
Contents
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Introduction Introduction
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Machine Learning and Survival Models Machine Learning and Survival Models
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Random Survival Forests: Random Survival Forests:
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Gradient Boosting Machines: Gradient Boosting Machines:
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Measures of Performance in Survival Analysis Measures of Performance in Survival Analysis
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ROC Curve, AUROC, Brier, and the Problem with Time: ROC Curve, AUROC, Brier, and the Problem with Time:
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Concordance Index: Concordance Index:
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Application: Prediction of Sex Crime Recidivism Application: Prediction of Sex Crime Recidivism
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Conclusion Conclusion
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References References
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Machine Learning for Survival Analysis: Comments and a Review of Methods
Get accessAlejandro Quiroz Flores is Senior Lecturer in the Department of Government at the University of Essex.
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Published:22 May 2024
Cite
Abstract
In the social sciences, survival analysis has focused mostly on inference. However, the prediction of time to an event is of equal importance, particularly when predictions inform interventions. Producing “good” predictions, however, is challenging because even in the simplest hazard model, the survival process consists of two variables: the occurrence (or lack thereof) of a transition to a state, and the elapsed time to the occurrence of that transition or to the end of follow-up time. However, advances in Machine Learning and Biostatistics offer powerful tools for prediction and the analysis of model performance in these complex settings, and event history modeling can benefit from advances in these fields. This chapter focuses on machine learning models specifically designed for survival data. It also discusses measures of performance appropriate for these models. To demonstrate these techniques, the chapter presents an application to the prediction of sex crime recidivism in South Carolina.
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