Identifying important risk factors for survival in patient with systolic heart failure using random survival forests.
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Random ForestsPrediction of Hematopoietic Stem Cell Transplantation Related Mortality- Lessons Learned from the In-Silico Approach: A European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study.Do Non-Clinical Factors Improve Prediction of Readmission Risk?: Results From the Tele-HF StudyAn adjustable predictive score of graft survival in kidney transplant patients and the levels of risk linked to de novo donor-specific anti-HLA antibodiesAn international data set for CMML validates prognostic scoring systems and demonstrates a need for novel prognostication strategies.Using machine learning to examine medication adherence thresholds and risk of hospitalization.Risk Prediction of One-Year Mortality in Patients with Cardiac Arrhythmias Using Random Survival Forest.Early illness features associated with mortality in the juvenile idiopathic inflammatory myopathies.Right atrial area and right ventricular outflow tract akinetic length predict sustained tachyarrhythmia in repaired tetralogy of Fallot.Home monitoring of heart failure patients at risk for hospital readmission using a novel under-the-mattress piezoelectric sensor: A preliminary single centre experiencePublication of trials funded by the National Heart, Lung, and Blood Institute.Biomarkers in advanced heart failure: diagnostic and therapeutic insights.Machine Learning in Medicine.Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.Cardiovascular Event Prediction by Machine Learning: The Multi-Ethnic Study of Atherosclerosis.Severe chronic norovirus diarrheal disease in transplant recipients: Clinical features of an under-recognized syndrome.Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption.Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approachDiscordance between 'actual' and 'scheduled' check-in times at a heart failure clinic.A prediction-based alternative to P values in regression models.Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.
P2860
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P2860
Identifying important risk factors for survival in patient with systolic heart failure using random survival forests.
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2010 nî lūn-bûn
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2010年の論文
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2010年学术文章
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2010年学术文章
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Identifying important risk fac ...... using random survival forests.
@en
Identifying important risk fac ...... using random survival forests.
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type
label
Identifying important risk fac ...... using random survival forests.
@en
Identifying important risk fac ...... using random survival forests.
@nl
prefLabel
Identifying important risk fac ...... using random survival forests.
@en
Identifying important risk fac ...... using random survival forests.
@nl
P2093
P2860
P921
P1476
Identifying important risk fac ...... using random survival forests.
@en
P2093
Eileen Hsich
Eiran Z Gorodeski
Eugene H Blackstone
Michael S Lauer
P2860
P356
10.1161/CIRCOUTCOMES.110.939371
P577
2010-11-23T00:00:00Z