University of Adelaide, Adelaide, South Australia 5005, Australia.
Queen Elizabeth Hospital, Adelaide, South Australia 5011, Australia.
Int J Qual Health Care. 2023 Oct 17;35(4). doi: 10.1093/intqhc/mzad077.
Falls are a common problem associated with significant morbidity, mortality, and economic costs. Current fall prevention policies in local healthcare settings are often guided by information provided by fall risk assessment tools, incident reporting, and coding data. This review was conducted with the aim of identifying studies which utilized natural language processing (NLP) for the automated detection and prediction of falls in the healthcare setting. The databases Ovid Medline, Ovid Embase, Ovid Emcare, PubMed, CINAHL, IEEE Xplore, and Ei Compendex were searched from 2012 until April 2023. Retrospective derivation, validation, and implementation studies wherein patients experienced falls within a healthcare setting were identified for inclusion. The initial search yielded 2611 publications for title and abstract screening. Full-text screening was conducted on 105 publications, resulting in 26 unique studies that underwent qualitative analyses. Studies applied NLP towards falls risk factor identification, known falls detection, future falls prediction, and falls severity stratification with reasonable success. The NLP pipeline was reviewed in detail between studies and models utilizing rule-based, machine learning (ML), deep learning (DL), and hybrid approaches were examined. With a growing literature surrounding falls prediction in both inpatient and outpatient environments, the absence of studies examining the impact of these models on patient and system outcomes highlights the need for further implementation studies. Through an exploration of the application of NLP techniques, it may be possible to develop models with higher performance in automated falls prediction and detection.
跌倒 是一个常见的问题，与重大发病率、死亡率和经济成本有关。目前，当地医疗保健环境中的跌倒预防政策通常是根据跌倒风险评估工具、事件报告和编码数据提供的信息来指导的。本综述的目的是确定利用自然语言处理（NLP）自动检测和预测医疗环境中跌倒的研究。从 2012 年到 2023 年 4 月，在 Ovid Medline、Ovid Embase、Ovid Emcare、PubMed、CINAHL、IEEE Xplore 和 Ei Compendex 数据库中进行了搜索。纳入了在医疗保健环境中经历过跌倒的回顾性推导、验证和实施研究。最初的搜索产生了 2611 篇标题和摘要筛选的出版物。对 105 篇出版物进行了全文筛选，最终有 26 项独特的研究进行了定性分析。研究应用 NLP 进行跌倒风险因素识别、已知跌倒检测、未来跌倒预测和跌倒严重程度分层，取得了相当大的成功。研究之间详细审查了 NLP 管道，检查了基于规则、机器学习 (ML)、深度学习 (DL) 和混合方法的模型。随着关于住院和门诊环境中跌倒预测的文献不断增加，缺乏研究检查这些模型对患者和系统结果的影响，这突出了进一步实施研究的必要性。通过探索 NLP 技术的应用，可能开发出在自动跌倒预测和检测方面性能更高的模型。
