自然语言处理用于管理中风疾病的应用:范围审查
Helios De Rosario1, Salvador Pitarch-Corresa1, Ignacio Pedrosa2
1Instituto de Biomecánica de Valencia, Universitat Politècnica de València, Valencia, Spain.
JMIR medical informatics
|September 6, 2023
概括
自然语言处理 (NLP) 有助于中风应急管理,主要用于诊断和预后,利用机器学习和对临床文本和图像数据的深度学习. 这一趋势表明,对于中风护理,先进的深度学习模型的使用越来越多.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 神经学 神经学
背景情况:
- 自然语言处理 (NLP) 的进步正在增加医疗应用,特别是对于中风紧急情况.
- 在这种不断发展的领域中,了解关于中风的NLP积累的经验至关重要.
研究的目的:
- 在中风应急管理中对NLP应用进行10年范围审查.
- 识别最先进的技术,应用环境和软件工具.
主要方法:
- 使用关键词"自然语言处理"和"中风"搜索了Scopus和Medline数据库.
- 关于研究阶段,背景,数据类型,方法和软件的数据被提取和分析.
- 用多重对应分析来探索类别之间的关系.
主要成果:
- 审查了29篇论文,主要是过去两年关于缺血性中风的队列研究.
- NLP主要协助诊断和结果预后,使用诊断报告和医学图像注释.
- 使用更简单的NLP方法和本体学的机器学习是常见的,深度学习技术的兴起.
结论:
- 在中风中NLP的应用反映了更广泛的AI趋势,重点是改善临床过程而不是康复.
- 深度学习,特别是来自变压器的双向编码器表示 (BERT),是NLP中风的最先进技术.
- 人们越来越重视使用先进的NLP处理医疗图像注释.
关键词:
在NLP中,我们使用了NLP.人工智能的人工智能是人工智能.心血管疾病的心血管疾病深度学习是一种深度学习.机器学习是机器学习.自然语言处理自然语言处理.审查方法论 审查方法论审查方法 审查方法探测范围 探测范围 探测范围范围审查 范围审查审查一次性中风中风中风中风中风更多相关视频
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