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一个可互操作的机器学习管道用于儿童肥胖风险估计.

Hamed Fayyaz1, Mehak Gupta2, Alejandra Perez Ramirez3

  • 1University of Delaware, Newark, DE, USA.

Proceedings of machine learning research
|March 7, 2025
PubMed
概括

这项研究引入了使用电子健康记录预测儿科肥胖症的新管道. 该工具旨在通过提前1至3年识别有风险的儿童,使早期干预成为可能.

关键词:
临床决策支持 临床决策支持深度学习 (Deep Learning) 是一种深度学习.菲希尔 (FHIR) 是一个人.在这里,我们可以看到它.互操作性 互操作性 互操作性儿童肥胖症 儿童肥胖症预防 预防 预防主要的护理是初级保健.

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科学领域:

  • 儿科健康 儿科健康
  • 机器学习 机器学习
  • 医疗信息学 医疗信息学

背景情况:

  • 儿童肥胖是一个越来越严重的问题,需要早期干预.
  • 现有的用于肥胖预测的机器学习 (ML) 模型缺乏临床整合.
  • 需要为儿童肥胖症提供可访问,临床适用的预测工具.

研究的目的:

  • 开发和评估一种新的端到端管道,用于预测儿科肥胖风险.
  • 用快速医疗互操作性资源 (FHIR) 促进预测模型融入临床工作流程.
  • 评估模型的预测效率和利益相关者的调整.

主要方法:

  • 利用来自儿科电子健康记录 (EHR) 的例行记录数据.
  • 开发了一个端到端的管道,用于通过API/UI.数据提取,推断和通信.
  • 采用专家策划的医疗概念列表来预测风险.
  • 设计了与快速医疗互操作性资源 (FHIR) 的管道,以集成EHR.

主要成果:

  • 证明了儿童肥胖预测模型的有效性.
  • 展示了管道对早期风险识别 (1-3年) 的能力.
  • 确认与不同利益相关方 (临床医生,IT,患者等) 的反一致. ) 的情况.

结论:

  • 开发的管道为早期儿童肥胖预测提供了可行的解决方案.
  • 整合FHIR有助于在临床环境中更广泛地采用.
  • 该工具支持及时的预防性干预,解决关键的公共卫生需求.