Researchxgboostantimicrobial resistanceragphi 3 mini
XGBoost Achieves Accurate AMR Trend Forecasts
8.1
Researchers present a two-component framework (Feb 26, 2026) to forecast antimicrobial resistance using WHO GLASS data from 2021–2023, benchmarking six models across 5,909 observations and six WHO regions. XGBoost performed best (test MAE 7.07%, R² 0.854), prior-year resistance dominated feature importance (50.5%), regional MAE ranged 4.16%–10.14%, and a RAG pipeline with ChromaDB and Phi-3 Mini supports source-attributed policy answers.
Key Points
- 1Benchmarked models show XGBoost best with 7.07% MAE and R² 0.854
- 2Identified prior-year resistance as dominant predictor (50.5% importance), indicating temporal autocorrelation
- 3Implemented RAG with ChromaDB and Phi-3 Mini to produce source-attributed policy guidance for decision-makers
Scoring Rationale
Practical, region-wide benchmarking and released code drive impact, limited by single-source arXiv preprint lacking peer review.
Sources
Public references used for this report.
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