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Event

SEMINAR: Scalable Joint Models for Reliable Event Prediction: Application to Monitoring Adverse Events using Electronic Health Record Data

Tuesday, February 7, 2017 15:30to16:30

Suchi Saria, PhD Assistant Professor of Computer Science, Statistics, and Health Policy, Johns Hopkins University Scalable Joint Models for Reliable Event Prediction: Application to Monitoring Adverse Events using Electronic Health Record Data Tuesday, 7 February 2017 3:30 pm – 4:30 pm - Purvis Hall, 1020 Pine Ave. West, Room 24

ALL ARE WELCOME

Abstract: Many life-threatening adverse events such as sepsis and cardiac arrest are treatable if detected early. Towards this, one can leverage the vast number of longitudinal signals---e.g., repeated heart rate, respiratory rate, blood cell counts, creatinine measurements---that are already recorded by clinicians to track an individual's health. Motivated by this problem, we propose a reliable event prediction framework comprising two key innovations. First, we extend existing state-of-the-art in joint-modeling to tackle settings with large-scale, (potentially) correlated, high-dimensional multivariate longitudinal data. For this, we propose a flexible Bayesian nonparametric joint model along with scalable stochastic variational inference techniques for estimation. Second, we use a decision-theoretic approach to derive an optimal detector that trades-off the cost of delaying correct adverse-event detections against making incorrect assessments. On a challenging clinical dataset on patients admitted to an Intensive Care Unit, we see significant gains in early event-detection performance over state-of-the-art techniques. 

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