Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction

By Hossein Soleimani, James Hensman, Suchi Saria. 2017

60 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research, Probabilistic & Bayesian Methods

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Citations in the corpus

Neural Ordinary Differential Equations (2018, neutral): “Missing data can be addressed using generative time-series models (Álvarez and Lawrence, 2011; Futoma et al., 2017; Mei and Eisner, 2017; Soleimani et al., 2017a) or data imputation (Che et al., 2018).”
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series (2019, methodology): “They have been used for smart imputation before a RNN or CNN architecture (Futoma et al., 2017; Moor et al., 2019), for modelling a hidden process in joint models (Soleimani et al., 2018), or to derive informative representations of time series (Ghassemi et al., 2015).”
Future Event Prediction: If and When (2019, methodology): “[20] use Gaussian processes to predict time to event in medical applications.”
Disease-Atlas: Navigating Disease Trajectories using Deep Learning (2018, background): “…patient covariates, such as genetic or demographic information which can have signiﬁcant impact on a patient’s clinical trajectory, are either omitted from the model (Schulam and Saria, 2017), or incorporated as a linear additive component to the time-to-event submodel (Soleimani et al., 2018).”
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL (2018, background): “Real-time prediction Several models have been proposed for real-time prediction on irregularly sampled EHR data (Soleimani et al., 2017; Futoma et al., 2017; Hwang et al., 2017; Zhang et al., 2017).”