Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach.

Identifying the influential variables that provide early warning of financial network instability is challenging, in part due to the complexity of the system, uncertainty of a failure, and nonlinear, time-varying relationships between network participants. In this study, we introduce a novel methodology to select variables that, from a data-driven and statistical modeling perspective, represent these relationships and may indicate that the financial network is trending toward instability. We introduce a novel variable selection methodology that leverages Shapley values and modified Borda counts, in combination with statistical and machine learning methods, to create an explainable linear model to predict relationship value weights between network participants.

A new weapon against severe sepsis related to necrotizing fasciitis.

Necrotizing fasciitis is a life-threatening infection. The purpose of this article is to review necrotizing fasciitis and nursing care as this disease may progress to sepsis.