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