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Temporal Poisson Square Root Graphical Models
Sinong Geng · Zhaobin Kuang · Peggy Peissig · University of Wisconsin David Page

Fri Jul 13 01:20 AM -- 01:30 AM (PDT) @ A4

We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data. Byestimating the temporal relationships for all possible pairs of event types, TPSQRs can offer a holistic perspective about whether the occurrences of any given event type could excite or inhibit any other type. A TPSQR is learned by estimating a collection of interrelated PSQRs that share the same template parameterization. These PSQRs are estimated jointly in a pseudo-likelihood fashion, where Poisson pseudo-likelihood is used to approximate the original more computationally intensive pseudo-likelihood problem stemming from PSQRs. Theoretically, we demonstrate that under mild assumptions, the Poisson pseudolikelihood approximation is sparsistent for recovering the underlying PSQR. Empirically, we learn TPSQRs from a real-world large-scale electronic health record (EHR) with millions of drug prescription and condition diagnosis events, for adverse drug reaction (ADR) detection. Experimental results demonstrate that the learned TPSQRs can recover ADR signals from the EHR effectively and efficiently.

Author Information

Sinong Geng (UW-Madison; Princeton University)
Charles Kuang (UW-Madison/Stanford)
Peggy Peissig (Marshfield Clinic Research Foundation)
University of Wisconsin David Page (University of Wisconsin, Madison)

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