一种用于自动化神经病患者出院总结医院课程的方法
Vince C Hartman1,2, Sanika S Bapat1,2, Mark G Weiner3,4
1Cornell Tech, New York, NY 10044, United States.
Journal of the American Medical Informatics Association : JAMIA
|August 28, 2023
概括
自动化临床笔记可以减少医生倦怠. 这项研究开发了一种人工智能方法,使用变压器模型生成出院的医院课程摘要,实现医生水平的质量.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 临床信息学 临床信息学
背景情况:
- 医生倦怠是医疗保健中的一个重要问题.
- 自动化临床笔记,特别是出院摘要,可以减轻医生的工作负担.
- 目前的电子健康记录 (EHR) 系统需要医院课程的手动文档.
研究的目的:
- 开发和评估一个自动化方法来总结出院摘要的医院课程部分.
- 评估人工智能生成的摘要的临床实用性和质量.
主要方法:
- 使用编码器-解码器序列对序列变压器模型 (BERT和BART).
- 精细调整的模型和优化为事实性使用受约束的光束搜索.
- 在学术医疗中心的神经病学部门的EHR数据上接受培训和测试.
主要成果:
- 获得了ROUGE的得分,R-2为13.76.
- 经过董事会认证的医生对62%的自动摘要进行了盲目评估,认为它们符合护理标准.
- 证明了自动化方法的潜在临床有用性.
结论:
- 本研究介绍了第一个用于生成出院总结医院课程的自动化方法之一.
- 开发的AI方法接近医生撰写的摘要的质量.
- 这些发现表明,这是一种有希望的工具,可以减轻医生倦怠和提高文档效率.
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