Comparison of State-of-the-Art Neural Network Survival Models with the Pooled Cohort Equations for Cardiovascular Disease Risk Prediction

Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association

(2022) Connie W. Tsao et al.   CIRCULATION

Use of Machine Learning Models to Predict Death After Acute Myocardial Infarction

(2021) Rohan Khera et al.   JAMA Cardiology

Incremental Benefits of Machine Learning—When Do We Need a Better Mousetrap?

(2021) Matthew M. Engelhard et al.   JAMA Cardiology

A scalable discrete-time survival model for neural networks

(2019) Michael F. Gensheimer et al.   PeerJ

Learning from Longitudinal Data in Electronic Health Record and Genetic Data to Improve Cardiovascular Event Prediction

(2019) Juan Zhao et al.   Scientific Reports

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

(2018) Jared L. Katzman et al.   BMC Medical Research Methodology

Natural Language Processing for EHR-Based Computational Phenotyping

(2018) Zexian Zeng et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

Cox-nnet: An artificial neural network method for prognosis prediction of high-throughput omics data

(2018) Travers Ching et al.   PLoS Computational Biology

Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk

(2018) Alexandros C. Dimopoulos et al.   BMC Medical Research Methodology

Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies

(2018) Lisa Pennells et al.   EUROPEAN HEART JOURNAL

2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk

(2013) David C. Goff et al.   CIRCULATION

On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data

(2011) Hajime Uno et al.   STATISTICS IN MEDICINE