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人工智能预测医疗保健工作者使用抗生素的意图从多个理论的心理和行为措施跨越多个理论

Le Han¹, Peng Xian², Ying Liu¹

¹Department of Hospital Infection Control and Disease Prevention, Beijing Tongren Hospital, Beijing, China.

Scientific reports

|January 28, 2026

概括

了解医疗保健工作者的抗生素处方行为是打击抗菌素耐药性 (AMR) 的关键. 社会支持和认知处理显著影响适当的抗生素使用,指导量身定制的干预措施.

科学领域:

行为科学是一种行为科学.
医疗信息学医疗信息学
机器学习是机器学习.

背景情况:

抗菌素耐药性 (AMR) 是全球卫生危机,因不适当的抗生素处方而加剧.
目前的干预措施往往未能解决影响临床医生的行为的心理和上下文因素.

研究的目的:

在医疗保健工作者中确定适当的抗微生物药物处方意向的心理预测因素.
将行为理论与可解释的机器学习相结合,以增强理解.

主要方法:

在中国的1135名医疗保健工作者中进行了横截面调查.
利用来自多种行为理论 (TPB,HBM,TRA,自我效能,社会支持) 的构造.
应用了 LASSO 回归,SHAP 分析和机器学习分类器 (XGBoost,LightGBM,CatBoost).

主要成果:

社会支持,认知处理,知识,技能和健康信念成为关键预测因素.
SHAP分析强调了显著的非线性相互作用,特别是在社会支持和认知处理之间.
组合模型在预测高/中意图方面表现出高准确度 (F1 > 0.94),预测低意图方面存在挑战.

结论:

将行为理论与可解释的AI结合起来,提供了一种可扩展的方法来识别临床医生面临非指南一致处方风险.
这些发现支持开发针对性,实时的抗生素管理干预措施.
解决心理因素对于加强抗生素管理和打击AMR至关重要.

关键词:

抗生素管理的管理.适当使用抗微生物药物.行为意图是一种行为意图.可解释的机器学习与指导方针一致的处方方法医疗保健工作者医疗保健工作者拉索回归法 (Lasso Regression) 是一种回归法.在SHAP分析中,我们分析了SHAP.

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