ArticleJMIR medical informatics2019
Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination.
Article in JMIR medical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review.Journal of Alzheimer's disease : JAD · 2024Pooled it
- Multi-domain prognostic models used in middle-aged adults without known cognitive impairment for predicting subsequent dementia.The Cochrane database of systematic reviews · 2023Pooled it
- Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.Frontiers in neuroimaging · 2026Article
- Deep neural network base competing risk in predicting heart failure patient's survival.Journal of diabetes and metabolic disorders · 2025Article
- Precision fetal cardiology detects cyanotic congenital heart disease using maternal saliva metabolome and artificial intelligence.Scientific reports · 2025Article
- Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.JMIR formative research · 2024Article
- Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study.Cancer medicine · 2023Article
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- Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study.BMC medical research methodology · 2023Article
- Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry.Cardiovascular research · 2022Article
- Deep-learning model for predicting the survival of rectal adenocarcinoma patients based on a surveillance, epidemiology, and end results analysis.BMC cancer · 2022Article
- Using machine learning algorithms for predicting cognitive impairment and identifying modifiable factors among Chinese elderly people.Frontiers in aging neuroscience · 2022Article
- Deep neural network models for identifying incident dementia using claims and EHR datasets.PloS one · 2020Article
- Artificial intelligence algorithms for the prediction of cognitive decline and dementia: A theoretical-historical narrative review.Journal of Alzheimer's disease reportsReview
- What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review.Dementia and geriatric cognitive disorders extraReview
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith 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.
objectiveThis 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.
methodsDiagnostic 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).
resultsThe 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.
conclusionsA 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.
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