Dosing of Warfarin in Iranian Adult Patients with An Artificial Heart Valve Using Artificial Neural Networks
Bibliographic record
Abstract
Background and Aim: Artificial intelligence is a branch of computer science that has the ability of analyzing complex medical data. Using artificial intelligence is common in diagnosing, treating and taking care of patients. Warfarin is one of the most commonly prescribed oral anticoagulants. Determining the exact dose of warfarin needed for patients is one of the major challenges in the health system, which has attracted the attention of researchers. The purpose of this study was to determine the exact dose of warfarin needed for patients with artificial heart valves using artificial neural networks (ANN). Materials and Methods: A total of 9 multi-layer perceptron ANNs with different structures were constructed and evaluated based on a dataset including 846 patients who had referred to the PT clinic in Tehran Heart Center in the second half of the year 2013. Finally, the best structure of ANN for warfarin dose was investigated. All simulations including data preprocessing and neural network designing were done in MATLAB environment. Results: The effectiveness of ANNs was evaluated in terms of classification performance using 10-fold cross-validation procedure and the results showed that the best model was a network that had 7 neurons in its hidden layer with an average absolute error of 0.1, turbulence rate of 0.33, and regression of 0.87. Conclusion: The achieved results reveal that ANNs are able to predict warfarin dose in Iranian patients with an artificial heart valve. Although no system can be guaranteed to achieve 100% accuracy, they can be effective in reducing medical errors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".