The Misuse of AUC: What High Impact Risk Assessment Gets Wrong

When determining which machine learning model best performs some high impact risk assessment task, practitioners commonly use the Area under the Curve (AUC) to defend and validate their model choices. In this paper, we argue that the current use and understanding of AUC as a model performance metric misunderstands the way the metric was intended to be used. To this end, we characterize the misuse of AUC and illustrate how this misuse negatively manifests in the real world across several risk assessment domains. We locate this disconnect in the way the original interpretation of AUC has shifted over time to the point where issues pertaining to decision thresholds, class balance, statistical uncertainty, and protected groups remain unaddressed by AUC-based model comparisons, and where model choices that should be the purview of policymakers are hidden behind the veil of mathematical rigor. We conclude that current model validation practices involving AUC are not robust, and often invalid

Immune Reconstitution Inflammatory Syndrome Associated with Biologic Therapy

The use of biologics in the treatment of autoimmune disease, cancer, and other immune conditions has revolutionized medical care in these areas. However, there are drawbacks to the use of these medications including increased susceptibility to opportunistic infections. One unforeseen risk once opportunistic infection has occurred with biologic use is the onset of immune reconstitution inflammatory syndrome (IRIS) upon drug withdrawal. Although originally described in human immunodeficiency virus (HIV) patients receiving highly active antiretroviral therapy, it has become clear that IRIS may occur when recovery of immune function follows opportunistic infection in the setting of previous immune compromise/suppression. In this review, we draw attention to this potential pitfall on the use of biologic drugs