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通过神经机器翻译模型的患者教育材料提高健康素养

David Oniani¹, Sreekanth Sreekumar¹, Renuk DeAlmeida²

¹Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science

|June 23, 2023

概括

这项研究使用自然语言处理来简化复杂的健康信息. 神经机器翻译模型,特别是BiLSTM,通过将术语翻译成普通语言,提高了健康素养.

科学领域:

医疗信息学医疗信息学
自然语言处理自然语言处理.
患者教育患者教育

背景情况:

低健康素养有助于健康状况不佳和不平等.
患者教育材料往往含有复杂的语言,许多人无法访问.
提高健康素养是国家健康倡议的一个关键目标.

研究的目的:

调查自然语言处理 (NLP) 的使用,以提高健康素养.
将患者教育材料中的复杂医学术语自动翻译成更简单的语言.
为了比较不同神经机器翻译 (NMT) 模型对此任务的性能.

主要方法:

从四个著名的健康网站取了患者教育材料.
训练并评估了最先进的神经机器翻译 (NMT) 模型,包括基于双向长期短期记忆 (BiLSTM) 和BERT的模型.
通过分析健康-文盲语言和句子特征的简化来评估模型的有效性.

主要成果:

双向长短期记忆 (BiLSTM) NMT模型表现出比基于BERT的NMT模型更好的性能.
NMT模型成功地将复杂的医学词汇识别和简化为普通人的术语.
在保持句子完整性,流性和可读性方面仍然存在挑战,特别是在专门的医学术语方面.

结论:

神经机器翻译显示,通过简化患者教育材料,有望提高健康素养.
BiLSTM NMT模型有效地将健康术语翻译成易于理解的语言.
需要进一步的研究来解决复杂的医学术语的句子结构,流动和准确性的局限性.

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