Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Patients with chronic kidney disease suffer from renal anemia due to inadequate erythropoietin (EPO) secretion. Determining the optimal EPO dosage and frequency is complex and requires decision-support technologies. Model predictive control (MPC) is an effective decision-making technique that requires a prediction model of the controlled process. In this work, it was discovered that Physics-Informed Neural Networks for Control (PINNC), which integrates physiological model with data-driven methodology, were capable of predicting the patient hemoglobin level with good accuracy and computational efficiency. Based on this prediction model, we developed a zone MPC framework to optimize the dosing strategy. Simulation results show that the proposed control method can serve as an effective tool for determining the optimal EPO dosages for renal anemia patients.
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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