Background Falls involve dynamic risk factors that change over time, but most studies on fall-risk factors are cross-sectional and do not capture this temporal aspect. The longitudinal clinical notes within electronic health records (EHR) provide an opportunity to analyse fall risk factor trajectories through Natural Language Processing techniques, specifically dynamic topic modelling (DTM). This study aims to uncover fall-related topics for new fallers and track their evolving trends leading up to falls. Methods This case–cohort study utilised primary care EHR data covering information on older adults between 2016 and 2019. Cases were individuals who fell in 2019 but had no falls in the preceding three years (2016–18). The control group was randomly sampled individuals, with similar size to the cases group, who did not endure falls during the whole study follow-up period. We applied DTM on the clinical notes collected between 2016 and 2018. We compared the trend lines of the case and control groups using the slopes, which indicate direction and steepness of the change over time. Results A total of 2,384 fallers (cases) and an equal number of controls were included. We identified 25 topics that showed significant differences in trends between the case and control groups. Topics such as medications, renal care, family caregivers, hospital admission/discharge and referral/streamlining diagnostic pathways exhibited a consistent increase in steepness over time within the cases group before the occurrence of falls. Conclusions Early recognition of health conditions demanding care is crucial for applying proactive and comprehensive multifactorial assessments that address underlying causes, ultimately reducing falls and fall-related injuries.

中文翻译：

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通过全科医生临床记录的自然语言处理，新跌倒者跌倒事件发生前的主题演变

背景跌倒涉及随时间变化的动态风险因素，但大多数关于跌倒风险因素的研究都是横向的，并没有捕捉到这个时间方面。电子健康记录 (EHR) 中的纵向临床记录提供了通过自然语言处理技术（特别是动态主题建模 (DTM)）分析跌倒风险因素轨迹的机会。这项研究旨在为新跌倒者揭示与跌倒相关的主题，并跟踪他们导致跌倒的演变趋势。方法 这项病例队列研究利用了涵盖 2016 年至 2019 年老年人信息的初级保健 EHR 数据。病例是在 2019 年跌倒但在之前三年 (2016-18 年) 没有跌倒的个体。对照组是随机抽取的个体，其体型与病例组相似，在整个研究随访期间未经历跌倒。我们对 2016 年至 2018 年间收集的临床记录应用了 DTM。我们使用斜率比较了病例组和对照组的趋势线，斜率表明随时间变化的方向和陡度。结果 共纳入 2,384 名跌倒者（例）和同等数量的对照者。我们确定了 25 个主题，这些主题在病例组和对照组之间显示出显着的趋势差异。在跌倒发生之前，病例组中药物、肾脏护理、家庭护理人员、入院/出院和转诊/简化诊断途径等主题的陡度随着时间的推移而持续增加。结论 及早识别需要护理的健康状况对于应用主动、全面的多因素评估来解决根本原因、最终减少跌倒和跌倒相关伤害至关重要。