An Adaptive Neuro-Fuzzy Inference System Approach to Neutrophil Prediction in Childhood Leukaemia
Bibliographic record
Abstract
Acute Lymphoblastic Leukaemia (ALL) is the most common form of cancer in childhood.Chemotherapy treatment for ALL involves three phases viz.induction, intensification and maintenance.The maintenance phase includes daily intake of the drug 6-Mercaptopurine that helps kill any remaining abnormal cells and prevent relapse.A well-known side effect of the drug, however, is a reduction in neutrophils, a type of white blood cell, leading to an increased risk of secondary infection, periods of hospitalisation and time off treatment.Blood counts are monitored on a weekly basis and drug dosages altered in an attempt to minimise this risk.However, there is currently no intelligent method of determining optimum dosages for individual patients and typically neutrophil counts will drop below an acceptable level every 6-8 week.This paper proposes a seven-day ahead neutrophil count prediction methodology based on a Takagu-Sugeno model and an Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to support dosing decisions.The methodology is applied to data for the maintenance phase from one female patient, where it is shown to accurately predict neutrophil counts seven-days ahead with a mean squared error of 0.25.This is not only significant in terms of improving the outcomes for ALL patients but also has the potential to be applied to the treatment of other forms of cancer and other diseases where personalised dosing is important, e.g. in Schizophrenia treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".