PDF(Pubmed)
Abstract：
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：已知在医院记录中记录和编码老化综合征是次优的。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，以改善这些老化综合征的记录和编码。但这种算法的可行性和诊断准确性尚不清楚。
方法：我们根据预定义的方案进行了系统评价，并符合系统评价和荟萃分析（PRISMA）指南的首选报告项目。从每个数据库开始到2023年9月底，在PubMed中进行了搜索，Medline,Embase,CINAHL,ACM数字图书馆,IEEEXplore和Scopus。通过两位共同作者对搜索结果进行独立审查，并从每项研究中提取数据以确定计算方法，从而确定合格的研究。文本的来源，测试策略和性能指标。根据无荟萃分析指南的研究，通过衰老综合征和计算方法对数据进行叙述性合成。
结果：从1030个标题筛选，22项研究符合纳入条件。一项研究专注于识别肌肉减少症，一个脆弱，十二个瀑布，五次谵妄,五个痴呆和四个失禁。在20项研究中报告了算法与参考标准相比的敏感性（57.1％-100％）。仅12项研究报道了特异性(84.0%-100%).研究设计质量是可变的，与诊断准确性相关的结果并不总是报告，很少有研究对算法进行外部验证。
结论：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老化综合征。然而,算法需要在严格设计的诊断准确性研究中进行测试，并报告适当的指标。
方法：我们根据预定义的方案进行了系统评价，并符合系统评价和荟萃分析（PRISMA）指南的首选报告项目。从每个数据库开始到2023年9月底，在PubMed中进行了搜索，Medline,Embase,CINAHL,ACM数字图书馆,IEEEXplore和Scopus。通过两位共同作者对搜索结果进行独立审查，并从每项研究中提取数据以确定计算方法，从而确定合格的研究。文本的来源，测试策略和性能指标。根据无荟萃分析指南的研究，通过衰老综合征和计算方法对数据进行叙述性合成。
结果：从1030个标题筛选，22项研究符合纳入条件。一项研究专注于识别肌肉减少症，一个脆弱，十二个瀑布，五次谵妄,五个痴呆和四个失禁。在20项研究中报告了算法与参考标准相比的敏感性（57.1％-100％）。仅12项研究报道了特异性(84.0%-100%).研究设计质量是可变的，与诊断准确性相关的结果并不总是报告，很少有研究对算法进行外部验证。
结论：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老化综合征。然而,算法需要在严格设计的诊断准确性研究中进行测试，并报告适当的指标。
