Work

Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processes

Year: 2023

Type: conference-paper

Abstract: Modern health care systems are conducting continuous, automated surveillance of the electronic medical record (EMR) to identify adverse events with increasing frequency; however, many events such as sepsis do not have elucidated prodromes (i.e., event chains) that can be used to identify and intercept the adverse event early in its course. Clinically relevant and interpretable results require a framework that can (i) infer temporal interactions across multiple patient features found in EMR data (e.g., Labs, vital signs, etc.) and (ii) identify patterns that precede and are specific to an impending adverse event (e.g., sepsis). In this work, we propose a linear multivariate Hawkes process model, coupled with ReLU link function, to recover a Granger Causal (GC) graph with both exciting and inhibiting effects. We develop a scalable two-phase gradient-based method to obtain a maximum surrogate-likelihood estimator, which is shown to be effective via extensive numerical simulation. Our method is subsequently extended to a data set of patients admitted to Grady hospital system in Atlanta, GA, USA, where the estimated GC graph identifies several highly interpretable GC chains that precede sepsis. The code is available at https://github.com/SongWei-GT/two-phase-MHP.

Source: ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)

DOI: 10.1145/3580305.3599369

Authors: Song Wei, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran

Institutions: Georgia Institute of Technology, Emory University

Language: English

Topic: Sepsis Diagnosis and Treatment

Subfield: Epidemiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Science Foundation, Emory University, Division of Civil, Mechanical and Manufacturing Innovation