利用人工智能推动及时改善患者体验反:算法验证
Mustafa Khanbhai1,2, Catalina Carenzo1,2, Sarindi Aryasinghe1,2
1iCARE Digital Collaboration Space & Secure Data Environment, NIHR Imperial Biomedical Research Centre, 1a Sheldon Square, London, W2 6PY, United Kingdom, 44 20 7589 5111.
JMIR medical informatics
|July 10, 2025
概括
使用监督算法对患者反的自动文本分析在预测不同医疗保健环境中的主题和情绪方面实现了超过75%的准确性. 进一步的改进提高了性能,使朋友和家庭测试数据的分析更容易.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 手动分析朋友和家庭测试 (FFT) 数据是具有挑战性的,因为越来越多的数量.
- 自动化文本分析对于患者反中的实时情绪和主题预测至关重要.
研究的目的:
- 测试和完善一个监督的算法,用于分析不同医疗机构的患者反.
- 改善文本分析在各种护理环境中的跨语境性能.
主要方法:
- 一个监督的文本分析算法,最初是在伦敦开发的,在9个不同的英国医疗保健组织中进行了测试.
- 该算法通过使用匿名的FFT数据的词包进行代训练,并使用手动编码进行验证.
- 为了标准化实施,开发了一个部署框架和管道.
主要成果:
- 该算法在预测各种设置中的主题和情绪方面取得了超过75%的准确性.
- 在儿科和心理健康方面,初始较低的准确性与信任特定编码模板显著改善.
- 经过五次组织,主题和得到了实现,这表明学习效率高.
结论:
- 开发的监督学习算法有效地利用自由文本的FT数据,在各种医疗保健环境中获得洞察力.
- 通过进一步编码来解决上下文变化,提高了算法准确性和可靠性.
- 该方法在分析患者反时促进合作和共享学习.
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