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MAPRS:基于多标签学习的药物处方后审查的智能方法
Guangfei Yang1, Ziyao Zhou2, Aili Ding3
1Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital), Dalian 116033, China; Institute of Systems Engineering, Dalian University of Technology, Dalian 116024, China.
Artificial intelligence in medicine
|September 12, 2024
概括
抗菌素耐药性 (AMR) 是一个全球性的健康威胁. 一个新的多标签抗微生物药物处方后审查系统 (MAPRS) 使用NLP和机器学习来识别不适当的处方并解释其原因,改善抗微生物药物的管理.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 抗菌素耐药性 (AMR) 构成了全球健康的重大风险.
- 处方后审查 (PPR) 对抗微生物药物管理至关重要,但通常使用简化的二进制分类.
- 现有的PPR方法缺乏解释不适当的抗微生物使用,阻碍了有针对性的改进.
研究的目的:
- 开发一个智能系统来审查抗微生物药物处方.
- 为了解决处方分类的多标签性质.
- 识别和解释不适当的抗菌素处方背后的原因.
主要方法:
- 收集了来自清洁手术患者的抗菌素处方和临床数据.
- 开发了一种使用自然语言处理 (NLP) 的多标签抗菌药物处方后审查系统 (MAPRS).
- 使用的分类器链与机器学习融合,用于多标签分类和SHAP用于解释性.
主要成果:
- 在一个六类多标签任务中,MAPRS取得了高绩效.
- 显示了90.7%的子集精度和94.3%的平均AUROC.
- 成功解释了不适当的抗菌素处方的原因.
结论:
- MAPRS为智能抗菌素处方审查提供了一个有效的解决方案.
- 该系统通过识别和解释处方问题来增强抗菌药物管理.
- 这种方法可以帮助医院改善抗微生物药物的使用和打击AMR.
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