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可解释的机器学习模型预测免疫检查点抑制剂诱导的甲状腺功能低下症:一个回顾性队列研究
Su-Yan Zhu1, Tong-Tong Yang1, Yi-Zhuo Zhao2
1Department of Pharmacy, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, China.
Cancer science
|September 24, 2024
概括
机器学习可以预测甲状腺功能低下症,这是癌症治疗中免疫检查点抑制剂 (ICI) 的副作用. 使用甲状腺刺激激素 (TSH) 的XGBoost模型在识别有风险的患者方面显示出最高的准确性.
科学领域:
- 在瘤学瘤学.
- 内分泌学 在内分泌学.
- 人工智能的人工智能
背景情况:
- 免疫检查点抑制剂 (ICI) 在癌症治疗中至关重要,但可能导致甲状腺功能低下.
- 早期预测这种不良事件对于患者管理至关重要.
研究的目的:
- 开发一种可解释的机器学习 (ML) 模型,用于预测接受ICI的患者的甲状腺功能低下症.
- 为了确定ICI诱导的甲状腺功能低下症的关键预测因素.
主要方法:
- 458名接受ICI治疗的患者进行了回顾性队列研究.
- 应用后勤回归,随机森林,SVM和XGBoost模型.
- 使用SHAP进行模型解释性和AUC进行评估.
主要成果:
- 甲状腺功能低下症发生在12.88%的患者中.
- XGBoost实现了最高的预测性能,其AUC为0.833.
- 甲状腺刺激激素 (TSH) 被确定为最重要的预测因子.
结论:
- 一个可解释的ML模型,特别是XGBoost,可以有效预测ICI诱导的甲状腺功能低下症.
- 这种方法支持针对癌症患者的个性化风险管理和治疗策略.
- 在ICI治疗期间,ML为主动监测内分泌不良事件提供了一个有前途的工具.
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