Dynamic Survival Analysis for Early Event Prediction
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910373846712320 |
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| author | Yèche, Hugo Burger, Manuel Veshchezerova, Dinara Rätsch, Gunnar |
| author_facet | Yèche, Hugo Burger, Manuel Veshchezerova, Dinara Rätsch, Gunnar |
| contents | This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics. By adapting and evaluating DSA models against traditional EEP benchmarks, our research demonstrates their ability to match EEP models on a time-step level and significantly improve event-level metrics through a new alarm prioritization scheme (up to 11% AuPRC difference). This approach represents a significant step forward in predictive healthcare, providing a more nuanced and actionable framework for early event prediction and management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12818 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic Survival Analysis for Early Event Prediction Yèche, Hugo Burger, Manuel Veshchezerova, Dinara Rätsch, Gunnar Machine Learning This study advances Early Event Prediction (EEP) in healthcare through Dynamic Survival Analysis (DSA), offering a novel approach by integrating risk localization into alarm policies to enhance clinical event metrics. By adapting and evaluating DSA models against traditional EEP benchmarks, our research demonstrates their ability to match EEP models on a time-step level and significantly improve event-level metrics through a new alarm prioritization scheme (up to 11% AuPRC difference). This approach represents a significant step forward in predictive healthcare, providing a more nuanced and actionable framework for early event prediction and management. |
| title | Dynamic Survival Analysis for Early Event Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.12818 |