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相关概念视频

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整合结构化和非结构化数据来预测紧急情况的严重程度:使用基于变压器的自然语言处理模型进行关联和预测研究.

Xingyu Zhang1, Yanshan Wang2, Yun Jiang3

  • 1Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA. xiz261@pitt.edu.

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概括

结合结构化和非结构化患者数据,可显著提高急诊室 (ED) 选准确度. 使用机器学习的临床笔记可以提高严重性预测和患者的治疗结果.

关键词:
协会研究研究协会研究临床决策支持 临床决策支持应急部门的紧急情况部门.自然语言处理自然语言处理.预测建模的预测建模.非结构化数据是非结构化数据.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 临床决策支持 临床决策支持

背景情况:

  • 紧急部门 (ED) 的分拣对于及时的患者护理至关重要.
  • 传统的分组依赖于结构化数据,但非结构化的临床笔记为增强的预测建模提供了潜力.
  • 这项研究评估了结构化和非结构化数据的结合,以改善紧急情况的严重性预测.

研究的目的:

  • 评估整合结构化和非结构化数据的有效性,以预测紧急情况的严重程度.
  • 探索患者特征与紧急情况严重程度结果之间的关联.
  • 为了比较使用不同数据配置的机器学习模型的性能.

主要方法:

  • 利用了2021年国家医院门诊医疗调查 (NHAMCS) 对成年ED患者的数据.
  • 使用紧急严重程度指数对紧急情况的严重程度进行分类 (紧急:1-3,非紧急:4-5).
  • 使用双向编码器从变压器 (BERT) 模型处理非结构化数据 (首席投诉,访问原因);应用后勤回归,随机森林,梯度增强和极端梯度增强到结构化,非结构化和组合数据.

主要成果:

  • 该研究包括8,716名成年患者; 74.6%被归类为紧急.
  • 严重程度的重要预测因素包括老年,心率升高,慢性脏病和冠状动脉疾病.
  • 渐变增强与综合数据实现了最高的性能 (AUC: 0.789,准确率: 0.726,精度: 0.892).

结论:

  • 结合结构化和非结构化数据可以提高ED患者的紧急严重性预测.
  • 将文本数据集成到预测模型中,可以提供更准确的严重性评估,改善资源配置和患者的治疗结果.
  • 未来的研究应该集中在实时应用和验证在不同的临床环境.