Center for Language and Speech Processing, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
Department of Computer Science, Johns Hopkins Whiting School of Engineering, Baltimore, Maryland, USA.
J Am Med Inform Assoc. 2019 Aug 1;26(8-9):787-795. doi: 10.1093/jamia/ocz093.
Geriatric syndromes such as functional disability and lack of social support are often not encoded in electronic health records (EHRs), thus obscuring the identification of vulnerable older adults in need of additional medical and social services. In this study, we automatically identify vulnerable older adult patients with geriatric syndrome based on clinical notes extracted from an EHR system, and demonstrate how contextual information can improve the process.
We propose a novel end-to-end neural architecture to identify sentences that contain geriatric syndromes. Our model learns a representation of the sentence and augments it with contextual information: surrounding sentences, the entire clinical document, and the diagnosis codes associated with the document. We trained our system on annotated notes from 85 patients, tuned the model on another 50 patients, and evaluated its performance on the rest, 50 patients.
Contextual information improved classification, with the most effective context coming from the surrounding sentences. At sentence level, our best performing model achieved a micro-F1 of 0.605, significantly outperforming context-free baselines. At patient level, our best model achieved a micro-F1 of 0.843.
Our solution can be used to expand the identification of vulnerable older adults with geriatric syndromes. Since functional and social factors are often not captured by diagnosis codes in EHRs, the automatic identification of the geriatric syndrome can reduce disparities by ensuring consistent care across the older adult population.
EHR free-text can be used to identify vulnerable older adults with a range of geriatric syndromes.
老年综合征，如功能障碍和缺乏社会支持，在电子健康记录（EHR）中通常未被编码，从而掩盖了需要额外医疗和社会服务的脆弱老年人的识别。在这项研究中，我们基于从 EHR 系统中提取的临床记录，自动识别患有老年综合征的脆弱老年患者，并展示了如何利用上下文信息来改善该过程。
我们提出了一种新颖的端到端神经架构，用于识别包含老年综合征的句子。我们的模型学习句子的表示形式，并使用上下文信息对其进行增强：周围的句子、整个临床文档以及与文档相关联的诊断代码。我们在 85 名患者的注释记录上训练我们的系统，在另外 50 名患者上调整模型，并在其余 50 名患者上评估其性能。
上下文信息提高了分类性能，最有效的上下文信息来自周围的句子。在句子级别上，我们表现最好的模型的微 F1 值为 0.605，明显优于无上下文的基线。在患者级别上，我们表现最好的模型的微 F1 值为 0.843。
我们的解决方案可用于扩大对患有老年综合征的脆弱老年人的识别。由于功能和社会因素在 EHR 中通常不被诊断代码所捕捉，因此自动识别老年综合征可以通过确保老年人群体的一致护理来减少差异。
EHR 自由文本可用于识别患有各种老年综合征的脆弱老年人。
