从现实世界的电子健康记录中识别 Delirium 临床笔记
Jennifer St Sauver1,2, Sunyang Fu3, Sunghwan Sohn3
1Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
Journal of clinical and translational science
|September 25, 2023
概括
一个自然语言处理 (NLP) 算法在一项大型临床笔记研究中发现了比国际疾病分类 (ICD) 代码更多的妄想发作. 这突出了NLP的重要性.
科学领域:
- 医疗信息学 医疗信息学
- 临床自然语言处理 临床自然语言处理
- 老年医学 老年医学
背景情况:
- 痴呆症是住院老年人的常见和严重的疾病.
- 精确的 Delirium 识别对于及时干预和改善患者结果至关重要.
- 目前的方法,如国际疾病分类 (ICD) 编码,可能低估了妄想的流行率.
研究的目的:
- 评估自然语言处理 (NLP) 算法的性能,以识别现实世界的临床笔记中的妄想发作.
- 将NLP识别的妄想率与ICD代码识别的妄想率进行比较.
- 使用这两种方法分析痴呆率的人口和时间趋势.
主要方法:
- 利用罗切斯特流行病学项目确定2011年至2017年期间住院的65岁及以上的个人.
- 使用ICD代码和NLP算法应用到临床笔记,确定了 Delirium 病例.
- 计算和比较每100次住院 delirium 率,分析年龄,性别,种族/种族和年龄的差异,使用波桑回归.
主要成果:
- 与ICD代码 (3.02%) 相比,NLP算法发现了明显更多的妄想发作 (7.36%).
- 在这两种方法中,妄想率随年龄增加.
- 基于NLP的比率显示出种族/种族差异 (白人人口中较高),与基于ICD的比率不同.
结论:
- 与传统的ICD编码相比,NLP算法在临床笔记中显示出更高的能力来检测妄想发作.
- 尽管NLP具有优势,但由于临床文档的局限性,NLP可能仍然低估了妄想.
- 需要进一步的研究来完善NLP算法,并解决更全面的妄想监测的文档挑战.
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