Results of included studies, grouped by outcome.

IntroductionNatural language processing (NLP) uses various computational methods to analyse and understand human language, and has been applied to data acquired at Emergency Department (ED) triage to predict various outcomes. The objective of this scoping review is to evaluate how NLP has been applied to data acquired at ED triage, assess if NLP based models outperform humans or current risk stratification techniques when predicting outcomes, and assess if incorporating free-text improve predictive performance of models when compared to predictive models that use only structured data.MethodsAll English language peer-reviewed research that applied an NLP technique to free-text obtained at ED triage was eligible for inclusion. We excluded studies focusing solely on disease surveillance, and studies that used information obtained after triage. We searched the electronic databases MEDLINE, Embase, Cochrane Database of Systematic Reviews, Web of Science, and Scopus for medical subject headings and text keywords related to NLP and triage. Databases were last searched on 01/01/2022. Risk of bias in studies was assessed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Due to the high level of heterogeneity between studies and high risk of bias, a metanalysis was not conducted. Instead, a narrative synthesis is provided.ResultsIn total, 3730 studies were screened, and 20 studies were included. The population size varied greatly between studies ranging from 1.8 million patients to 598 triage notes. The most common outcomes assessed were prediction of triage score, prediction of admission, and prediction of critical illness. NLP models achieved high accuracy in predicting need for admission, triage score, critical illness, and mapping free-text chief complaints to structured fields. Incorporating both structured data and free-text data improved results when compared to models that used only structured data. However, the majority of studies (80%) were assessed to have a high risk of bias, and only one study reported the deployment of an NLP model into clinical practice.ConclusionUnstructured free-text triage notes have been used by NLP models to predict clinically relevant outcomes. However, the majority of studies have a high risk of bias, most research is retrospective, and there are few examples of implementation into clinical practice. Future work is needed to prospectively assess if applying NLP to data acquired at ED triage improves ED outcomes when compared to usual clinical practice.引言：自然语言处理（Natural Language Processing，NLP）依托多种计算方法对人类语言进行分析与理解，目前已被应用于急诊科（Emergency Department，ED）分诊获取的数据中，以预测各类临床结局。本次范围综述的研究目的在于：评估NLP在ED分诊获取数据中的应用方式；对比基于NLP的模型与人类专家或现有风险分层技术在预测临床结局时的性能优劣；以及分析相较于仅使用结构化数据的预测模型，纳入自由文本数据是否能够提升模型的预测性能。 方法：所有将NLP技术应用于ED分诊获取的自由文本数据的英文同行评议研究均符合纳入标准。本研究排除仅聚焦于疾病监测的研究，以及使用分诊后获取的信息的研究。我们通过MEDLINE、Embase、Cochrane系统综述数据库（Cochrane Database of Systematic Reviews）、Web of Science及Scopus等电子数据库，检索与NLP和分诊相关的医学主题词与文本关键词。数据库的最后检索时间为2022年1月1日。研究的偏倚风险采用预测模型偏倚风险评估工具（Prediction model Risk of Bias Assessment Tool，PROBAST）进行评价。鉴于各研究间异质性较高且偏倚风险普遍较高，本研究未开展Meta分析，而是采用叙述性综合分析的方法进行结果整合。 结果：本研究共筛选3730项研究，最终纳入20项相关研究。各研究的样本量差异悬殊，覆盖180万例患者至598份分诊记录不等。本次纳入研究最常评估的结局包括分诊评分预测、入院预测及危重症预测。NLP模型在预测入院需求、分诊评分、危重症，以及将自由文本形式的主诉映射至结构化字段等任务中均取得了较高的准确率。相较于仅使用结构化数据的模型，同时纳入结构化数据与自由文本数据的模型预测效果更优。然而，多数研究（80%）被评定为存在较高偏倚风险，且仅有1项研究报道了将NLP模型部署至临床实践的案例。 结论：非结构化的自由文本分诊记录已被NLP模型用于预测具有临床意义的结局。但目前多数相关研究存在较高偏倚风险，且绝大多数为回顾性研究，仅有极少数案例实现了NLP模型在临床实践中的落地应用。未来仍需开展前瞻性研究，以评估相较于常规临床实践，将NLP应用于ED分诊获取的数据是否能够改善急诊科的临床结局。