Neural Conditional Event Time Models
This addresses a critical limitation in event time modeling for applications like medical diagnoses and social media, where not all events are guaranteed to occur, though it is an incremental improvement over existing neural methods.
The paper tackled the problem of event time prediction where events may not occur, by developing a neural conditional event time model that separates the probability of occurrence from the predicted time, achieving superior predictions on synthetic data, medical events, and social media posts across 21 tasks.
Event time models predict occurrence times of an event of interest based on known features. Recent work has demonstrated that neural networks achieve state-of-the-art event time predictions in a variety of settings. However, standard event time models suppose that the event occurs, eventually, in all cases. Consequently, no distinction is made between a) the probability of event occurrence, and b) the predicted time of occurrence. This distinction is critical when predicting medical diagnoses, equipment defects, social media posts, and other events that or may not occur, and for which the features affecting a) may be different from those affecting b). In this work, we develop a conditional event time model that distinguishes between these components, implement it as a neural network with a binary stochastic layer representing finite event occurrence, and show how it may be learned from right-censored event times via maximum likelihood estimation. Results demonstrate superior event occurrence and event time predictions on synthetic data, medical events (MIMIC-III), and social media posts (Reddit), comprising 21 total prediction tasks.