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Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination

Authors
 Woo Jung Kim  ;  Ji Min Sung  ;  David Sung  ;  Myeong-Hun Chae  ;  Suk Kyoon An  ;  Kee Namkoong  ;  Eun Lee  ;  Hyuk-Jae Chang 
Citation
 JMIR Medical Informatics, Vol.7(3) : e13139, 2019 
Journal Title
 JMIR Medical Informatics 
Issue Date
2019
Keywords
deep learning ; dementia ; proportional hazards models
Abstract
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.
Files in This Item:
T201903124.pdf Download
DOI
10.2196/13139
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Woo Jung(김우정)
Namkoong, Kee(남궁기) ORCID logo https://orcid.org/0000-0003-1400-8057
Sung, Ji Min(성지민)
An, Suk Kyoon(안석균) ORCID logo https://orcid.org/0000-0003-4576-6184
Lee, Eun(이은) ORCID logo https://orcid.org/0000-0002-7462-0144
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/171235
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