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在意大利,从未编码的急诊室入院记录进行儿科伤害监测:基于机器学习的文本挖掘方法

Danila Azzolina1, Silvia Bressan2, Giulia Lorenzoni3

  • 1Department of Environmental and Preventive Sciences, University of Ferrara, Ferrara, Italy.

JMIR public health and surveillance
|July 12, 2023
PubMed
概括

机器学习技术 (MLT) 可以自动分类儿科急诊诊断,改善伤害监测. 这种方法提高了伤害病例的识别,并减少了卫生专业人员手动编码的努力.

关键词:
儿童和青少年健康问题死亡死亡死亡死亡死亡死亡紧急情况 紧急情况紧急情况部门的急救部门.流行病学监测 流行病学监测 流行病学监测住院治疗 住院治疗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.患者记录 患者记录儿科的入院时间儿科 儿科 儿科 儿科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文本采矿 文本采矿是什么无意中造成的伤害.

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科学领域:

  • 儿科急诊医学 儿科急诊医学
  • 公共卫生监督是对公共卫生的监督.
  • 计算流行病学计算流行病学

背景情况:

  • 意外伤害是幼儿死亡的主要原因.
  • 急诊室 (ED) 的诊断对于伤害监测至关重要.
  • ED数据经常使用自由文本,阻碍了有效的分析.

研究的目的:

  • 开发一个自动化工具来对儿科ED诊断进行分类.
  • 确定受伤病例并评估意大利帕多瓦的儿科受伤负担.
  • 提高儿童伤害流行病学监测的效率.

主要方法:

  • 利用了2007-2018年283,468例儿科住院的数据集.
  • 训练有素的机器学习分类器 (SVM,GBM,随机森林) 在约4万个手动分类的诊断上.
  • 诊断分为伤害与非伤害,故意与非故意伤害,以及非故意伤害的类型.

主要成果:

  • 支持矢量机 (SVM) 在伤害与非伤害分类方面实现了94.14%的准确性.
  • 渐变增强方法 (GBM) 显示了92%的准确性,用于故意与非故意的伤害分类.
  • 在非故意伤害分类方面,SVM也表现最好.

结论:

  • 机器学习技术 (MLT) 在自动化儿科ED诊断分类方面表现有前途.
  • MLT通过提高准确性和减少手工工作量来加强流行病学监测.
  • 开发的系统有助于有效识别和分类儿科伤害.