# Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach

arXiv: https://arxiv.org/abs/2509.14283 · HTML: https://mlsanity.com/paper/2509.14283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Abstract (arXiv)

Antibiotic resistance poses a significant threat in in-patient settings with high mortality. Using MIMIC-III data, we generated Sentence-BERT embeddings from clinical notes and applied Neural Networks and XGBoost to predict antibiotic susceptibility. XGBoost achieved an average F1 score of 0.86, while Neural Networks scored 0.84. This study is among the first to use document embeddings for predicting antibiotic resistance, offering a novel pathway for improving antimicrobial stewardship.
