Details of included studies.

Introduction Natural 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. Methods All 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. Results In 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. Conclusion Unstructured 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）依托多种计算方法分析并理解人类语言，目前已被应用于急诊科（Emergency Department，ED）分诊获取的数据，以预测各类临床结局。本范围综述的研究目的为：评估自然语言处理在急诊科分诊数据中的应用场景；对比基于自然语言处理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；探究相较于仅使用结构化数据的预测模型，融入自由文本是否能够提升模型的预测性能。 研究方法 所有将自然语言处理技术应用于急诊科分诊获取的自由文本的英文同行评审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、考克兰系统综述数据库（Cochrane Database of Systematic Reviews）、Web of Science及Scopus等电子数据库，检索与自然语言处理和分诊相关的医学主题词及文本关键词。最后一次检索数据库的时间为2022年1月1日。采用预测模型偏倚风险评估工具（Prediction model Risk of Bias Assessment Tool，PROBAST）对纳入研究的偏倚风险进行评估。由于各研究间异质性较高且偏倚风险普遍较高，本研究未开展元分析，而是采用描述性综合的方式进行结果整合。 研究结果 本研究共筛选3730项研究，最终纳入20项。各研究的研究人群规模差异显著，覆盖从180万患者至598份分诊记录的区间。本研究评估的最常见结局包括分诊评分预测、收治入院预测以及重症疾病预测。自然语言处理模型在预测收治需求、分诊评分、重症疾病，以及将自由文本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性能更优。然而，大部分研究（80%）被评估为存在较高偏倚风险，且仅有1项研究报道了将自然语言处理模型应用于临床实践的案例。 研究结论 自然语言处理模型已利用非结构化自由文本分诊记录实现临床相关结局的预测。但现有研究普遍存在较高偏倚风险，多数研究为回顾性研究，且将模型落地至临床实践的案例较为匮乏。未来仍需开展前瞻性研究，对比基于急诊科分诊数据的自然语言处理应用与常规临床实践，评估其是否能够改善急诊科临床结局。