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使用机器学习开发SSRI相关出血的临床预测模型:可行性研究

Jatin Goyal1, Ding Quan Ng2, Kevin Zhang1

  • 1Donald Bren School of Information and Computer Sciences, University of California Irvine, Irvine, CA, USA.

BMC medical informatics and decision making
|June 10, 2023
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测选择性血红素再吸收抑制剂 (SSRI) 药物的出血风险. 关键预测因素包括出血史和社会经济地位,有助于预防不良药物事件.

关键词:
药物不良事件是药物不良事件.流血 出血 流血 出血电子健康记录是电子健康记录.机器学习是机器学习.选择性血清素再吸收抑制剂 选择性血清素再吸收抑制剂

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科学领域:

  • 药物监督和机器学习
  • 临床信息学 临床信息学
  • 计算健康 计算健康

背景情况:

  • 药物不良事件 (ADEs) 导致患者的治疗结果差,医疗费用增加.
  • 对ADE的预测工具可以减轻风险并提高患者安全.
  • 选择性血红素再吸收抑制剂 (SSRI) 通常被处方,但相关的出血事件需要监测.

研究的目的:

  • 开发和评估机器学习 (ML) 模型,用于预测与SSRI使用相关的出血事件.
  • 确定导致SSRI相关出血的关键临床和人口特征.
  • 评估使用大规模电子健康记录 (EHR) 数据用于ADE预测的可行性.

主要方法:

  • 利用国家卫生研究院我们所有人 (AoU) 数据库,包括超过10,000名暴露于SSRI的参与者的EHR数据.
  • 选择了88个特征,包括社会人口统计,生活方式,并发病症和药物使用.
  • 应用后勤回归,决策树,随机森林和极端梯度提升模型来预测出血事件,通过AUC评估性能.

主要成果:

  • 在10362名SSRI暴露的参与者中,9.6%的参与者经历了出血事件.
  • 机器学习模型实现的AUC范围从0.632到0.698.8.
  • 临床上显著的预测因素包括健康素养 (对于埃斯基塔洛普拉姆) 和出血史和社会经济地位 (对于所有SSRI).

结论:

  • 证明了使用ML来预测SSRI相关出血事件的可行性.
  • 确定了患者的特定特征,这些特征是出血的重大风险因素.
  • 通过结合基因组数据和深度学习,提出了改善ADE预测的潜力.