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开发一种基于机器学习的预测模型,用于癌症患者通过皮肤服用芬太尼的止痛效果:一种可解释的方法

Xiaogang Hu¹, Ya Chen², Yuelu Tang³

¹Department of Pharmacy, Chongqing Jiulongpo People's Hospital, Chongqing, China.

International journal of clinical pharmacy

|March 17, 2025

概括

预测透皮芬太尼在癌症疼痛中的有效性至关重要. 随机森林模型准确地识别了可能经历不足的疼痛缓解的患者,帮助优化疼痛管理策略.

科学领域:

在瘤学瘤学.
疼痛管理疼痛管理
药理学药理学是指药理学的学科.

背景情况:

与癌症相关的疼痛显著影响患者的生活质量.
透皮芬太尼是特定患者群体的方便阿片类药物.
预测镇痛效果是优化癌症疼痛管理的关键.

研究的目的:

在癌症患者中开发一种透皮芬太尼疗效的预测模型.
确定影响透皮芬太尼疗效的关键因素.

主要方法:

分析了151名成人癌症疼痛患者的临床数据.
9个预测模型的开发和比较 (逻辑回归,随机森林,极端梯度提升).
模型性能使用ROC曲线,尤登指数,布里尔得分,交叉验证和SHAP分析进行评估.

主要成果:

27.2%的患者报告透皮芬太尼无效.
随机森林模型8获得了最高的预测性能 (ROC-AUC:0.984).
不有效性的关键预测因素包括NRS,透皮芬太尼剂量,BMI和ALT.

结论:

一个随机森林模型有效地预测了癌症疼痛中的透皮芬太尼的有效性.
这个模型支持精细的疼痛评估和管理策略.
已识别的预测因素可以指导个性化治疗方法.

关键词:

癌症疼痛癌症疼痛预测模型的预测模型.预测因素是一个预测因素.风险评估风险评估风险评估通过皮肤注射的芬太尼.

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