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A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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利用可解释的人工智能来优化临床决策支持.

Siru Liu1,2, Allison B McCoy1, Josh F Peterson1,3

  • 1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.

Journal of the American Medical Informatics Association : JAMIA
|February 21, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一种基于数据的方法,使用可解释的人工智能 (XAI) 来增强临床决策支持 (CDS) 预警标准. 该方法成功地确定了改善警报和发现工作流的问题的有用建议.

关键词:
临床决策支持 临床决策支持电子健康记录 电子健康记录可解释的人工智能

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科学领域:

  • 临床信息学 临床信息学
  • 医疗保健中的人工智能
  • 机器学习用于医疗决策支持

背景情况:

  • 临床决策支持 (CDS) 警报对于患者护理至关重要,但往往需要优化.
  • 手动审查警报标准可能耗时,可能会错过潜在的改进.
  • 可解释的人工智能 (XAI) 为理解和完善复杂系统提供了新的方法.

研究的目的:

  • 开发和评估一个数据驱动的过程,以产生可行的建议,以改善CDS警报标准.
  • 利用可解释的人工智能 (XAI) 技术来识别最佳警报参数.
  • 评估XAI产生的建议与历史数据和专家临床判断的有用性.

主要方法:

  • 从范德比尔特大学医学中心 (2019-2020) 提取了警报数据.
  • 开发机器学习模型来预测用户对警报的反应.
  • 应用了XAI技术来解释警报行为的全球和本地解释.
  • 通过与警报变更日志进行比较并对利益相关者进行采访来评估建议.

主要成果:

  • 使用了2,991,823个警报发射的数据集,其中有2689个特征.
  • 轻GBM模型实现了0.919的ROC曲线下的高面积.
  • 确定了96个有用的建议,以改善警报标准.
  • 确定9.3%的警报发射可能会被消除.
  • 未发现的工作流程和与教育相关的问题影响了警报的接受.

结论:

  • 使用XAI的数据驱动过程有效地产生了改善CDS警报标准的建议.
  • 这种方法可以识别手动审查可能忽略或延迟的改进.
  • XAI具有双重目的:优化警报标准和发现与工作流程,教育或人员配置相关的质量问题.