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Introduction to Language of Pathophysiology ll01:17

Introduction to Language of Pathophysiology ll

This lesson explores key terms that describe how diseases progress, their outcomes, and their distribution in populations.Diagnostic tests identify diseases and monitor treatment. These include blood and urine tests, biopsies, imaging (X-ray, MRI), and detection of infectious agents.Remission is a reduction or disappearance of symptoms.Exacerbation refers to the worsening of symptoms, such as increased wheezing during an asthma attack.A precipitating factor triggers an acute episode, while a...

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相关实验视频

Updated: Jun 10, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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基于机器学习的自然语言处理,从临床笔记中提取PD-L1表达水平.

Eric Lin1,2, Robert Zwolinski1, Julie Tsu-Yu Wu3,4

  • 1VA Boston Healthcare System, Boston, MA, USA.

Health informatics journal
|August 28, 2023
PubMed
概括
此摘要是机器生成的。

一个新的自然语言处理 (NLP) 工具准确地从临床笔记中提取编程的死亡连接物1 (PD-L1) 表达式. 这种方法通过自动化从电子健康记录中提取数据来促进大规模的癌症免疫治疗研究.

关键词:
PD-l1 一个癌症 癌症 癌症 癌症 癌症电子健康记录是电子健康记录.机器学习是机器学习.自然语言处理自然语言处理.

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科学领域:

  • 在瘤学瘤学.
  • 生物医学信息学 生物医学信息学
  • 自然语言处理自然语言处理.

背景情况:

  • 编程死亡配体1 (PD-L1) 表达对于预测患者对癌症免疫疗法的反应至关重要.
  • PD-L1状态通常被埋在非结构化的临床笔记中,阻碍其在大规模研究中的使用.
  • 自动化PD-L1提取对于推进癌症免疫治疗研究至关重要.

研究的目的:

  • 开发和评估基于机器学习的自然语言处理 (NLP) 工具.
  • 从退伍军人事务电子健康记录系统中的非结构化临床笔记中提取PD-L1表达值.
  • 为了使PD-L1状态在癌症免疫疗法研究中能够进行人口层面的分析.

主要方法:

  • 开发用于NLP的机器学习模型.
  • 使用退伍军人事务电子健康记录系统数据进行培训和验证.
  • 评估模型在提取PD-L1表达值方面的性能.

主要成果:

  • 该NLP工具在各种细分级别中表现出高性能.
  • 对PD-L1阳性的平均精度为0.859,回忆为0.994,F1得分为0.921.
  • 对于数值PD-L1值,平均绝对误差为0.537 (尺度0-100).

结论:

  • 开发了一种准确的NLP方法,从临床笔记中推导PD-L1状态.
  • 这种工具大大减少了医疗记录审查所需的手工工作.
  • 该方法将促进未来在癌症免疫治疗中进行人口水平研究.