AGE Research Group, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne NHS Foundation Trust, Cumbria Northumberland Tyne and Wear NHS Foundation Trust and Newcastle University, Newcastle upon Tyne, UK.
Age Ageing. 2024 Jul 2;53(7). doi: 10.1093/ageing/afae135.
Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear.
We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines.
From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%-100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%-100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms.
Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported.
在医院记录中，衰老综合征的记录和编码被认为是不理想的。自然语言处理算法可能有助于识别电子医疗记录中的诊断，从而改善这些衰老综合征的记录和编码，但这些算法的可行性和诊断准确性尚不清楚。
我们按照预先确定的方案并遵循系统评价和荟萃分析的首选报告项目 (PRISMA) 指南进行了系统审查。从每个数据库的创建到 2023 年 9 月底在 PubMed、Medline、Embase、CINAHL、ACM 数字图书馆、IEEE Xplore 和 Scopus 中进行了搜索。通过两名合著者对搜索结果进行独立审查，并从每项研究中提取数据来确定计算方法、文本来源、测试策略和性能指标，从而确定符合条件的研究。按照无荟萃分析研究指南，根据衰老综合征和计算方法对数据进行了叙述性综合。
从筛选出的 1030 个标题中，有 22 项研究符合纳入标准。一项研究侧重于识别肌少症，一项研究侧重于虚弱，12 项研究侧重于跌倒，5 项研究侧重于谵妄，5 项研究侧重于痴呆，4 项研究侧重于尿失禁。与参考标准相比，20 项研究报告了算法的敏感性（57.1%-100%），仅 12 项研究报告了特异性（84.0%-100%）。研究设计质量参差不齐，并非所有研究都报告了与诊断准确性相关的结果，并且很少有研究对算法进行外部验证。
目前的证据表明，自然语言处理算法可以识别电子健康记录中的衰老综合征。但是，需要使用报告适当指标的严格设计的诊断准确性研究来测试算法。
