使用机器学习对皮佩拉西林-塔扎巴克坦治疗下呼吸道感染的治疗反应的可解释预测
Zhijing Zhu1, Tao Yang2, Kun Han3
1School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai, China.
Medicine
|August 5, 2025
概括
一个机器学习模型有效地预测了严重肺炎的皮佩拉西林/塔扎巴克坦 (PIPT) 治疗结果. 它可以识别高风险患者,个性化护理,减少不必要的抗生素使用.
科学领域:
- 传染性疾病 传染性疾病
- 医疗信息学 医疗信息学
- 药理学 药理学是指药理学的学科.
背景情况:
- 经验性piperacillin/tazobactam (PIPT) 是严重的社区获得性肺炎的标准.
- 过度使用PIPT有助于抗生素耐药性和不良影响.
- 预测PIPT的有效性对于优化患者护理至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,以预测PIPT在性下呼吸道感染中的有效性.
- 确定影响治疗结果的关键因素.
- 个性化抗生素治疗,减少不适当的使用.
主要方法:
- 用PIPT治疗的住院患者的回顾性分析.
- 使用后勤回归,随机森林和决策树算法开发一个ML模型.
- 通过最小绝对收缩和选择操作员回归来选择特征.
- 使用夏普利添加式解释 (SHAP) 的模型解释.
主要成果:
- 决策树模型实现了0.73 (95% CI 0.61-0.86) 的预测性能得分.
- SHAP分析确定了低血清白蛋白,降低PIPT剂量和并发症 (COPD,心力衰竭) 作为治疗失败的预测因素.
- 不良的中性粒细胞与淋巴细胞比率 (≥70%) 也与治疗失败有关.
结论:
- 开发的ML模型准确地预测了PIPT治疗结果.
- 该模型有助于识别高风险患者,以制定个性化治疗策略.
- 这种方法优化了患者的护理,并最大限度地减少了不适当的抗生素使用.
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