Inferring serial correlation with dynamic backgrounds

Wei, Song; Xie, Yao; Rahnev, Dobromir

doi:10.48550/arxiv.2101.10962

Cited by 1 publication

(1 citation statement)

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“…The proof of the above lemma leverages the concentration property for martingales. Similar results could also be found in Juditsky et al [2020], Wei et al [2021]. We defer its proof to Appendix B.2.We want to remark that further improvement on this bound can be achieved by Bernstein inequality (as Juditsky et al…”

Section: Non-asymptotic Error Bound and Confidence Intervalsupporting

confidence: 69%

Causal Graph Discovery from Self and Mutually Exciting Time Series

Song¹,

Xie²,

Josef³

et al. 2021

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Continuous, automated surveillance systems that incorporate machine learning models are becoming increasingly more common in healthcare environments. These models can capture temporally dependent changes across multiple patient variables and can enhance a clinician's situational awareness by providing an early warning alarm of an impending adverse event such as sepsis. However, most commonly used methods, e.g., XGBoost, fail to provide an interpretable mechanism for understanding why a model produced a sepsis alarm at a given time. The "black box" nature of many models is a severe limitation as it prevents clinicians from independently corroborating those physiologic features that have contributed to the sepsis alarm. To overcome this limitation, we propose a generalized linear model (GLM) approach to fit a Granger causal graph based on the physiology of several major sepsis-associated derangements (SADs). We adopt a recently developed stochastic monotone variational inequality-based estimator coupled with forwarding feature selection to learn the graph structure from both continuous and discrete-valued as well as regularly and irregularly sampled time series. Most importantly, we develop a nonasymptotic upper bound on the estimation error for any monotone link function in the GLM. We conduct real-data experiments and demonstrate that our proposed method can achieve comparable performance to popular and powerful prediction methods such as XGBoost while simultaneously maintaining a high level of interpretability.

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Section: Non-asymptotic Error Bound and Confidence Intervalsupporting

confidence: 69%