ARDSFlag:一种NLP/机器学习算法,可视化和检测高概率的ARDS录取,独立于提供商识别和计费代码
Amir Gandomi1,2, Phil Wu3, Daniel R Clement4
1Frank G. Zarb School of Business, Hofstra University, Hempstead, NY, USA. amir.gandomi@hofstra.edu.
BMC medical informatics and decision making
|July 16, 2024
概括
我们开发了ARDSFlag,这是一个使用机器学习和NLP的自动化算法,用于根据柏林定义诊断急性呼吸困扰综合征 (ARDS). 在检测ARDS病例方面,ARDSFlag取得了89.0%的准确性.
科学领域:
- 关键护理医学 关键护理医学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 急性呼吸困扰综合征 (ARDS) 诊断不一致,影响患者的护理.
- 自动化ARDS检测对于及时准确的临床决策至关重要.
- 柏林定义提供了ARDS诊断的标准,但它的应用可能是可变的.
研究的目的:
- 开发一种自动化算法,ARDSFlag,使用柏林定义来诊断ARDS.
- 创建一个可视化工具,以帮助临床医生评估ARDS标准.
- 提高在重症监护病房中ARDS检测的一致性和准确性.
主要方法:
- 利用机器学习 (ML) 和自然语言处理 (NLP) 来分析电子健康记录 (EHR).
- 纳入结构化和非结构化数据来评估柏林标准.
- 在大型数据集上训练有素的文本分类器,用于识别关键诊断指标.
主要成果:
- 在识别双边透物 (91.9%) 和心力衰竭/流体过载 (86.1%在放射学, 98.4%在心声图) 中,ARDSFlag取得了很高的准确性.
- 在300个案例的测试组中,ARDSFlag显示总体准确率为89.0% (特异性:91.7%,回忆:80.3%,精度:75.0%).
- 该算法成功诊断了ARDS,发病时间和严重程度.
结论:
- 本研究介绍了使用ML和NLP自动检测ARDS的第一个方法.
- 与现有方法相比,ARDSFlag在准确性和一致性方面提供了显著的改进.
- 开发的算法和可视化工具有可能增强ARDS诊断和管理.
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