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机器学习的应用在e-CHIRPP系统中自动编码伤害数据:开发和评估研究

Shamir N Mukhi1, Steven R McFaull2, Wendy Thompson2

  • 1Canadian Network for Public Health Intelligence, Public Health Agency of Canada, 9700 Jasper Ave, Edmonton, AB, T5J 4C3, Canada, 1-204-771-4698.

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机器学习准确地自动编码患者叙述中的伤害数据,改善公共卫生监测的及时性并减少行政负担. 这增强了加拿大医院伤害报告和预防计划 (CHIRPP) 系统.

关键词:
自动编码自动编码.信息学是一个信息学领域.伤害伤害伤害伤害伤害伤害伤害伤害伤害机器学习是机器学习.这是一种中毒,中毒.公共卫生公共卫生.监控监督监督监督监督监督监督监督监督监督监督监督监督监督监督监督监督监督

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科学领域:

  • 公共卫生信息学 公共卫生信息学
  • 机器学习应用 机器学习应用
  • 伤害监控系统 伤害监控系统

背景情况:

  • 加拿大医院伤害报告和预防计划 (CHIRPP) 是加拿大的关键伤害监测系统,自1990年以来收集了超过400万份记录.
  • 在e-CHIRPP系统中手动编码伤害数据是行政繁重的,并导致报告的重大延迟.

研究的目的:

  • 实施机器学习以基于患者叙述的伤害数据自动编码.
  • 提高监测结果的及时性,提高电子CHIRPP系统内的适应性.

主要方法:

  • 评估了机器学习算法,用于从e-CHIRPP系统中提取的伤害数据的分类和自动编码.
  • 选择的算法在2年和7年数据集上进行了评估,并对不准确性进行了调查,以改进过程.

主要成果:

  • 对于大多数伤害变量,自动编码与手动编码相比显示出高准确度.
  • 确定不准确的来源为持续的流程改进和改进提供了洞察力.

结论:

  • 基于机器学习的自动编码为公共卫生监测提供了重大潜力.
  • 优势包括近实时智能,减少行政工作量和增强系统适应性.