十多年来电子健康记录中精神病学临床记录的词汇稳定性

IF 3.8 4区 医学 Q1 Medicine Acta Neuropsychiatrica Pub Date : 2022-09-06 DOI:10.1101/2022.09.05.22279610
L. Hansen, Kenneth C. Enevoldsen, M. Bernstorff, E. Perfalk, A. Danielsen, K. Nielbo, S. Østergaard
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引用次数: 2

摘要

自然语言处理方法有望通过利用隐藏在电子健康记录的临床笔记中的信息来改进临床预测。然而,临床实践——以及记录和存储临床记录的系统和数据库——随着时间的推移而变化。因此,临床记录的内容也可能随着时间的推移而改变,这可能会降低预测模型的性能。尽管它很重要,但临床记录随时间的稳定性很少得到测试。因此,在本研究中,我们通过量化句子长度、可读性、句法复杂性和临床内容,并估计这些指标的变化点,研究了2011年1月1日至2021年11月22日期间丹麦中部地区精神病学服务中心临床笔记的词汇稳定性(共14811551份临床笔记,描述了129570名患者)。随着时间的推移,我们发现词汇和句法的稳定性,这预示着在临床实践中使用自然语言处理进行预测建模的好兆头。
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Lexical Stability of Psychiatric Clinical Notes from Electronic Health Records over a Decade
Natural Language Processing methods hold promise for improving clinical prediction by utilising information otherwise hidden in the clinical notes of electronic health records. However, clinical practice-as well as the systems and databases in which clinical notes are recorded and stored-change over time. As a consequence, the content of clinical notes may also change over time, which could degrade the performance of prediction models. Despite its importance, the stability of clinical notes over time has rarely been tested. Therefore, in this study, we examined the lexical stability of clinical notes from the Psychiatric Services of the Central Denmark Region in the period from January 1, 2011, to November 22, 2021 (a total of 14,811,551 clinical notes describing 129,570 patients) by quantifying sentence length, readability, syntactic complexity and clinical content - and estimating changepoints in these metrics. We find lexical and syntactic stability over time, which bodes well for the use of Natural Language Processing for predictive modelling in clinical practice.
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来源期刊
Acta Neuropsychiatrica
Acta Neuropsychiatrica 医学-精神病学
CiteScore
8.50
自引率
5.30%
发文量
30
审稿时长
6-12 weeks
期刊介绍: Acta Neuropsychiatrica is an international journal focussing on translational neuropsychiatry. It publishes high-quality original research papers and reviews. The Journal''s scope specifically highlights the pathway from discovery to clinical applications, healthcare and global health that can be viewed broadly as the spectrum of work that marks the pathway from discovery to global health.
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