机器学习的方法用于从电子健康记录中提取现实世界的数据变量
Blythe Adamson1,2, Michael Waskom1, Auriane Blarre1
1Flatiron Health, Inc., New York, NY, United States.
Frontiers in pharmacology
|October 2, 2023
概括
使用自然语言处理 (NLP) 和机器学习 (ML) 的人工智能 (AI) 可以有效地将电子健康记录 (EHR) 编辑成用于瘤学研究的现实世界数据 (RWD).
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 人工智能的进步,包括像ChatGPT这样的NLP和ML模型,为EHR数据策划创造了新的可能性.
- 电子健康记录 (EHR) 的高效选为现实数据 (RWD) 对于瘤学的证据生成至关重要.
- 促进对EHR数据的AI方法的透明度和可解释性是一个新兴的研究领域.
研究的目的:
- 描述人工智能驱动的EHR数据策划行业方法的研究和开发.
- 促进人工智能在现实世界数据生成中的应用透明度和可解释性.
主要方法:
- 应用NLP和ML技术来训练,验证和测试从非结构化EHR文档中提取信息.
- 利用全国范围的EHR衍生数据库进行研究.
- 专注于基于机器学习的,仅由模型决定的变量提取,而不预测未来事件或推断缺失的信息.
主要成果:
- 开发并验证了一种NLP/ML方法,用于从非结构化的EHR数据中提取临床上有意义的信息.
- 与手动抽象数据相比,在提取的变量中实现了高性能.
- 生成的研究准备变量包括癌症诊断,阶段,组织学,吸烟状态,手术,生物标志物结果和治疗方法.
结论:
- 通过NLP和ML,可以快速且可扩展地从电子健康记录中提取回顾性临床数据.
- 这种方法有助于研究人员从癌症患者的经验中学习.
- 人工智能驱动的数据策划增强了对瘤学研究现实数据的实用性.
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