用临床记录预测抗生素耐药性，准确率达86%
Predicting Antibiotic Resistance Patterns Using Sentence-BERT: A Machine Learning Approach
- 用Sentence-BERT将病历文本转为向量，输入机器学习模型
- XGBoost模型平均F1得分0.86，优于神经网络的0.84
- 为临床合理使用抗生素提供新工具，适合医疗AI研究者
抗生素耐药性在住院患者中构成重大威胁，致死率高。本研究基于MIMIC-III数据集，从临床病历中生成Sentence-BERT嵌入表示，并应用神经网络与XGBoost模型预测抗生素敏感性。XGBoost平均F1得分为0.86，神经网络得分为0.84。这是首批利用文档嵌入预测抗生素耐药性的研究之一，为改善抗菌药物管理提供了新路径。
原文摘要 · Abstract (English)
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.
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