Recently, healthcare has seen a sharp rise in the implementation of machine learning derived algorithms for predicting risk across a broad range of clinical scenarios. The Number Needed to Treat Thresholding Toolkit created by HIP Investigator, Dr. Brian Patterson of the BerbeeWalsh Department of Emergency Medicine, allows users to generate similar graphs, either from raw data of an algorithm’s performance in a given population, or applying an algorithm with known test characteristics at various thresholds to a theoretical population.
This toolkit is intended for clinicians, policymakers, administrators or researchers interested in projecting the effectiveness of an intervention in a given population at various risk stratification thresholds. It allows calculations of NNT vs. Patients Identified plots from ROC curves and intervention effectiveness estimates.