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 prediction tasks.
Dynamic Survival Analysis for Early Event Prediction
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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.
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Towards Foundation Models for Critical Care Time Series
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 prediction tasks.