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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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通过人工智能增强甲状腺病理:使用RUBY从电子健康报告中自动提取数据.

Dorian Culié1,2, Renaud Schiappa2, Sara Contu2

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此摘要是机器生成的。

这项研究开发了一个使用人工智能的自动化系统,从甲状腺癌患者记录中提取关键数据. 这有助于改善恶性瘤风险的评估,并支持全面的数据存储.

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 医疗保健中的人工智能
  • 自然语言处理自然语言处理.

背景情况:

  • 甲状腺结节很普遍,需要对患者的护理进行准确的恶性瘤风险评估.
  • 手术干预是对不确定的甲状腺结节的最终治疗方法.
  • 从电子健康记录 (EHR) 中有效地提取数据对于改善初始评估至关重要.

研究的目的:

  • 开发一种自动化流程,从电子健康记录中提取和结构化生物医学见解.
  • 将卷积神经网络 (CNN) 应用于大型甲状腺病理队列.
  • 为了提高对甲状腺结节恶性瘤风险的初步评估.

主要方法:

  • 一组1600名患有甲状腺病理的患者被用于模型开发和测试.
  • 使用了来自各种临床接触的电子健康记录.
  • 一个基于CNN的模型 (RUBY-THYRO) 是使用SpaCy,关键字提取和后处理开发的.

主要成果:

  • 自动化系统表现出高性能,大多数变量 (30/42) 在精度,回忆和F1分数方面超过90%.
  • 病理性瘤阶段达到100%的准确性,而结节的数量得分较低.
  • 手术和预麻醉报告产生了特别高的性能指标.

结论:

  • 一种基于CNN的自然语言处理 (NLP) 方法有效地从甲状腺病理 EHR中提取和结构化数据.
  • 人工智能驱动的NLP为广泛的数据提取提供了具有成本效益的方法.
  • 这有助于建立针对甲状腺病理的综合性全医院数据仓库.