通过使用机器学习来确定万科米辛药物剂量的稳定状态低谷范围
M Samie Tootooni1, Erin F Barreto2, Phichet Wutthisirisart3
1Department of Health Informatics and Data Science, Loyola University Chicago, Maywood, IL, United States of America.
Journal of critical care
|March 19, 2024
概括
机器学习模型预测了在重症监护病房 (ICU) 中的万科米辛剂量风险. 这些模型识别了治疗次和治疗次的万科米辛最低水平,提高了重症患者药物剂量准确度.
科学领域:
- 药理学 药理学是指药理学的学科.
- 临床药房 临床药房
- 机器学习在医学中的应用
背景情况:
- 范科米辛是ICU中关键的抗生素,具有狭窄的治疗窗口,是毒的.
- 达到治疗性万科米辛的最低水平对于疗效和安全至关重要.
- 预测和管理万科米辛剂量对于重症患者至关重要.
研究的目的:
- 开发和比较机器学习模型,以预测万科米辛的最低水平.
- 为了评估治疗次和治疗次范科米辛水平的风险.
- 为了提高在重症监护室 (ICU) 患者的万科米辛剂量的准确性.
主要方法:
- 从成年ICU患者中使用了5337个万科米辛疗程的队列 (2007-2017年).
- 训练并比较了各种分类模型,使用分类的万科米辛最低水平 (次治疗,治疗,超治疗).
- 使用AUC-ROC,特异性和灵敏度评估模型性能.
主要成果:
- 与其他机器学习方法相比,XGBoost模型表现出更高的性能.
- 实现的AUC-ROC分别为0.85和0.83的治疗以下和治疗以上水平.
- 关键预测因素包括动力估计的膜过率,万科米辛治疗方案,并发症,BMI,年龄,性别和血压.
结论:
- 开发了预测模型,以识别不适当的米低谷水平的风险.
- 这些模型有助于优化ICU患者的万科米辛剂量策略.
- 提高药物剂量的准确性可以改善重症监护机构患者的治疗结果.
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