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A Systematic Review of Artificial Intelligence Models for Time-to-Event Outcome Applied in Cardiovascular Disease Risk Prediction.
- Source :
-
Journal of Medical Systems . 7/19/2024, Vol. 48 Issue 1, p1-18. 18p. - Publication Year :
- 2024
-
Abstract
- Artificial intelligence (AI) based predictive models for early detection of cardiovascular disease (CVD) risk are increasingly being utilised. However, AI based risk prediction models that account for right-censored data have been overlooked. This systematic review (PROSPERO protocol CRD42023492655) includes 33 studies that utilised machine learning (ML) and deep learning (DL) models for survival outcome in CVD prediction. We provided details on the employed ML and DL models, eXplainable AI (XAI) techniques, and type of included variables, with a focus on social determinants of health (SDoH) and gender-stratification. Approximately half of the studies were published in 2023 with the majority from the United States. Random Survival Forest (RSF), Survival Gradient Boosting models, and Penalised Cox models were the most frequently employed ML models. DeepSurv was the most frequently employed DL model. DL models were better at predicting CVD outcomes than ML models. Permutation-based feature importance and Shapley values were the most utilised XAI methods for explaining AI models. Moreover, only one in five studies performed gender-stratification analysis and very few incorporate the wide range of SDoH factors in their prediction model. In conclusion, the evidence indicates that RSF and DeepSurv models are currently the optimal models for predicting CVD outcomes. This study also highlights the better predictive ability of DL survival models, compared to ML models. Future research should ensure the appropriate interpretation of AI models, accounting for SDoH, and gender stratification, as gender plays a significant role in CVD occurrence. [ABSTRACT FROM AUTHOR]
- Subjects :
- *STATISTICAL models
*RISK assessment
*MEDICAL information storage & retrieval systems
*CARDIOVASCULAR diseases
*PREDICTION models
*SOCIAL determinants of health
*ARTIFICIAL intelligence
*SEX distribution
*CARDIOVASCULAR diseases risk factors
*DESCRIPTIVE statistics
*SYSTEMATIC reviews
*MEDLINE
*DEEP learning
*SURVIVAL analysis (Biometry)
*MACHINE learning
*EARLY diagnosis
*SOCIODEMOGRAPHIC factors
*DATA analysis software
*TIME
*ALGORITHMS
*BIOMARKERS
CARDIOVASCULAR disease related mortality
Subjects
Details
- Language :
- English
- ISSN :
- 01485598
- Volume :
- 48
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Medical Systems
- Publication Type :
- Academic Journal
- Accession number :
- 178623196
- Full Text :
- https://doi.org/10.1007/s10916-024-02087-7