Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach
This addresses antibiotic resistance in in-patient settings, offering a novel pathway for antimicrobial stewardship, though it is incremental as it applies existing methods to a new domain.
The study tackled predicting antibiotic susceptibility from clinical notes using Sentence-BERT embeddings, achieving an average F1 score of 0.86 with XGBoost and 0.84 with Neural Networks.
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.