使用自然语言处理技术在主诉文本中识别痛风发作
Identification of Gout Flares in Chief Complaint Text Using Natural Language Processing.
作者信息
Osborne John D, Booth James S, O'Leary Tobias, Mudano Amy, Rosas Giovanna, Foster Phillip J, Saag Kenneth G, Danila Maria I
机构信息
University of Alabama at Birmingham, Birmingham, Alabama, USA.
出版信息
AMIA Annu Symp Proc. 2021 Jan 25;2020:973-982. eCollection 2020.
Abstract
Many patients with gout flares treated in the Emergency Department (ED) often do not receive optimal continuity of care after an ED visit. Thus, developing methods to identify patients with gout flares in the ED and referring them to appropriate outpatient gout care is required. While Natural Language Processing (NLP) has been used to detect gout flares retrospectively, it is much more challenging to identify patients prospectively during an ED visit where documentation is usually minimal. We annotate a corpus of ED triage nurse chief complaint notes for the presence of gout flares and implement a simple algorithm for gout flare ED alerts. We show that the chief complaint alone has strong predictive power for gout flares. We make available a de-identified version of this corpus annotated for gout mentions, which is to our knowledge the first free text chief complaint clinical corpus available.
摘要
许多在急诊科（ED）接受痛风发作治疗的患者在急诊就诊后往往无法获得最佳的连续护理。因此，需要开发方法来识别急诊科中痛风发作的患者，并将他们转介到适当的门诊痛风护理。虽然自然语言处理（NLP）已被用于回顾性检测痛风发作，但在急诊就诊期间前瞻性识别患者则更具挑战性，因为此时的文档记录通常很少。我们对急诊分诊护士的主诉记录语料库进行注释，以确定是否存在痛风发作，并实施了一种简单的痛风发作急诊警报算法。我们表明，仅主诉就对痛风发作具有很强的预测能力。我们提供了一个经过去识别处理的该语料库版本，其中标注了痛风相关内容，据我们所知，这是首个可用的标注有痛风相关内容的自由文本主诉临床语料库。
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