Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction

Hossein Soleimani, James Hensman, Suchi Saria

Research output: Contribution to journalArticlepeer-review


—Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.

Original languageEnglish (US)
JournalUnknown Journal
StatePublished - Aug 15 2017


  • Joint Modeling
  • Missing Data
  • Scalable Gaussian Processes
  • Survival Analysis
  • Time Series
  • Uncertainty-Aware Prediction

ASJC Scopus subject areas

  • General

Fingerprint Dive into the research topics of 'Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction'. Together they form a unique fingerprint.

Cite this