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

ABSTRACT:

Background

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.

Methods and results

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

SUBMITTER: Hung CY 

PROVIDER: S-EPMC6415884 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature