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Dynamic Survival Analysis for Early Event Prediction

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arxiv 2403.12818 v1 pith:4ULY54DR submitted 2024-03-19 cs.LG

Hugo Yèche , Manuel Burger , Dinara Veshchezerova , Gunnar Rätsch This is my paper · ORCID
classification cs.LG
keywords eventearlypredictionalarmanalysisapproachdynamichealthcare
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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.

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Cited by 1 Pith paper

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  1. Towards Foundation Models for Critical Care Time Series

    cs.LG 2024-11 conditional novelty 5.0 of 10

    The paper introduces a harmonized multi-center critical care time series dataset and transfer benchmark covering nine datasets from three continents, with treatment variables, and compares seven models on early event ...

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