基于机器学习的抗生素组合预测多中心临床数据和药物相互作用相关性
Jia'an Qin1, Yuhe Yang2, Chao Ai3
1Beijing Institute of Clinical Pharmacy, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
International journal of antimicrobial agents
|March 2, 2024
概括
这项研究开发了一个机器学习模型来预测可行的抗生素组合,旨在减少药物相互作用风险. 抗生素组合推模型 (ACRM) 支持更安全的抗生素处方实践.
科学领域:
- 药理学 药理学是指药理学的学科.
- 传染性疾病 传染性疾病
- 计算生物学 计算生物学
背景情况:
- 抗生素耐药性的增加需要有效的组合疗法.
- 目前的抗生素组合处方缺乏快速可行性评估和对药物相互作用风险的明确理解.
研究的目的:
- 开发一种机器学习模型,用于预测可行的抗生素组合.
- 评估抗生素组合和药物相互作用之间的相关性.
- 支持更安全的抗生素使用,提高药物安全性.
主要方法:
- 对16101个抗生素联合处方 (2015-2023) 的统计分析.
- 使用前神经网络 (FNN) 开发抗生素组合推模型 (ACRM).
- 连续方法和药物银行用于药物相互作用分析的整合.
主要成果:
- 该ACRM是使用55种抗生素和657种组合构建的,在回顾性队列中实现了61.54-73.33%的预测准确度.
- 对于各种推类,AUROCs在0.589-0.895之间.
- 在推组合和药物相互作用风险之间发现了正相关性 (29.2%强烈推与43.5%不推).
结论:
- 机器学习有效地模拟了回顾性抗生素处方数据,用于组合建议.
- 该ACRM有助于减少药物相互作用,增强抗生素管理.
- 采用此类系统可以改善临床决策和药物安全.
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