Machine learning-enabled multitrust audit of stroke comorbidities using natural language processing.
Machine learning-enabled multitrust audit of stroke comorbidities using natural language processing.
复制标题
使用自然语言处理对中风合并症进行机器学习支持的多信任审计。
DOI：
10.1111/ene.15071
复制
发表时间：
2021
影响因子：
5.1
通讯作者：
Shek A
中科院分区：
文献类型：
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作者：
Shek A
背景和目的随着电子记录在卫生系统中的越来越多的采用，机器学习技术为这些数据的更多计算机辅助管理提供了机会，用于审计和研究目的。在这个项目中，我们评估了常规临床实践中使用的传统治疗方法与新的机器学习工具MedCAT的一致性，MedCAT用于提取英国Sentinel Stroke National Audit Programme（SSNAP）倡议中记录的卒中合并症。2019年1月至2020年4月期间，被纳入本次评估。此外，将当前的临床治疗方法（SSNAP）和机器学习方法（MedCAT）与我们研究团队手动审查的200个入院事件的子样本进行了比较。性能指标的敏感性，特异性，精度，阴性预测值，和F1 scoresreport.ResultsThe报告中风合并症与目前的临床治疗方法是好的房颤，高血压，糖尿病，但充血性心力衰竭差。与当前的临床方法相比，机器学习支持的方法MedCAT在所有四种评估的合并症中取得了更好的性能，主要是由更高的灵敏度和F1 scores.ConclusionsWe驱动的，机器学习支持的数据收集可以支持现有的临床和服务计划，有可能提高现有临床数据库的数据提取质量和速度。因此，这些新机器学习工具的可扩展性和灵活性为彻底改变审计和研究方法提供了机会。
Background and purposeWith the increasing adoption of electronic records in the health system, machine learning‐enabled techniques offer the opportunity for greater computer‐assisted curation of these data for audit and research purposes. In this project, we evaluate the consistency of traditional curation methods used in routine clinical practice against a new machine learning‐enabled tool, MedCAT, for the extraction of the stroke comorbidities recorded within the UK's Sentinel Stroke National Audit Programme (SSNAP) initiative.MethodsA total of 2327 stroke admission episodes from three different National Health Service (NHS) hospitals, between January 2019 and April 2020, were included in this evaluation. In addition, current clinical curation methods (SSNAP) and the machine learning‐enabled method (MedCAT) were compared against a subsample of 200 admission episodes manually reviewed by our study team. Performance metrics of sensitivity, specificity, precision, negative predictive value, and F1 scores are reported.ResultsThe reporting of stroke comorbidities with current clinical curation methods is good for atrial fibrillation, hypertension, and diabetes mellitus, but poor for congestive cardiac failure. The machine learning‐enabled method, MedCAT, achieved better performances across all four assessed comorbidities compared with current clinical methods, predominantly driven by higher sensitivity and F1 scores.ConclusionsWe have shown machine learning‐enabled data collection can support existing clinical and service initiatives, with the potential to improve the quality and speed of data extraction from existing clinical repositories. The scalability and flexibility of these new machine‐learning tools, therefore, present an opportunity to revolutionize audit and research methods.