通过合并文本处理和深度学习来优化医疗保健系统:系统性审查
1Department of Computer Science and Engineering, NSUT East Campus (erstwhile AIACTR), Affiliated to Guru Gobind Singh Indraprastha University, Delhi, India.
本综述探讨了用于处理临床文本数据的深度学习,涵盖了CNN和RNN等方法. 它讨论了从概念提取到药物监督的应用,并确定了未来的研究方向.
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.
- 人工智能的人工智能
背景情况:
- 临床文本数据的快速增长给医疗保健专业人员带来了挑战.
- 现有的工具和方法难以跟上日常数据生成的步伐.
研究的目的:
- 在医疗保健中使用深度学习来审查文本处理管道.
- 讨论深度学习在临床文本分析中的应用.
- 确定挑战和未来的研究范围.
主要方法:
- 对深度学习模型的调查,包括卷积神经网络 (CNN),循环神经网络 (RNN),长期短期记忆 (LSTM) 和门式循环单元 (GRU).
- 分析医疗保健领域的文字处理管道.
主要成果:
- 深度学习方法为分析大量临床文本数据提供解决方案.
- 应用包括临床概念提取,对话系统,情绪分析,临床试验匹配和药物监测.
结论:
- 深度学习和文本处理集成对于优化医疗保健至关重要.
- 需要进一步的研究来应对在临床环境中部署这些技术的挑战.
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