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在精神卫生保健中使用电子健康记录数据开发自杀风险预测算法:现实世界案例研究

Linda Hummel1,2, Karin C A G Lorenz-Artz1,2, Joyce J P A Bierbooms1

  • 1Tranzo Scientific Center for Care and Wellbeing, Tilburg School of Social and Behavioral Sciences, Tilburg University, Prof. Cobbenhagenlaan 125, Reitse Poort, Room RP 204, Tilburg, 5037 DB, The Netherlands, 31683662495.

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概括

为心理健康护理开发人工智能 (AI) 面临着数据和实施方面的挑战. 解决这些问题需要整合临床和技术观点,以获得有效的数据驱动精神卫生服务.

关键词:
人工智能的人工智能是人工智能.电子健康记录是电子健康记录.实施科学 实施科学心理健康服务 心理健康服务预测算法 预测算法自杀预防 自杀预防

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科学领域:

  • 心理健康技术 心理健康技术
  • 医疗保健中的人工智能
  • 临床信息学 临床信息学

背景情况:

  • 人工智能 (AI) 为紧张的心理健康系统提供了解决方案,但在临床实践中实施的AI工具很少.
  • 算法开发阶段对于将创新与实际应用联系起来至关重要,影响未来的实施成功.
  • 人工智能开发必须考虑设计选择如何影响临床采用和可用性.

研究的目的:

  • 通过使用电子健康记录 (EHR) 数据,检查自杀风险预测算法的开发过程.
  • 识别算法开发过程中遇到的挑战以及用于解决这些问题的策略.
  • 在AI中整合技术和临床视角以实现数据驱动精神卫生保健的关键考虑.

主要方法:

  • 一个探索性的,多方法的定性案例研究.
  • 通过办公桌研究,团队会议和反会议收集数据.
  • 专题分析以确定发展挑战和应对措施,为未来的算法开发提供信息.

主要成果:

  • 挑战包括在EHR中定义因数据质量问题而导致的自杀事件,以及操作化心理社会变量.
  • 非结构化数据的自然语言处理使情绪分析成为可能,但模型的复杂性影响了可解释性.
  • 偏差风险来自不平等分布的问卷数据,需要仔细的输入选择.

结论:

  • 推进数据驱动的心理健康护理需要强大的数据治理,质量控制和文档的文化转变.
  • 缓解偏见,平衡预测准确性与可解释性,并保持临床医生在循环中的方法至关重要.
  • 未来的研究应该专注于人工智能开发,实施和在心理健康护理中使用的社会技术方面.