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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

753
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
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相关实验视频

Updated: May 15, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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基于Web的可解释的基于机器学习的药物监测,用于预测苏尼提尼布和索拉费尼布相关的甲状腺功能障碍:模型开发和验证研究.

Fan-Ying Chan1, Yi-En Ku1, Wen-Nung Lie2

  • 1Department of Clinical Pharmacy, College of Pharmacy, Taipei Medical University, 250 Wuxing St, Xinyi Dist, Taipei, 11031, Taiwan, 886 2-2736-1661.

JMIR formative research
|April 10, 2025
PubMed
概括

机器学习模型使用时间序列数据从癌症药物sunitinib和sorafenib中预测甲状腺功能障碍. 这种可解释的系统有助于早期的药物不良反应监测.

关键词:
这里是TKI TKI.癌症 癌症 癌症 癌症 癌症机器学习是机器学习.索拉芬尼布 (Sorafenib) 是一个苏尼提尼布 (sunitinib) 的使用方法甲状腺功能障碍 甲状腺功能障碍氨酸激酶抑制剂 氨酸激酶抑制剂

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科学领域:

  • 在瘤学瘤学.
  • 药物监督 药物监督 药物监督
  • 数据科学数据科学数据科学

背景情况:

  • 识别高风险患者的传统方法有限.
  • 使用时间序列数据的机器学习模型为癌症患者的不良事件提供了预测能力.
  • 及时管理癌症治疗副作用至关重要.

研究的目的:

  • 开发和验证用于预测与sunitinib和sorafenib相关的甲状腺功能障碍的机器学习模型.
  • 采用时间序列数据收集方法来提高预测准确度.
  • 为了确定药物诱导的甲状腺功能障碍的关键预测因素.

主要方法:

  • 从接受sunitinib或sorafenib治疗的患者收集的时间序列数据.
  • 开发了使用后勤回归,随机森林,自适应增强,光梯度增强机器和梯度增强决策树的预测模型.
  • 评估模型性能使用准确度,精度,回忆,F1得分,AUC-ROC和AUC-PR.
  • 使用夏普利添加式解释 (SHAP) 进行特征重要性分析.

主要成果:

  • 渐变增强决策树模型表现出卓越的性能.
  • 最好的模型实现了AUC-PR的0.600和AUC-ROC的0.876.
  • 发现的关键预测因素包括胆固醇升高,长时间服用药物,以及清细胞腺癌组织学.
  • 该模型被集成到基于Web的应用程序中,以便在实践中使用.

结论:

  • 开发了一个可解释的药物不良反应监测系统.
  • 该模型有效地预测了与sunitinib和sorafenib相关的甲状腺功能障碍.
  • 该工具支持主动的患者管理,并加强药物安全监测.