Background: With the increase in the world's aging population, there is a growing need to prevent and predict dementia among the general population. The availability of national time-series health examination data in South Korea provides an opportunity to use deep learning algorithm, an artificial intelligence technology, to expedite the analysis of mass and sequential data. Objective: This study aimed to compare the discriminative accuracy between a time-series deep learning algorithm and conventional statistical methods to predict all-cause dementia and Alzheimer dementia using periodic health examination data. Methods: Diagnostic codes in medical claims data from a South Korean national health examination cohort were used to identify individuals who developed dementia or Alzheimer dementia over a 10-year period. As a result, 479,845 and 465,081 individuals, who were aged 40 to 79 years and without all-cause dementia and Alzheimer dementia, respectively, were identified at baseline. The performance of the following 3 models was compared with predictions of which individuals would develop either type of dementia: Cox proportional hazards model using only baseline data (HR-B), Cox proportional hazards model using repeated measurements (HR-R), and deep learning model using repeated measurements (DL-R). Results: The discrimination indices (95% CI) for the HR-B, HR-R, and DL-R models to predict all-cause dementia were 0.84 (0.83-0.85), 0.87 (0.86-0.88), and 0.90 (0.90-0.90), respectively, and those to predict Alzheimer dementia were 0.87 (0.86-0.88), 0.90 (0.88-0.91), and 0.91 (0.91-0.91), respectively. The DL-R model showed the best performance, followed by the HR-R model, in predicting both types of dementia. The DL-R model was superior to the HR-R model in all validation groups tested. Conclusions: A deep learning algorithm using time-series data can be an accurate and cost-effective method to predict dementia. A combination of deep learning and proportional hazards models might help to enhance prevention strategies for dementia.
|Journal||JMIR Medical Informatics|
|Publication status||Published - 2019 Jul 1|
Bibliographical noteFunding Information:
This work was supported by the Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT; 2017-0-00255, autonomous digital companion framework and application). The funding source had no role in the design of the study; the collection, management, analysis, and interpretation of data; preparation and writing of the manuscript; or the decision to submit the manuscript for publication.
© 2019 Woo Jung Kim, Ji Min Sung, David Sung, Myeong-Hun Chae, Suk Kyoon An, Kee Namkoong, Eun Lee, Hyuk-Jae Chang.
All Science Journal Classification (ASJC) codes
- Health Informatics
- Health Information Management