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
1Cervico-Facial Oncology Surgical Department, University Institute of Face and Neck, Centre Antoine Lacassagne University of Côte d'Azur, Nice, France.
JCO clinical cancer informatics
|December 10, 2024
概括
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.
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.
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