Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database

Abstract

BackgroundIntelligent 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.Methods and resultsThe 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.ConclusionsUsing 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.

Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event

“…These have been used for prediction of outcomes in acute stroke (91) with better area under the curve for deep neural network model. Deep neural networks have been studied for ischemic stroke risk assessment with promising results (92). Artificial neural network have been used to differentiate stroke from stroke mimics (93) and to identify patients at high risk for TIA or minor stroke (94).…”

Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine

“…Predicting survival via ML utilizing echocardiography and CT angiogram (CTA) has also been attempted with promising results [ 16 , 17 ]. Four large-scale studies, mainly from Asian countries, have focused on estimating the risk of cerebrovascular disease ( Table 2 ) [ 18 , 19 , 20 , 21 ]. These studies have sought to estimate the risk of stroke in patients with atrial fibrillation.…”

Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction

“…The most popular ML model applied for stroke risk prediction is support vector machine (SVM). In recent years, deep neural networks (DNN) gained attention [ 101 , 102 ]. These DNN models are constituted by several layers of neural networks extending the ability of these model to study the relationship between input features and the output through the hidden layers [ 103 ].…”