Temporal Poisson Square Root Graphical Models

TL;DR

This paper introduces TPSQRs, a novel model for analyzing longitudinal event data, effectively capturing interactions between event types, and demonstrates its utility in detecting adverse drug reactions from large-scale health records.

Contribution

The paper develops TPSQRs, extending PSQRs for temporal data, and proposes a computationally efficient pseudo-likelihood approach with theoretical guarantees.

Findings

Successfully applied to EHR data for ADR detection

Recovers meaningful temporal relationships between events

Efficiently handles large-scale longitudinal data

Abstract

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. By estimating 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 pseudo-likelihood approximation is sparsistent for recovering the underlying PSQR. Empirically, we learn TPSQRs from Marshfield Clinic electronic health records (EHRs) 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.