使用人工智能和机器学习算法对电子健康记录数据进行预测建模:优势和挑战
Michael J Patton1, Vincent X Liu2
1Medical Scientist Training Program, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA; Hugh Kaul Precision Medicine Institute at the University of Alabama at Birmingham, 720 20th Street South, Suite 202, Birmingham, Alabama, 35233, USA.
Critical care clinics
|September 13, 2023
概括
电子健康记录 (EHR) 和机器学习 (ML) 推进了关键护理. 人工智能和电子病历数据现在推动了重症监护室 (ICU) 结果预测研究,改善了患者护理.
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:
- 2008年至2014年间,美国医院广泛实施电子健康记录 (EHR) 系统,产生了大量的新数据集.
- 同时,计算机和机器学习 (ML) 算法的进步使得这些健康数据的有效分析成为可能,从而促进了临床创新.
研究的目的:
- 检查重症监护病房 (ICU) 内结果预测方法的历史演变.
- 调查EHR数据在重症监护研究中的越来越多的利用情况.
- 分析人工智能 (AI) 和ML在重症监护中的出现和影响.
主要方法:
- 审查在重症监护研究的历史趋势.
- 分析EHR采用对数据可用性的影响.
- 在ICU环境中探索AI和ML算法的集成.
主要成果:
- 电子病历数据和AI/ML的融合导致了重症监护中的结果预测研究的显著增加.
- 电子健康记录系统提供了对先进分析方法至关重要的新型数据元素.
- 人工智能和机器学习为分析复杂的ICU数据提供了强大的工具.
结论:
- 整合EHR数据和AI/ML代表了重症监护研究的范式转变.
- 未来的研究很可能会利用这些技术来更准确地预测患者的结果.
- 增强的数据分析能力有望推动重症监护中的重大临床创新.
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