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使用自然语言处理与基于区块的视觉编程接口的智能试验资格选工具:开发和可用性研究

Ya-Han Hu1,2, Yi-Ying Cheng1, Chung-Ching Lan1

  • 1Department of Information Management, National Central University, Taoyuan, Taiwan.

JMIR medical informatics
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PubMed
概括
此摘要是机器生成的。

智能试验资格查工具 (iTEST) 显著提高了临床试验查的准确性和效率. 该工具通过确保对试验进行正确的参与者选择来提高患者的安全性.

关键词:
基于区块的视觉编程.临床决策支持 临床决策支持临床试验是指临床试验中的临床试验.电子医疗记录 电子医疗记录资格选对资格进行选.自然语言处理自然语言处理.患者安全 患者安全

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科学领域:

  • 医疗信息学 医疗信息学
  • 临床试验管理 临床试验管理
  • 健康 数据科学 数据科学

背景情况:

  • 电子医疗记录 (EMR) 在临床试验资格查方面存在挑战,原因是数据复杂性和各种术语.
  • 手动查是低效的,需要专业知识,并可能导致不一致的参与者选择,影响患者安全和研究结果,特别是在急性缺血性中风等关键情况下.
  • 现有的计算机化工具通常需要软件工程专业知识进行更新,当资格标准发生变化时,这限制了它们的实际使用.

研究的目的:

  • 开发和评估智能试验资格选工具 (iTEST),该工具将自然语言处理与视觉编程接口集成在一起.
  • 使临床医生能够独立创建和修改资格查规则.
  • 评估iTEST规则评估模块与标准EMR接口的性能.

主要方法:

  • 在一所高等教学医院的12名临床医生进行了一项实验,该实验使用了2周期交叉设计.
  • 临床医生评估了中风患者在两项试验中的资格,使用标准EMR和iTEST.
  • iTEST使用Google Blockly创建规则,并使用MetaMap Lite从EMR数据中提取概念;结果包括准确性,任务完成时间,认知工作负载 (NASA-TLX) 和系统可用性 (SUS).

主要成果:

  • 与标准EMR相比,iTEST显著提高了准确性 (0.91至1.00,P<.001),并减少了完成时间 (3.18至2.44分钟,P=.004).
  • 用户报告说,使用iTEST的认知工作负载较低 (NASA-TLX: 39.7 vs 62.8,P=.02) 和系统可用性更高 (SUS: 71.3 vs 46.3,P=.01).
  • 在时间需求,努力和丧方面,认知工作量显著改善.

结论:

  • iTEST在临床试验资格查方面表现出卓越的表现,提高了准确性,效率和可用性.
  • 提高准确性对于患者安全至关重要,防止不适当的治疗或从有益的试验中排除.
  • 对结构化/非结构化数据的适应性和易于修改的iTEST使其对时间敏感的研究和不断发展的协议非常有价值.