Bibliographic record
Abstract
Chemotherapy of acute leukemia is important in ensuring long-term positive outcomes, yet standard dosing protocols use reactive adjustments based on infrequent blood tests and do not address high inter-patient variability. This thesis develops and analyzes a series of model-based control strategies to personalize chemotherapy and maintain the Absolute Neutrophil Count (ANC) near a target concentration. The thesis begins with an analysis of the Friberg model of myelosuppression, and explicit stability regions are derived. State and output feedback controllers are designed for the Friberg model, establishing local asymptotic stability guarantees through Lyapunov and frequency-domain analysis. To address the clinical challenges of unknown parameters, infrequent measurements, and dosing constraints, the thesis culminates in an Adaptive Model Predictive Control (AMPC) framework. This practical approach integrates a joint Unscented Kalman Filter (UKF) for online state and parameter estimation with a predictive controller based on the comprehensive Jost pharmacokinetic/pharmacodynamic (PK/PD) model. Simulations using a population of 116 virtual patients are used to validate each controller, and suggest that a more conservative ANC target can enable safe regulation with lower risk of neutropenia and lower drug exposure. Overall, this work presents a progression from theoretical to practical solutions, highlighting the potential of advanced control strategies to improve the safety and effectiveness of leukemia treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.005 | 0.001 |
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".