ArticlePloS one2019
Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Prediction models for incident stroke in the community: a systematic review and meta-analysis of predictive performance.European heart journal. Digital health · 2026Article
- Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records.BMC medical informatics and decision making · 2024Article
- An intelligent learning system based on electronic health records for unbiased stroke prediction.Scientific reports · 2024Article
- Multi-objective learning and explanation for stroke risk assessment in Shanxi province.Scientific reports · 2022Article
- Simulation of a machine learning enabled learning health system for risk prediction using synthetic patient data.Scientific reports · 2022Article
- Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.Journal of clinical medicine · 2021Review
- Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction.Sensors (Basel, Switzerland) · 2021Review
- Clinical Risk Score for Predicting Recurrence Following a Cerebral Ischemic Event.Frontiers in neurology · 2019Review
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
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.
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