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info.json
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/
info.json
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{
"abstract": "New methods for time-to-event prediction are proposed by extending the Cox proportional hazards model with neural networks. Building on methodology from nested case-control studies, we propose a loss function that scales well to large data sets and enables fitting of both proportional and non-proportional extensions of the Cox model. Through simulation studies, the proposed loss function is verified to be a good approximation for the Cox partial log-likelihood. The proposed methodology is compared to existing methodologies on real-world data sets and is found to be highly competitive, typically yielding the best performance in terms of Brier score and binomial log-likelihood. A python package for the proposed methods is available at <a href='https://github.com/havakv/pycox'>https://github.com/havakv/pycox</a>.",
"authors": [
"H{{\\aa}}vard Kvamme",
"{{\\O}}rnulf Borgan",
"Ida Scheel"
],
"emails": [
"haavakva@math.uio.no",
"borgan@math.uio.no",
"idasch@math.uio.no"
],
"id": "18-424",
"issue": 129,
"pages": [
1,
30
],
"title": "Time-to-Event Prediction with Neural Networks and Cox Regression",
"title_bibtex": "Time-to-Event Prediction with Neural Networks and {C}ox Regression",
"volume": 20,
"year": 2019
}