Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach

Abstract Background Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis mod...

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Bibliographic Details
Published in:International journal of epidemiology 2022-06, Vol.51 (3), p.931-944
Main Authors: Barbieri, Sebastiano, Mehta, Suneela, Wu, Billy, Bharat, Chrianna, Poppe, Katrina, Jorm, Louisa, Jackson, Rod
Format: Article
Language:English
Subjects:
Cardiovascular Diseases - epidemiology
Deep Learning
Female
Heart Disease Risk Factors
Humans
Male
Methods
Proportional Hazards Models
Risk Assessment
Risk Factors
Survival Analysis
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Summary:Abstract Background Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets. Methods Using individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30–74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were compared based on the proportion of explained variance, model calibration and discrimination, and hazard ratios for predictor variables. Results First CVD events occurred in 61 927 of 2 164 872 people. Within the reference group, the largest hazard ratios estimated by the deep learning models were for tobacco use in women (2.04, 95% CI: 1.99, 2.10) and chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95% CI: 1.50, 1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk factors. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance (R2: 0.468 vs 0.425 in women and 0.383 vs 0.348 in men), calibration and discrimination (all P 
ISSN:0300-5771
1464-3685
DOI:10.1093/ije/dyab258