ArticleComputational and mathematical methods in medicine2015
Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest.
Article in Computational and mathematical methods in medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- Mortality prediction and influencing factors for intensive care unit patients with acute tubular necrosis: random survival forest and cox regression analysis.Frontiers in pharmacology · 2024Article
- CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model.Kidney medicine · 2023Article
- Study on the prediction model of atherosclerotic cardiovascular disease in the rural Xinjiang population based on survival analysis.BMC public health · 2023Article
- Characteristics of peripheral blood cells are independently related to major adverse cardiovascular events after carotid endarterectomy.Atherosclerosis plus · 2023Article
- Development and visualization of a risk prediction model for metabolic syndrome: a longitudinal cohort study based on health check-up data in China.Frontiers in nutrition · 2023Article
- Evaluating Histological Subtypes Classification of Primary Lung Cancers on Unenhanced Computed Tomography Based on Random Forest Model.Journal of healthcare engineering · 2023Article
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- Building Computational Models to Predict One-Year Mortality in ICU Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
- Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility.Journal of medical Internet research · 2018Article
- Prognostic value of cancer antigen -125 for lung adenocarcinoma patients with brain metastasis: A random survival forest prognostic model.Scientific reports · 2018Article
- Can a Bayesian Belief Network Be Used to Estimate 1-year Survival in Patients With Bone Sarcomas?Clinical orthopaedics and related research · 2017Article
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Corrections and comments
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Authors and funding
5 authors.
Funding
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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.
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