基于深度学习的自然语言处理模型,以从例行访问中识别心血管疾病和糖尿病患者的住院治疗"文本"
Alessandro Guazzo1, Enrico Longato1, Gian Paolo Fadini2
1Department of Information Engineering, University of Padova, 35131, Padua, Italy.
Scientific reports
|November 5, 2023
概括
这项研究开发了使用自然语言处理 (NLP) 的深度学习模型,从糖尿病患者的电子健康记录中提取心血管疾病 (CVD) 住院情况. NLP方法有效地识别了心血管疾病事件,有助于临床决策和研究.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 心血管医学 心血管医学
背景情况:
- 临床事件报告在很大程度上依赖于医疗笔记中的非结构化的自由形式文本.
- 从自由形式文本中提取全面的患者病史仍然是一个重大挑战.
- 自然语言处理 (NLP) 提供了一种将非结构化文本转换为结构化数据的方法.
研究的目的:
- 开发和评估基于深度学习的NLP模型,用于识别糖尿病患者的心血管疾病 (CVD) 住院.
- 评估NLP模型在不同时间窗口 (无限,24,12和6个月) 的性能,以检测CVD事件.
- 探索用于医疗记录和决策支持的自动CVD事件提取的临床实用性.
主要方法:
- 使用电子医疗记录数据从糖尿病患者.
- 为NLP开发深度学习模型,以处理自由形式的临床笔记.
- 在定义的时间窗口中使用精度回忆曲线,精度,回忆和F1分数来评估模型性能.
主要成果:
- 拟议的NLP方法在识别无限和24个月时间窗口的CVD相关住院治疗方面取得了成功.
- 正如预期的那样,随着时间窗口变得更短,模型性能下降.
- 该研究证实了使用NLP从例行访问笔记中提取特定临床结果的可行性.
结论:
- 基于深度学习的NLP模型可以有效地从非结构化的临床文本中识别糖尿病患者的心血管疾病住院.
- 开发的NLP工具在研究和临床决策方面的回顾性医疗记录丰富方面具有潜在的应用.
- 自动提取心脏病史可以提高患者记录的完整性,并支持流行病学研究.
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