risk prediction of one-year mortality in patients with cardiac arrhythmias using random survival forest
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2015
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Abstract
Existing models for predicting mortality based on traditional Cox proportional hazard approach (CPH) often have low prediction accuracy. This paper aims to develop a clinical risk model with good accuracy for predicting 1-year mortality in cardiac arrhythmias patients using random survival forest (RSF), a robust approach for survival analysis. 10,488 cardiac arrhythmias patients available in the public MIMIC II clinical database were investigated, with 3,452 deaths occurring within 1-year followups. Forty risk factors including demographics and clinical and laboratory information and antiarrhythmic agents were analyzed as potential predictors of all-cause mortality. RSF was adopted to build a comprehensive survival model and a simplified risk model composed of 14 top risk factors. The built comprehensive model achieved a prediction accuracy of 0.81 measured by c-statistic with 10-fold cross validation. The simplified risk model also achieved a good accuracy of 0.799. Both results outperformed traditional CPH (which achieved a c-statistic of 0.733 for the comprehensive model and 0.718 for the simplified model). Moreover, various factors are observed to have nonlinear impact on cardiac arrhythmias prognosis. As a result, RSF based model which took nonlinearity into account significantly outperformed traditional Cox proportional hazard model and has great potential to be a more effective approach for survival analysis.
| Reference Key |
miao2015computationalrisk
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| Authors | ;Fen Miao;Yun-Peng Cai;Yu-Xiao Zhang;Ye Li;Yuan-Ting Zhang |
| Journal | advanced functional materials |
| Year | 2015 |
| DOI |
10.1155/2015/303250
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