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Main Authors: Alwakeel, Mahmoud, Yarrington, Michael E., Wrenn, Rebekah H., Fang, Ethan, Pei, Jian, Chowdhury, Anand, Wong, An-Kwok Ian
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2509.14283
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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