Predicting Drug Shortages for Healthcare Supply Chain Optimization Using Machine Learning
Bibliographic record
Abstract
The Canadian health care system is vulnerable to drug shortages. Which influence patient care, inflate costs, and disrupt treatments. This paper proposes a prediction machine learning model to identify drug shortages using 572 shortage reports and 20 discontinuation reports notices sourced from the Drug Shortages Canada portal. A novel Therapeutic Risk Score assess drugs from the clinical importance, probability of shortage, and treatment consequences.Different models tested include Random Forest, Logistic Regression, Gradient Boosting, and Support vector Machine (SVM); the best performance was from the random forest model with the highest accuracy. The system categorizes the drugs into high priority, moderate buffer, and standard stocking, with accompanying inventory recommendations. Predictive analytics combined with a cost estimation model determines Expected cost = Shortage probability * Reimbursement rate) allows for proactive supply chain management. This adaptive tool will better prepare the hopstai;s and pharmacies ahead of time for access to vital drugs and resilience in health care system
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".