从自由文本护理笔记中提取痴呆症激动症状,使用先进的自然语言处理.
Dinithi Vithanage1, Yunshu Zhu1, Zhenyu Zhang1
1Center for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, Australia.
研究人员开发了一种深度学习模型,从护理笔记中提取痴呆症激动症状. 这项技术有助于了解患者的需求,并改善老年护理机构的护理.
科学领域:
- 老年学是一门学科.
- 计算机科学 计算机科学
- 临床信息学 临床信息学
背景情况:
- 护理笔记是丰富的患者数据来源,包括护理需求和症状.
- 从自由文本笔记中提取特定信息,比如痴呆症的兴奋症状,是非常具有挑战性的.
- 自动分析护理笔记可以改善患者护理和临床决策.
研究的目的:
- 开发和评估基于深度学习和转移学习的命名实体识别 (NER) 模型.
- 从自由文本护理笔记中提取痴呆症中激动的症状.
- 为机器学习模型奠定基础,推最佳的护理行动.
主要方法:
- 使用临床BioBERT模型进行文字嵌入.
- 应用双向长期短期记忆 (BiLSTM) 和条件随机场 (CRF) 模型用于NER.
- 训练并评估了澳大利亚住宅老年护理机构的护理笔记模型.
主要成果:
- 拟议的NER模型在提取兴奋症状方面表现出令人满意的表现.
- 获得了75%的F1得分和78%的准确性识别症状.
- 成功处理了自由文本护理笔记,以确定关键的患者观察.
结论:
- 深度学习模型,特别是BiLSTM-CRF的NER,对于从护理笔记中提取痴呆症激动症状是有效的.
- 开发的模型显示了改善老年护理机构数据提取的前景.
- 未来的工作重点是开发机器学习模型,以推护理行动.
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