Optimization of Warfarin Dosing Using Machine Learning: A Literature Review
Bibliographic record
Abstract
Warfarin is an oral anticoagulant medication used to prevent conditions caused by blood clots.Due to the broad spectrum of variability among patients and the limited therapeutic margin, determining the appropriate dose for each individual is often difficult.To remediate the potentially fatal consequences that incorrect dosing can bring (such as thromboembolism or hemorrhaging), machine learning (ML) algorithms are used to enhance the accuracy of doses using clinical and pharmacogenetic variables.Researchers continue to build on previously published works to develop further methods for improving accuracy.Despite the advantages that ML techniques can bring, there are certain limitations when evaluating their performance in real clinical settings.For instance, warfarin research lacks diversity in demographics because most current papers only deal with the Caucasian, African/African American, and Asian populations.Those who don't fall under these specific categories are labeled as having a "missing" or "mixed" ethnicity, which may not accurately represent their ethnic demographic.Incorrect identification of patient information leads to inaccurate dose predictions, which can have detrimental consequences.This results in a disparity in the accuracy of medical treatment, a prevalent problem in all aspects of medicine.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| 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".