Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916295762509824 |
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| author | Alberge, Julie Maladière, Vincent Grisel, Olivier Abécassis, Judith Varoquaux, Gaël |
| author_facet | Alberge, Julie Maladière, Vincent Grisel, Olivier Abécassis, Judith Varoquaux, Gaël |
| contents | When data are right-censored, i.e. some outcomes are missing due to a limited period of observation, survival analysis can compute the "time to event". Multiple classes of outcomes lead to a classification variant: predicting the most likely event, known as competing risks, which has been less studied. To build a loss that estimates outcome probabilities for such settings, we introduce a strictly proper censoring-adjusted separable scoring rule that can be optimized on a subpart of the data because the evaluation is made independently of observations. It enables stochastic optimization for competing risks which we use to train gradient boosting trees. Compared to 11 state-of-the-art models, this model, MultiIncidence, performs best in estimating the probability of outcomes in survival and competing risks. It can predict at any time horizon and is much faster than existing alternatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_14085 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks Alberge, Julie Maladière, Vincent Grisel, Olivier Abécassis, Judith Varoquaux, Gaël Artificial Intelligence When data are right-censored, i.e. some outcomes are missing due to a limited period of observation, survival analysis can compute the "time to event". Multiple classes of outcomes lead to a classification variant: predicting the most likely event, known as competing risks, which has been less studied. To build a loss that estimates outcome probabilities for such settings, we introduce a strictly proper censoring-adjusted separable scoring rule that can be optimized on a subpart of the data because the evaluation is made independently of observations. It enables stochastic optimization for competing risks which we use to train gradient boosting trees. Compared to 11 state-of-the-art models, this model, MultiIncidence, performs best in estimating the probability of outcomes in survival and competing risks. It can predict at any time horizon and is much faster than existing alternatives. |
| title | Teaching Models To Survive: Proper Scoring Rule and Stochastic Optimization with Competing Risks |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2406.14085 |