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Methods of Documentation VII: EMR01:30

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Health literacy is an individual's or a community's capacity to comprehend, receive, read, and use relevant healthcare information and services. The World Health Organization (WHO, 2018) defines health literacy as the cognitive and social skills that determine the ability of individuals to gain access to, understand, and use information in ways that promote and maintain good health. As a result, the WHO helps individuals manage long-term health concerns, participate in preventative...
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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通过上下文学习有效检测电子健康记录中的污名化语言:比较分析和验证研究

Hongbo Chen1, Myrtede Alfred1, Eldan Cohen1

  • 1Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.

JMIR medical informatics
|August 18, 2025
PubMed
概括

在语境学习 (ICL) 有效地检测到电子健康记录 (EHR) 中的污名化语言,数据较少. 与传统方法相比,这种数据效率高的方法也显示出更好的公平性.

关键词:
人工智能的人工智能是人工智能.电子健康记录 电子健康记录公平的公平的公平.几次射击,几次射击.在上下文学习学习.大型语言模型机器学习是机器学习.促使战略促使战略.污名化的语言.文字分类 文本分类 文本分类没有射击的零射击.

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科学领域:

  • 自然语言处理 (NLP) 是一种自然语言处理.
  • 医疗保健中的机器学习
  • 临床信息学 临床信息学

背景情况:

  • 电子健康记录 (EHR) 中的污名化语言延续了偏见,并危及了患者的护理.
  • 监督的机器学习模型用于耻辱检测需要广泛的注释数据集.
  • 在上下文学习 (ICL) 提供了使用指令和示例的数据效率替代方案.

研究的目的:

  • 评估ICL在电子健康记录中检测污名化语言的有效性.
  • 在数据稀缺条件下评估ICL性能.
  • 将ICL与传统的机器学习方法在效率和公平性方面进行比较.

主要方法:

  • 从密集护理-IV (MIMIC-IV) 数据集中的医疗信息中心分析了5043个句子.
  • 对比ICL (零射击,少数射击与4个提示策略) 与零射击文本包含和少数射击SetFit模型.
  • 使用跨受保护属性 (性别,年龄,种族) 的平等绩效标准评估模型公平性.

主要成果:

  • 像GEMMA-2 (零射击) 和LLAMA-3 (几射击) 这样的ICL模型在检测污名化语言方面明显优于文本包容和SetFit模型.
  • 最好的ICL模型在仅有32个标记实例的情况下获得了0.901的F1得分,与在数千个实例上训练的监督微调模型密切竞争.
  • 与监督微调模型相比,ICL模型表现出更高的公平性,在受保护属性中表现出更少的偏差.

结论:

  • ICL提供了一种灵活而稳健的方法,用于识别电子健康文件中的污名化语言.
  • ICL是一个比传统的监督机器学习更有效,更公平的数据替代方案.
  • ICL可以改善临床文档中的偏差检测,同时最大限度地减少对大型标记数据集的需求.