A0947
Title: Global and episode-specific prediction of recurrent events using longitudinal health informatics data
Authors: Yifei Sun - Columbia University (United States) [presenting]
Sy Han Chiou - Southern Methodist University (United States)
ChiungYu Huang - University of California, San Francisco (United States)
Abstract: Accurate prediction of recurrent clinical events is crucial for effective management of chronic conditions such as cancer and cardiovascular disease. In recent years, longitudinal health informatics databases, which routinely collect data on repeated clinical events, have been increasingly utilized to construct risk prediction models. A novel nonparametric framework is introduced to predict recurrent events on a gap time scale using survival tree ensembles. The framework incorporates two predictive modeling strategies: an episode-specific model and a global model. These models avoid strong assumptions on how future event risk depends on previous event history and other predictors, making them a promising alternative to Cox-type models. Additional complexities in tree-based prediction for recurrent events include induced informative censoring of gap times and inter-event correlations. Algorithms are developed to address these issues through the use of inverse probability of censoring weighting and modified resampling procedures. Applied to SEER-Medicare data to predict repeated hospitalizations for breast cancer patients, our models showed superior performance. In particular, borrowing information across events via global models substantially improved prediction accuracy for later hospitalizations.
