Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916955469905920 |
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| author | Alwakeel, Mahmoud Yarrington, Michael E. Wrenn, Rebekah H. Fang, Ethan Pei, Jian Chowdhury, Anand Wong, An-Kwok Ian |
| author_facet | Alwakeel, Mahmoud Yarrington, Michael E. Wrenn, Rebekah H. Fang, Ethan Pei, Jian Chowdhury, Anand Wong, An-Kwok Ian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach Alwakeel, Mahmoud Yarrington, Michael E. Wrenn, Rebekah H. Fang, Ethan Pei, Jian Chowdhury, Anand Wong, An-Kwok Ian Computation and Language 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. |
| title | Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.14283 |