Department of Electrical Engineering, National Tsing Hua University, Hsinchu, Taiwan.
Department of Internal Medicine, Taipei Veterans General Hospital, Hsinchu Branch, Hsinchu, Taiwan.
PLoS One. 2019 Mar 13;14(3):e0213007. doi: 10.1371/journal.pone.0213007. eCollection 2019.
Intelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data for estimating stroke risk needs to be assessed and validated. This study aims to apply a DNN for deriving a stroke predictive model using a big electronic health record database.
The Taiwan National Health Insurance Research Database was used to conduct a retrospective population-based study. The database was divided into one development dataset for model training (~70% of total patients for training and ~10% for parameter tuning) and two testing datasets (each ~10%). A total of 11,192,916 claim records from 840,487 patients were used. The primary outcome was defined as any ischemic stroke in inpatient records within 3 years after study enrollment. The DNN was evaluated using the area under the receiver operating characteristic curve (AUC or c-statistic). The development dataset included 672,214 patients (a total of 8,952,000 records) of whom 2,060 patients had stroke events. The mean age of the population was 35.5±20.2 years, with 48.5% men. The model achieved AUC values of 0.920 (95% confidence interval [CI], 0.908-0.932) in testing dataset 1 and 0.925 (95% CI, 0.914-0.937) in testing dataset 2. Under a high sensitivity operating point, the sensitivity and specificity were 92.5% and 79.8% for testing dataset 1; 91.8% and 79.9% for testing dataset 2. Under a high specificity operating point, the sensitivity and specificity were 80.3% and 87.5% for testing dataset 1; 83.7% and 87.5% for testing dataset 2. The DNN model maintained high predictability 5 years after being developed. The model achieved similar performance to other clinical risk assessment scores.
Using a DNN algorithm on this large electronic health record database is capable of obtaining a high performing model for assessment of ischemic stroke risk. Further research is needed to determine whether such a DNN-based IDSS could lead to an improvement in clinical practice.
智能决策支持系统（IDSS）已应用于疾病管理任务。深度神经网络（DNN）是一种实现高建模能力的人工智能技术。需要评估和验证 DNN 对大型数据进行估计中风风险的应用。本研究旨在应用 DNN 从大型电子健康记录数据库中得出中风预测模型。
使用台湾全民健康保险研究数据库进行回顾性基于人群的研究。该数据库分为一个开发数据集（用于模型训练，约占总患者的 70%和 10%用于参数调整）和两个测试数据集（各占 10%）。共使用了 840,487 名患者的 11,192,916 份理赔记录。主要结果定义为研究入组后 3 年内住院记录中的任何缺血性中风。使用接收者操作特征曲线下的面积（AUC 或 c 统计量）评估 DNN。开发数据集包括 672,214 名患者（共 8,952,000 份记录），其中 2,060 名患者有中风事件。人群的平均年龄为 35.5±20.2 岁，男性占 48.5%。该模型在测试数据集 1 中的 AUC 值为 0.920（95%置信区间[CI]，0.908-0.932），在测试数据集 2 中的 AUC 值为 0.925（95%CI，0.914-0.937）。在高灵敏度工作点下，测试数据集 1 的敏感性和特异性分别为 92.5%和 79.8%；测试数据集 2 的敏感性和特异性分别为 91.8%和 79.9%。在高特异性工作点下，测试数据集 1 的敏感性和特异性分别为 80.3%和 87.5%；测试数据集 2 的敏感性和特异性分别为 83.7%和 87.5%。DNN 模型在开发后 5 年内仍保持较高的预测能力。该模型的性能与其他临床风险评估评分相当。
在这个大型电子健康记录数据库上使用 DNN 算法能够获得用于评估缺血性中风风险的高性能模型。需要进一步研究确定基于 DNN 的 IDSS 是否可以改善临床实践。
